Baptiste Caramiaux

AI with Museums and Cultural Heritage

published or not. The documents may come

INTRODUCTION

Where there is data, there must be artificial intelligence (AI). This is an assertion that tends to hold true in many fields, particularly those at the heart of this paper: museums and the cultural heritage sector. AI undoubtedly offers a number of opportunities to sort through and use data. But it also creates tensions, especially in the current context, where the regulation of AI is lagging behind the speed of innovation. In this short paper, I would like to discuss the following questions: What is the use of AI in these sectors? Are there problems that AI can solve for these industries that more traditional forms of computation and human labour could not? What do these sectors have to gain? And what can museums and the cultural heritage sector in turn dictate with respect to the development of this technology?

In our contemporary societies, museums and the cultural heritage sector play a role in the conservation, exhibition, and study of the cultural heritage of the particular society in which they are situated. These institutions can be seen as means of preserving and exhibiting past and present traces of human societies. Museums and cultural heritage therefore have a tangible and material identity. But recently, museums and the cultural heritage sector, like many other creative and cultural sectors, have been experiencing an increasing digitization of their collections. Along with collections of physical artefacts, some museums are building collections of their dig-ital counterparts, thus expanding their original mission into the digital world. With this context of increasing digitization in mind along with the potential opportunities for museums and the public that it brings, some institutions have begun to take an interest in the use of AI techniques applied in connection with their collections, needs, and audiences.

In this context, I have recently participated in writing reports about both the opportunities and challenges connected with the use of these technologies in the cultural and creative sectors (see for instance [START_REF] Caramiaux | AI in the media and creative industries[END_REF]). In most cases, AI was seen as a tool that opens up a new space of possibilities and opportunities for cultural actors. The first objective of this paper is hence to present these opportunities made possible by AI in the context of museums and the cultural heritage sector. Seeing this technology as a tool, however, also generates a blind spot with respect to the underlying sociocultural and sociotechnical characteristics of this technology. In these reports, we have barely grasped the need for stakeholders in these sectors to express their views on how they regard this technology, how it should be integrated into their practice and organizations, and whether sometimes it should be avoided altogether.

At meetings and conferences parallel to the creation of these reports, and notably at the Cultures of AI conference held in Karlsruhe in December 2022, we had several discussions about machine learning and artificial intelligence from a sociocultural perspective. During these exchanges, I could hear a generally critical voice, not necessarily about the technology itself, but rather about its current hegemonic cultural representation, in which artificial intelligence has been presented (and is still presented) in a normative way. During these meetings, we pragmatically addressed the demystification of artificial intelligence, which involves diversifying representations of technology through alternative cultural references and integrating them into the practices of archivists, librarians, or curators of museums. The second objective of this paper is thus to present elements of these discussions, particularly on how critical and cultural studies in the context of museums and the cultural heritage sector provide a complementary and important standpoint on this technology.

I have structured this paper as follows. In the first section, I will discuss AI and its terminology as well as its materiality with respect to infrastructure. With these elements in mind, I will then present its usefulness for museums and cultural heritage, including the set of opportunities as listed in the recent institutional reports mentioned above. These opportunities will show a utilitarian view of AI for these sec-tors. I will then outline how these sectors could also be beneficial and critical actors in the development and practice of AI.

FRAMING 'AI'

AI is a term widely used to refer to a broad set of technologies. Before describing the interactions between AI and the cultural heritage sector, I will thus first present a working definition and discuss the terminology used and what it represents in terms of sociotechnical infrastructures.

TECHNIQUE

Artificial intelligence is a digital technology that facilitates the automation of existing processes hitherto carried out by other means, such as human labour or other forms of calculations. In the context that interests us in this paper, automated processes include the generation of visual or textual content, audience forecasts in the case of public receptions, the identification of artists and market trends, support for decision-makers in the cultural sectors, or personalization and visitor services.

Automation is made possible by the ability of AI algorithms to exploit aggregated datasets by being trained on them in order to make predictions about future data close enough to that used to train the algorithms. In addition, AI has shown a certain level of openness regarding types of data: it can be applied to sound, images, physiological data, or texts, to cite just a few examples. Advances in these areas, AI's openness with regard to the types of input data, and the development of effective accompanying tools and services have led cultural institutions to question the extent to which they can make use of AI technologies.

TERMINOLOGY

Over the past decade, there has been a shift in terminology from machine learning to artificial intelligence. While the first term is usually associated with techniques capable of identifying structures in a complex dataset and making predictions based on them, the second term is multifaceted and does not refer solely to the under-lying technologies. Machine learning continues to be used in academic disciplines interested in creating ever more efficient learning techniques for complex cognitive and sensorimotor tasks, in finding ways to interact effectively with these techniques, or in exploiting their analytical and discursive potential. The term AI is meanwhile spreading across academia, industry, and culture through media, speeches, papers, films, and practices. In a recent paper, Meredith Whittaker recalls this change in terminology at the time of the breakthrough of deep learning in machine learning, which occurred in 2012. This breakthrough showed research and the economics of technology the possibility of using this technology to address real-world problems with less structured and complex data (for instance, high-dimensional images or long text sequences). She wrote: "The year 2012 showed the commercial potential of supervised machine learning and the power of the term AI as a marketing hook. Tech companies quickly (re-)branded machine learning and other data-dependent approaches as AI, framing them as the product of breakthrough scientific innovation. Companies acquired labs and start-ups, and worked to pitch AI as an efficient and precise multitool, suitable for nearly any purpose across countless domains" [Whittaker, 2021]. This is a warning to keep in mind: in many cases, this term has become a communication and marketing tool, which is important to unpack in order to be fully aware of the methods implemented.

While machine learning, though technical, seemed more clearly defined, artificial intelligence remains deliberately vague, which makes it generic enough to be incorporated into every sector. In 'Algorithms as Culture', Nick Seaver writes that algorithms are 'something of a modern myth' [START_REF] Barocas | Provocation piece | Governing Algorithms[END_REF], which means, 'to which great importance and power are ascribed, but whose properties are ill-defined' [Seaver, 2017]. The algorithms in question refer to technological solutions deployed by large companies capable of processing large amounts of data and producing a service in return, which includes AI algorithms such as those implemented on the Facebook platform, Netflix, and now OpenAI, to name just a few examples. These AI algorithms seem less and less bounded, and less and less possible to isolate and dissect. Rather, they describe an infrastructure that combines different types of technologies and actors, both private and public. So, the second warning is this: the term AI does not designate a technical object, but instead an infrastructure integrated into the socioeconomic fabric of modern digitized societies, which makes it less easy to define and gives it greater power.

INFRASTRUCTURE

AI refers to an infrastructure consisting of a large number of heterogeneous elements: data collection, annotation, processing, and storage, but also the training and development of learning models on significant distributed computing resources, terminals such as smartphones as interfaces with the social fabric of our daily lives, and a huge amount of energy to power these different parts, as well as the exploitation of resources essential to constructing the elements of each computing device. Such an infrastructure involves a tremendous amount of human labour, from data annotation to maintaining the computation infrastructure, which includes the computer servers on which the algorithms run each time a user makes a query of an AI-based service hosted on this server. Kate Crawford gives a detailed description of this infrastructure in her work with Vladan Joler titled ' Anatomy of an AI'. 1 The authors take the case of Amazon Echo, a consumer device that users interact with using their voice. Based on this example and a simple voice interaction, the authors dissect all the elements that make it work and highlight the infrastructure necessary to enable people to have this device in their home environment. The authors discuss the different elements at play in order for AI to exist, such as the rare minerals central to the batteries and screens of computing systems that structure the infrastructural grid, or the electricity consumed to power these computing resources. The authors describe how such an infrastructure evinces a centralization of power and an exploitation of human labour and environmental resources (see details in 'Atlas of AI' [Crawford, 2021]).

The ramifications of this infrastructure are too often forgotten or ignored by focusing on an ill-defined notion of 'artificial intelligence' too often represented instead as humanoid robots. 2 The description of AI as the infrastructure that creates and supports this technology has the advantage of situating it and updating its materiality so as to go beyond a representation of technology that would remain ambiguous and abstract. Digital materiality consists of pipes, cables, standards, protocols, machines, and labour [Star, 1999, Denis and[START_REF] Denis | [END_REF]. The interaction between operators and subjects shapes digital information on nested operating chains. This view of digital materiality as components of underlying infrastructures helps ground digital information in reality, as opposed to an idealistic view, which promotes the digital as an abstract entity devoid of materiality. And this process also makes it possible to highlight the different relationships between socioeconomic groups, technical substrates, and services.

In summary, in this section, my aim was to warn against the ambiguity of the term AI, which will nonetheless be used extensively in what follows. This term does not only describe a set of technical objects. Materialistically, it underlies an infrastructure involving heterogeneous interactions between various actors and digital technology. In the next section, I will focus on the usefulness of AI for museums and cultural heritage. We will see that some of the elements of the infrastructure will disappear in order to focus on techniques and, in particular, the tasks that these techniques are capable of performing. In the subsequent section, I try to bring back in some elements of the infrastructure previously left hanging in order to discuss them in the context that animates us: museums and cultural heritage.

A TOOL FOR MUSEUMS AND CULTURAL HERITAGE

In recent years, international institutions have been examining the (current and future) impact of AI in the creative and cultural sectors and generated reports and white papers on the topic. Such reports have been commissioned by the European Commission,3 the European Parliament,4 and a European association [START_REF] Caramiaux | AI in the media and creative industries[END_REF]. Across the various creative and cultural sectors, these reports have studied how AI is currently being used in museums and cultural heritage based on interviews and documentary research. The aim was to deduce the potential opportunities that this technology can bring to these cultural actors and the challenges associated with enacting these opportunities within these sectors. AI was thus primarily presented as an algorithmic tool that facilitates opportunities. In this section, I present the four classes of opportunities that are commonly presented in the reports mentioned above, in which AI is a technological tool at the disposal of cultural institutions.

ARCHIVING, CATALOGUING, MANAGING INFORMATION

A first opportunity identified is the use of AI as a tool for information management and cataloguing digitized cultural artefacts by means of automated processes of labelling, classifying, or organizing based on similarities (also called clustering in machine learning). AI-powered classification and tagging is able to automatically associate tags with a document on the basis of a set of associations that the algorithm has been trained on beforehand. This process can help sort large collections of artefacts or include new artefacts in an existing catalogue by means of automatic tagging. Clustering is able to automatically sort a set of data elements by similarity with no need for their content to be described (in other words, with annotations or meta-data).

One concrete example faced by librarians and cultural heritage practitioners is the digitization and management of large numbers of printed and manuscript materials. One objective is to organize these documents according to their content, but due to their large number and the need to extract their content manually, this is tedious and unrealistic. AI (or machine learning) can be useful in this context since there are many tools available to perform the tasks necessary to achieve this document management goal. Optical character recognition (OCR) is used to extract characters from a scanned version of a document and help to create digital text as output. Automatic computer vision methods based on modern machine learning algorithms are able to analyse document layouts, which will eventually assist in organizing archives. Finally, AI can be used to perform text or image similarity analysis to provide a better understanding of document content and organization based on similarity (newspapers, administrative documents, etcetera). In this case study, 'AI' is a toolbox, where each tool performs a task in the information management pipeline. A concrete example is the ongoing project at the Staatsbibliothek zu Berlin (Berlin State Library, SBB) called Qurator. 5A second example concerns the automatic markup of digitized works. In the context of museums and the cultural heritage sector, such a task can help add meta-data to these works. A recent experiment at The Metropolitan Museum of Art in New York brought together hundreds of people to interact with an automated tagging system applied to the museum's art collection. The system used a game interface to engage visitors in the experience so that it would not be too boring. When an artwork was selected, the AI-based algorithm guessed what the particular artwork contained (a house, flower, tree, person ...), and visitors were able to confirm the suggestion made by the system or not. Visitors could therefore interact with images in The Met's collection to determine if the suggestion provided by the system was correct, thus helping the AI-based system add metadata to the image.6

PROPOSING MORE ENGAGING SERVICES FOR THE AUDIENCE

A second opportunity identified is using AI as a tool to engage the public in interactions with artefacts in museum or cultural heritage archives. In this case, the objective is to offer visitors a personalized experience and interactive scenarios that make the exhibitions and archives more accessible to a broad audience. AI algorithms are integrated into an interactive application for visitors, which thus not only ensures the robustness of the algorithms, but also that the interactions are designed in a way that promotes both experience and usability.

Chatbots are an example of the type of AI-related methods used for audience engagement with collections. They are interactive systems capable of responding to text input by human users by providing text output in return. Users can typically request information about a museum (its history, opening times, prices), an artwork (its date of creation and techniques), or an artist (biography and other works). Chatbots can be embedded in websites or used through dedicated apps running on mobile devices. They are one of the most widespread uses of AI algorithms in the museum context [START_REF] French | AI, Visitor Experience, and Museum Operations: A Closer Look at the Possible[END_REF]. According to the website of the French company Ask Mona, which deploys chatbots in museums, statistics show that 93 per cent of visitors usually expect an answer to their questions within 24 hours and 86 per cent expect a personalized answer and experience when requesting information from a museum.7 One of the main motivations for using chatbots is thus to provide a better experience to visitors by reducing the time required for their questions to be answered, offering a service that is always available, and engaging them in a personalized conversation with the specific museum.

Several museums have implemented the use of chatbots to interact with their public. One example is an app deployed by the Field Museum that allows visitors to engage directly with items in the museum's collection, including chatting with the dinosaur Maximo, which is one of the centrepieces of the museum. Visitors can ask Maximo the dinosaur questions directly related to its history, habitat, and diet. The chatbot simulates the imaginary responses of the dinosaur to questions asked by the public, thus creating a personal and special relationship between the visitor and the museum piece.8

MANAGING VISITOR EXPERIENCE

A third class of opportunity, as mentioned in the reports cited above, concerns the ability of AI-based systems to manage, and potentially improve, the museum visitor experience. Museum visitors are thus encouraged to use mobile applications developed by the museums themselves, which make it possible to collect various types of data on visitors during their visit and hence evaluate the 'success' of an exhibition according to the criteria chosen. The use of AI methods is motivated by the possibility, in the long term, of being able to predict the popularity of an exhibition based on the number of visitors and their experience on the basis of this data, in addition to contextual data provided by GPS and Wi-Fi. Using predictive methods to assess an upcoming exhibition would save museums money by enabling them to properly allocate resources to the upcoming exhi-bition, especially with respect to the choice of works and their location in the museum.

One example of this, which is now quite a few years in the past, is the UK's National Gallery's creation of a project to predict the future number of visitors to a given exhibition based on the characteristics of that exhibition.9 Even though this project has been communicated extensively, there are not many details available to understand how this prediction is concretely made, or based on what features. There have been, to my knowledge, few other attempts in this direction, which is not necessarily surprising in view of the task, which relies on various criteria beyond what can be seen in the data, and on the other hand, gives rise to ethical issues related to the capturing of visitors' personal data.

Another source of data used by museums to identify potential visitors or analyse the quality of an exhibit are assessments of social media posts or tourist website ratings. These analyses can inform strategic decisions within organizations, particularly around communication and operational activities. As reported by [START_REF] French | AI, Visitor Experience, and Museum Operations: A Closer Look at the Possible[END_REF], some museums are already assessing comments posted on platforms such as TripAdvisor using sentiment analysis techniques and topic modelling. These techniques, which can be grouped under the category of AI techniques, enable museums to analyse feedback from thousands of visitors and provide insights on how to improve exhibits, visitor experience, orientation in the museum, and their communication of the events organized.

CREATING

The final opportunity identified is using AI as a tool to generate content and reflect on existing collections of data. So far, I have mainly talked about data analysis and classification, but some AI techniques are also designed to generate multimedia content such as images, text (used in chatbots), or sounds. Recent advances in AI have shown the ability of these systems to, for example, generate images from a textual description. This family of techniques has been referred to as generative AI, although generative methods based on machine learning have always existed along-side the other types of tasks mentioned above, such as classification techniques or clustering.

One example illustrating this possible use of AI is a recent work commissioned by the Museum of Modern Art in New York, which involved training a generative AI model on a collection of 180,000 works of art from the museum's collection. The resulting work titled Unsupervised by the artist Refik Anadol and his studio shows an abstract and moving visual representation of artworks in the collection. 10Another example of content generation can be found in the restoration of works of art. The Rijksmuseum in Amsterdam has collaborated with companies to use a generative AI technique to restore missing edges to Rembrandt's painting The Night Watch. He originally produced a painting slightly larger than the existing one. But the edges of the painting had to be cut off at some point in its history in order to fit it into a frame smaller than the original one. To restore the missing edges, the museum trained the AI-based technique to colour in the style that Rembrandt would have used in his painting. Thanks to this training dataset based on the artist's paintings, the technique was able to learn both the colours and the brushstrokes and used and thus recreate the missing edges.

CULTURAL HERITAGE PRACTICE AND AI

In the previous section, I presented how AI can be regarded as a tool for museums and cultural heritage and, as such, the opportunities this technology can bring to these sectors. In this section, I will discuss the way institutions and associations involved in collecting and conserving cultural heritage can have a voice in AI literacy. Firstly, I will discuss data curation, a documented practice in the field of cultural heritage and a necessary step in AI. Secondly, I will discuss the narrative of AI as a decontextualized and universal form of knowledge compression and how museums and the cultural heritage sector can help build alternative narratives.

DATA CURATION

Most artificial intelligence algorithms are based on datasets that largely dictate the behaviour of the algorithms through the use of the particular model trained on these datasets. If a model is trained to provide a translation from English into French, it will do so by learning from a large number of text fragments translated from English into French. The questions that arise are: Where do these fragments of text come from? How were they chosen and by whom? What do they contain and who translated them?

Datasets play a fundamental role in the design of artificial intelligence, and data curation has a critical impact on the behaviour of AI's algorithms. Indeed, since the rise of machine learning and AI ten years ago, much work has shown that the datasets used in many machine learning and AI tasks are fundamentally biased in the sense that certain tasks, such as face classification, do not work in the same way for all people, particularly when skin colour (darker skin will have poorer recognition performance) and gender (algorithms will recognize women less well than men) are concerned [START_REF] Buolamwini | Gender shades: Intersectional accuracy disparities in commercial gender classification[END_REF]. These algorithms are biased because their performance depends on the population considered in the dataset. And this bias is often a marker of societal and political biases, because these same populations are already marginalized in the societies in which these algorithms are deployed. In a recent paper, Scheuerman and colleagues [START_REF] Scheuerman | Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development[END_REF] studied how over one hundred datasets used in computer vision-based machine learning were created and the values conveyed within data practices employed to develop these datasets. They have shown, for example, how certain datasets are created by valuing efficiency over attention, or universality over contextuality. In fact, dataset creation is not a clearly established practice and some creators of datasets have no expertise in this area, or these same creators value the cost savings in a process where the valuation is often made based on products developed from AI models trained on these data.

Practices developed in museums and the cultural heritage sector can bring insights to the creation of datasets aimed at building AI technology. As elaborated by Jo and Gebru in a recent paper [START_REF] Jo | Lessons from archives: Strategies for collecting sociocultural data in machine learning[END_REF], there are differences between the creation of a dataset (as it is currently practiced) and the creation of a collection of artefacts. On the one hand, the creation of datasets tends to involve a low degree of intervention in the selection of elements for the dataset and in how they are annotated. The idea is the more, the better. On the other hand, the creation of collections in a cultural heritage context requires a high level of curation and intervention by various experts. In the latter case, decisions are taken based on what constitutes a significant element, thus prompting its inclusion in the collection. Practitioners in this sector have experience with selection and representation bias in the collections created. Importantly, the act of avoiding biases in datasets by finding means to de-bias them becomes another way to introduce biases. As Coleman writes: They are vital institutions full of committed individuals whose work lies in the tension between the inherently discriminatory mediating practice of organizing and categorizing and the desire to make information freely available and discoverable. That tension, or friction, provides stability and drives change. Cultural heritage institutions are perpetually confronting the questions: Are we preserving the right things? Are we making the right choices? There is no right answer.

The bases of decision-making change over time and are distributed. Each institution has its own character; the forces acting on decisionmaking are many. Most importantly, there are human beings behind the decisions and the institutional norms who are accountable. Attempts to de-bias algorithms or de-bias data have been introduced recently in response to a crisis in machine learning. But seeking to avoid accountability, disguised as objectivity or worse, neutrality, is a technocratic fallacy [Coleman, 2020] The question of who decides what to keep and show is of the utmost importance, as is inherent in the work of archivists and librarians, from whom AI practitioners must learn. And, conversely, the use of cultural heritage collections as datasets for AI is in no way straightforward and still in its infancy. As Neudecker writes: "To unlock the possibilities, libraries cannot just rely on the fast progress in AI research, but in order to fully benefit from it, need to invest into more suitable ways to share their data, and into digital curation with a considerably broader scope of use, and responsibilities with regard to managing ethical issues and biases in data" [Neudecker, 2022].

CULTURAL POLITICS AND NARRATIVES

As I mentioned briefly above, AI has often been presented as neutral or objective, based on the rationale that it is a set of statistical tools that make statistical predictions and is therefore supposedly free of bias. And if certain biases exist, there is the belief that there must also be ways to de-bias the system. While this position has been widely criticized by researchers, practitioners, and activists (for instance [O'neil, 2017, Noble, 2018, Benjamin, 2020]), has the status quo actually changed?

Recent advances in AI have shown a desire to build increasingly generic models capable of bringing together all the knowledge available digitally. Somewhere along the line, the response to criticisms of non-neutrality has been to go further in terms of the quantity of data and a model's capacity to ingest this larger amount of information. This has been clearly illustrated with linguistic models (or LLMs, large language models). Behind the construction of larger models lies the idea of universality: by building larger models capable of sorting through a wider range of data, it would be possible to build a universal model of knowledge. As Scheuerman and col-leagues write: "Implicit in this belief is the value of universality, insinuating a world that is able to be neatly captured and classified, often for the purposes of state and economic management" [START_REF] Scheuerman | Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development[END_REF]. AI is thus regarded as a universal form of knowledge compression.

To come back to the term artificial intelligence: intelligence is a broad concept, which is likely to be defined differently depending on the social group within which this notion takes shape. Furthermore, the term artificial is also problematic. Understood as technical, based on machines, this term is constructed in opposition to what is considered natural. But what is natural intelligence? Is the observed or designated intelligence of a person not also the result of social and material interactions with the environment? My position here is that, through the term artificial intelligence, the culture of machine learning does not explore the complexity and diversity of the concepts behind intelligence and artificiality, but instead takes on the disembodied form of efficient, rational and universal thinking. I do not think that cultural heritage practitioners, archivists, and librarians need methods that are imbued with intelligence. Their practice is already based on collaboration between people with their own intelligence, who jointly generate a form of 'artificial' intelligence used to understand and select the collections in their hands. More likely, there is a need for flexible and efficient methods for dealing with the huge amounts of digitized data coming from the institutions in which they work [Coleman, 2020]. Here, these actors can provide a critical voice concerning this technology not only through their use of it, but also through their practice of reflecting on the sociocultural impact of the technical objects with which they are led to select the artefacts to be preserved and exhibited. Practitioners in these sectors know the importance of regarding tools, instruments, and objects as situated in their cultural, temporal, political, or economic context. For AI technology, this practice of contextualization is just as necessary, but still lacking.

CONCLUSIONS

In this paper, my aim was to discuss how AI interacts with the museum and cultural heritage sectors. I began by outlining what the underpins the term AI and how this technology can be more fruitfully understood as infrastructure. With this in mind, I presented how AI is often regarded as a tool to accomplish tasks for these sectors. These opportunities arise from research work carried out on a European level and described in a series of reports and white papers to which I have contributed. These reports, however, lack information on how cultural heritage and museums can shape AI while integrating it into their established practices. For this reason, I have begun a discussion of these elements with a particular focus on data curation and the cultural politics of AI narratives.

This paper is the fruit of my own involvement in writing reports on the use of AI in the creative and cultural sectors, but also the result of a series of exchanges and discussions that took place in parallel with partners, players in the field, researchers, and activists, particularly during the Cultures of AI conference held in Karlsruhe in December 2022. These reflections are subjective but, I hope, can serve as complementary and critical material to accompany the institutional reports mentioned in this paper.

https://anatomyof.ai/ (all URLs here accessed in August

2023).2 https://news.un.org/en/story/2023/07/1138412

https://data.europa.eu/doi/10.2759/144212

https://data.europa.eu/doi/10.2861/602011

https://qurator.ai/

https://www.metmuseum.org/blogs/now-at-the-met/2019/wikipedia-art-and-ai

https://www.askmona.fr/en/chatbot/

https://www.fieldmuseum.org/exhibitions/maximo-titanosaur

https://dexibit.com/resource/national-gallery-predicts-future/

https://www.moma.org/calendar/exhibitions/5535


Khoa V Bui

Fengdi Li

Giao T M Nguyen

Cédric Vancaeyzeele

Chaoying Wan

Frederic Vidal
email: frederic.vidal@cyu.fr

Cédric Plesse

Ionogels and ionoelastomers as ionic conducting membranes with healable properties for soft actuators and sensors

Keywords: ionogel, ionoelastomer, vitrimer, ionic conductivity, strain sensor

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Ionogels and ionoelastomers as ionic conducting membranes with healable properties for soft actuators and sensors

Ionogels are organic materials consisting of ionic liquids (ILs) immobilized in a polymer network. They combine the mechanical properties of polymer networks with the ionic conductivity, non-volatility, and non-flammability of ILs. Ionoelastomers are elastomers based on crosslinked poly(ionic liquid) (PIL) and have been introduced recently as liquid-free and stretchable ionic conductors. As being ionically conductive, flexible and stretchable, ionogels and ionoelastomers have attracted huge attention in the field of wearable electronics, soft actuators or flexible sensors. However, in the frame of such applications, these materials are usually subjected to repeated deformation, making them susceptible to damage. In these works, we propose to enable the healing ability of these materials by the introduction of dynamic exchangeable bonds, more specifically by the synthesis of associative covalent adaptable networks, also called vitrimers. First, ionogels are prepared by introducing dynamic β-hydroxyester linkages. The networks are obtained by the in-situ step-growth polymerization between poly(ethyleneglycol) bis(carboxymethylether), trimethylolpropane triglycidyl ether (TMPTE) as crosslinker and poly(ethyleneglycol) diglycidylether as chain extender via carboxylic acidepoxy addition reaction. The resulting ionogels are soft, stretchable and conductive, with a tensile strength of 100 kPa, a maximum elongation of 300 % and an ionic conductivity of 2 x 4.10 -4 S.cm -1 at 30°C (with 50 wt. % of IL). The ionogel exhibits strain sensing ability (Gauge factor = 2.8 at 100 % strain), and low hysteresis. (see figure1). [START_REF] Bui | Stretchable, healable, and weldable vitrimer ionogel for ionotronic applications[END_REF] Concerning ionoelastomers, they are obtained in two-steps from a commercially available copolymer of polyepichlorohydrin and poly(ethyleneoxide). The commercial rubber is first modified to introduce allyl-imidazolium cationic side groups with bis(trifluoromethanesulfonyl)imide mobile counter-ions. The resulting linear PIL is then crosslinked through thiol-ene photoaddition thanks to a dithiol crosslinker bearing dynamic boronic ester (see figure 2). The resulting ionoelastomer is soft with a Young modulus of 0.2 MPa, stretchable up to 300% elongation, and exhibits an all-solid state ionic conductivity of 1.6 x 10 -5 S•cm -1 at 30 °C. [START_REF] Li | Healable Ionoelastomer Designed from Polymeric Ionic Liquid and Vitrimer Chemistry[END_REF] Both materials demonstrated efficient healing thanks to the dynamic vitrimer chemistry with a full recovery of mechanical and conducting properties. First examples of integrations of these materials in smart polymeric devices will be presented as well.

  Khoa V. Bui, Fengdi Li, Giao T.M. Nguyen, Cédric Vancaeyzeele, Chaoying Wan, Cédric Plesse, Frédéric Vidal To cite this version: Khoa V. Bui, Fengdi Li, Giao T.M. Nguyen, Cédric Vancaeyzeele, Chaoying Wan, et al.. Ionogels and ionoelastomers as ionic conducting membranes with healable properties for soft actuators and sensors. 51e Colloque National du Groupe Français d'études et d'applications des Polymères, Groupe Français d'études et d'applications des Polymères, Nov 2023, Bordeaux, France. hal-04399939

Figure 1 :

 1 Figure 1: Vitrimer ionogels with strain sensor properties Figure 2: synthesis of the ionoelastomer from PIL


J C Gonzalez-Torres

V Costan

G Damm

A Benchaib

F Lamnabhi- Lagarrigue

Dynamic control of embedded HVDC to contribute to transient stability enhancement

Keywords: Power system stability, HVDC Transmission, Hybrid AC/DC Transmission Grids, Transient stability, Multi-terminal DC grids, Wide Area Control System. Line 3

The reinforcement of the transmission systems by means of HVDC systems, appears to be one of the most suitable solutions to face the challenges related to the evolution of power systems. In this context, this paper aims to investigate the impact of the overlaid HVDC systems on stability of the surrounding AC grid. More in particular, the rotor-angle transient stability problem is addressed.

Firstly, using a benchmark power system, it is shown that HVDC links controlled to track constant power references, do not intrinsically increase transient stability margins as an AC line naturally does. Such comparison highlights the need of supplementary control layers aiming the stability enhancement of the surrounding AC grid.

A proposed control strategy to improve the angle stability of the surrounding AC system is presented. The control is based on three identified actions HVDC systems allows: Injection of damping power, injection of synchronising power and fast compensation of power disturbances. These actions are then extrapolated to MTDC grids and implemented using a novel control structure. This structure allows to keep the zero power balance in every moment (DC voltage disturbances are minimal) while dealing with active power limits of the converters.

The proposed controllers are tested on the EMT-RV software using EMT models of all the devices in the grid. Simulation results allow to show the effectiveness of the proposed controllers for enhancing the rotor angle stability of the surrounding AC grid.

INTRODUCTION

The operation and structure of transmission systems is changing as the paradigms under which they were originally designed have evolved. This evolution has created new challenges for system operators such as the necessity to transmit bulk power over long distances. When this power transmission is done through long AC transmission lines, transient stability (the ability of the system to keep synchronism after being subject to a large disturbance) is one of the limiting factors [START_REF] Kundur | Power System Stability And Control[END_REF]. One of the possibilities to overcome this challenge is the reinforcement of the transmission system. For this purpose, HVDC transmission has proven to be the most suitable technology since it overcomes some of the limitations of the AC lines. However, as shown in this paper, embedded HVDC systems used only for transmission purposes, don't intrinsically increase transient stability margins as an AC line naturally does. Control actions need to be performed in order to make the HVDC contribute to stability.

In this article, after showing the need of supplementary control layers for HVDC, a controller for active power modulations is presented. The control is based on three actions: damping power injection, synchronizing power injection and feed-forward actions. The control is applied on an HVDC in Point-to-Point configuration (PtP) embedded on a benchmark system. Then, the controller is implemented in a Multi-terminal HVDC grid (MTDC), taking into account the operational differences with respect to PtP configurations. All simulations in this paper are performed on EMPT-RV using the models of converters and cables described in [START_REF] Shinoda | Virtual Capacitor Control: Mitigation of DC Voltage Fluctuations in MMC-Based HVdc Systems[END_REF]- [START_REF] Zama | High Dynamics Control for MMC Based on Exact Discrete-Time Model With Experimental Validation[END_REF].

STABILITY MARGINS OF HYBRID AC/DC TRANSMISSION GRIDS

In order to assess the transient stability margins, two modified versions of the Klein-Rogers-Kundur system [START_REF] Kundur | Power System Stability And Control[END_REF] are compared. In the system in Figure 1(a) the two areas are interconnected through three identical AC lines. In the system in Figure 1(b), one of the AC lines is replaced by an HVDC link controlled in constant power mode. Converter connected to bus 7 controls the active power, while converter in bus 8 controls the DC voltage. The rated power of the converters is 350MVA. Compared with the original case, the exchanged power is increased to 450 MW, this power is equally distributed between the 3 lines in both cases (i.e. 150 MW per line). For the comparison, the simulated event is a three-phase fault on the third AC line near bus 7 at 1s, followed by the tripping of the line. Two different Clearing Times (CT, time span between fault occurrence and line tripping) are considered. Figure 2, shows the angle difference between the Center of Inertia [START_REF] Chiang | Direct Methods for Stability Analysis of Electric Power Systems[END_REF] (COI) of Area 1 (𝛿 𝐶𝑂𝐼,1 ) and the COI of Area 2 (𝛿 𝐶𝑂𝐼,2 ) during the event. This value reflects the equivalent angular distance between both Areas. Figure 2(a) shows the systems response when the fault is cleared 110ms after fault occurrence (CT=110ms). Firstly, it is observed that the pre-fault angle difference is the same for both systems (20°), however during the first swing the AC/DC system presents higher angular deviations meaning that the AC/DC system is closer to system splitting. Furthermore, in the post-fault situation, the angle difference tends to a higher value in the AC/DC system, representing a more loaded situation on the remaining AC lines. Figure 2(b) shows the systems response when a CT of 130ms is considered. It is shown that such disturbance leads the AC/DC system to split, while the AC system remains in synchronism. Through iterative simulations, the Critical Clearing Times (CCT) are computed. The CCT for the AC system is 199ms while for the AC/DC system it is 120ms. These values indicate that in the AC/DC case transient stability margins are smaller than in the AC case. This phenomenon is due to the synchronizing nature of the AC lines, their synchronizing power injections (they transmit power as function of the transport angle) contribute to decrease the separation of areas. In the AC/DC case, as the HVDC link is set in constant power mode (150MW), its contribution against angle (or frequency) deviations is zero. It is evident that the fast control of the HVDC needs to be used for modulating the power references during electromechanical transients. As an AC line does, the available power headroom of the HVDC link can be used in order to increase the stability margins.

SUPPLEMENTARY CONTROL FOR HVDC SYSTEMS

To enhance transient stability a supplementary control layer for the HVDC link is proposed. This new layer of control means to modify the power references during electromechanical transients. Therefore, the power reference injected to the converter can be divided in two parts as follows:

𝑃 ℎ𝑣𝑑𝑐_𝑟𝑒𝑓 = 𝑃 ℎ𝑣𝑑𝑐0 + Δ𝑃 ℎ𝑣𝑑𝑐
where 𝑃 ℎ𝑣𝑑𝑐0 is the power reference set by the TSO and Δ𝑃 ℎ𝑣𝑑𝑐 is the modulative part of the power reference used for stability enhancement. In order to get the most of the fast control of the HVDC link, three control actions are proposed to be combined, producing the following control law [START_REF] Gonzalez-Torres | Procède de commande d'un lien de transmission électrique incluant une ligne haute tension continu[END_REF]:

Δ𝑃 ℎ𝑣𝑑𝑐 = 𝑘 𝛿 (𝜃 1 -𝜃 2 -𝜃 ̅ ) ⏞ 𝐴𝐷𝐶 + 𝑘 𝜔 (𝜃 ̇1 -𝜃 ̇2) ⏞ 𝐹𝐷𝐶 + 𝑘 𝑓𝑓 (Scheduled Power -Exchanged measured power ) ⏞ 𝐹𝑒𝑒𝑑𝑓𝑜𝑟𝑤𝑎𝑟𝑑
where 𝜃 1 and 𝜃 2 are angles reflecting the angle separation of the areas, 𝜃 ̅ is an angle reference that can be dispatched by the operator as function of the scheduled power. 𝜃 ̇1 and 𝜃 ̇2 are instantaneous frequencies from the first and second areas and 𝑘 𝛿 , 𝑘 𝜔 and 𝑘 𝑓𝑓 are the control gains.

The control can be described as the combination of the three following actions:

• Angle Difference Controller (ADC): This control action modifies the power reference proportionally to the angle difference (synchronizing power) in a similar way an AC line does. Synchronizing power injection enhances transient stability margins as an AC line (see comparison in Section 2). The angle reference 𝜃 ̅ allows to make zero the ADC action in the pre-fault steady state. In addition to enhance transient stability, this action improve the static post-fault situation by modifying the steady-state power reference, thus unloading parallel AC lines [START_REF] Coronado | INELFE: main description and operational experience over three years in service[END_REF]. In particular situations, this action allows to synchronise two power systems even in the absence of an AC interconnection.

•

Frequency Difference Controller (FDC): This action modifies the power references proportionally to the frequency difference (injection of damping power). If the frequencies are measured at the Points of Common Coupling (PCC) of the converters, this strategy has proven to increase transient stability margins in every topological situation [START_REF] Sigrist | A fundamental study on the impact of HVDC lines on transient stability of power systems[END_REF]. This action is also used to damp power oscillations if the measurements provide information about the oscillation [START_REF] Gonzalez-Torres | Power system stability enhancement via VSC-HVDC control using remote signals: Application on the Nordic 44-bus test system[END_REF].

•

Feedforward Action: Through the fast response of the converters, it is possible to compensate power disturbances in the grid if they are known [START_REF] Eriksson | On the assessment of the impact of a conventional HVDC on a test power system[END_REF]. For example, if a parallel line is tripped, the power can be rapidly reallocated in the HVDC link. If fast power injections in one area occur (e.g. fast frequency response), this action can be used to rapidly share those injections between areas.

In addition of enhancing transient stability, the combination of these actions allows to modify the dynamic behaviour of the interarea oscillation by the correct tuning of the control gains.

Supplementary control for an embedded point-to-point HVDC

For the two-area AC/DC system in Figure 1(b) the active power references of the HVDC link are modulated around the steady-state set-point using the proposed controller, implemented as follows: The values 𝜃 ℎ𝑣𝑑𝑐,1 (𝜃 ̇ℎ𝑣𝑑𝑐,1 ) and 𝜃 ℎ𝑣𝑑𝑐,2 (𝜃 ̇ℎ𝑣𝑑𝑐,2 ) correspond to the phase angles (frequency) at the PCC of each converter (i.e. bus 7 and bus 8). Measurements of angles and frequencies are done using PLL-based models of Phasor Measurement Units (PMUs). The scheduled exchanged power between areas is 450MW and the reference angle is 𝜃 ̅ =20deg.

To test the controller, the fault clearing time considered in this example is CT=200ms. The three sets of gains summarized in Table 1 are considered. Case 0 represents the case without supplementary control (as in Section 2). In Case 1 the value of the synchronizing gain (𝑘 𝛿 ) is higher than in Case 2, while the damping gain (𝑘 𝜔 ) in Case 2 is higher than in Case 1. Figure 4 shows the system response for both configurations.

Regarding the stability margins, while system in Case 0 splits after a 200ms fault (since CCT=120ms, see Section 2), the systems with supplementary control remain stable as shown in Figure 4. The CCTs for Cases 1 and 2 are summarized in Table 1, it can be seen that these CCTs are even than the CCT of the case with three AC parallel lines (CCT=199ms, system in Figure 1(a)). It also must be noted that the choice of the gains can drastically modify the damping and frequency oscillation of the interarea oscillations present after fault clearing. For example, oscillations in Case 2 are better damped than Case 1 due to the higher value of the damping gain, while frequency of oscillations in Case 1 are faster than in Case 2 due to the higher value of the synchronizing gain [START_REF] Renedo | Impact of AC-line-emulation controllers of VSC-HVDC links on inter-area-oscillation damping[END_REF]. Table 1. Control gains and CCTs

Figure 5 shows the active power flowing through the HVDC as well as that passing through the AC lines. In the pre-fault situation the 3 lines transport the same amount of active power, during the fault transmitted power is zero until line 3 is tripped. After fault clearance, the power flowing through the HVDC link is modulated thanks to the controller. In Case 1, the control effort during the first swing is higher (even reaching converter limits) than in Case 2, this explains the smaller angle deviation in Figure 4. It is noted that in Case 1, HVDC power modulations are in phase with the power oscillations of the AC line (higher synchronizing gain), while in Case 2 modulations are in opposite phase, providing damping power injections (higher damping gain). Moreover, the feedforward action reallocates the power of the tripped line on the HVDC link, leading the HVDC link to double its power transmitted in the post-fault steady state. Additionally, after the disturbance, angle deviation tends to its pre-fault situation, leading the AC line to transport the same power as in the pre-fault steady state. The fast power reallocation can imply a severe power reference variation, therefore controllers allowing fast power reference modulations while keeping the stability of the DC systems are recommended (e.g. [START_REF] Zama | High Dynamics Control for MMC Based on Exact Discrete-Time Model With Experimental Validation[END_REF], [START_REF] Shinoda | Virtual capacitor for DC grid stability enhancement[END_REF]).

SUPPLEMENTARY CONTROL FOR MTDC GRIDS

Operational differences between MTDC grids and HVDC links

Inside the DC grid of an HVDC systems (point-to-point or in MTDC), the DC voltages are an indicator of the balance between power injections and extractions, thus a requirement for the correct operation of the system is to keep the DC voltage within an acceptable range. In VSC-HVDC links, control of DC voltage is managed by one station (master), evacuating all the power injected by the other station (slave). In MTDC grids it is preferred to use the DC voltage droop control which allows multiple stations to participate in the voltage control.

In a situation where multiple HVDC links are used to enhance rotor angle stability, the number of independent active power references is equal to the number of HVDC links. If an MTDC is used to enhance stability of the AC system, the number of independent active power references is equal to the number of connected stations minus one. In other words, for the same amount of converters, an MTDC provide some extra degrees of freedom, however the supplementary power modulations should be generated in a coordinated manner to guarantee the zero power balance.

Other difference concerns the power limits of the converters. In a PtP, both converters have the same rated power, if power references saturate the slave converter, the master is still able to evacuate the power. In an MTDC, the stations can have different rated powers, so if at least one converter is saturated, the stability of the DC voltage can be at risk. Thus, the supplementary controls need to guarantee the zero power balance even if converters reach their limits.

The supplementary control

In order to handle the aforementioned challenges in MTDC grids, a new control structure is proposed. The idea behind the structure is that if converter 𝑖 modulates its power in one direction, Converter 𝑗 modulates the same amount of power in the opposite direction. Through the coordination of references by pair of converters, this structure allows to apply a desired control law, while keeping the zero power balance even if converters reach their limits.

As an example, consider the 3-terminal embedded MTDC grid in Figure 6. The three power setpoints of the operator are set to zero. Consider the power of Converters 1 and 2 are modulated proportionally to the angle difference between bus 1 and 2, as follows:

Δ𝑃 ℎ𝑣𝑑𝑐,1 = 𝑘 𝛿,12 (𝜃 ℎ𝑣𝑑𝑐,1 -𝜃 ℎ𝑣𝑑𝑐,2 ) and Δ𝑃 ℎ𝑣𝑑𝑐,2 = 𝑘 𝛿,21 (𝜃 ℎ𝑣𝑑𝑐,2 -𝜃 ℎ𝑣𝑑𝑐,1 )

It is clear that if 𝑘 𝛿,12 = 𝑘 𝛿,21 , the same amount of power injected by Converter 1 will be extracted by Converter 2, preserving the zero power balance in the DC grid. Moreover, from the AC grid perspective, an amount of power is extracted from bus 1 and injected in bus 2 (similar to an AC line), passing it through the DC grid. In general, from the AC grid perspective, each pair of converters 𝑖𝑗 can be interpreted as an unified actuator, called here Virtual link 𝒊𝒋, transporting power from the AC bus 𝑖 to the AC bus 𝑗 (as a PtP does), independently of the DC grid topology.

Figure 6. Example of a virtual link between Stations 1 and 2

The concept of virtual link can be used to apply any control law between each pair of converters.

To be consistent, in this article the ADC and the FDC are applied to control power injections of an MTDC. Therefore, in order to be general, consider an MTDC composed of 𝑛 converters embedded into an AC grid, the power reference for the 𝑖 -𝑡ℎ converter is given by: 𝑃 ℎ𝑣𝑑𝑐_𝑟𝑒𝑓,𝑖 = 𝑃 ℎ𝑣𝑑𝑐0,𝑖 + Δ𝑃 ℎ𝑣𝑑𝑐,𝑖

where 𝑃 ℎ𝑣𝑑𝑐0,𝑖 is the desired set-point and Δ𝑃 ℎ𝑣𝑑𝑐,𝑖 is the supplementary reference given by the controller. In order to apply the FDC and the ADC between each pair of converters, the supplementary reference of the Converter 𝑖 is given by: where 𝜃 ℎ𝑣𝑑𝑐,𝑖 (𝜃 ̇ℎ𝑣𝑑𝑐,𝑖 ) is the phase angle (instantaneous frequency) measured at the PCC of the Converter 𝑖. The value 𝜃 ̅ 𝑖𝑗 is a reference for the angle between the PCCs of Converters 𝑖 and 𝑗. 𝑘 𝜔,𝑖𝑗 and 𝑘 𝛿,𝑖𝑗 are the control gains for each virtual link 𝑖𝑗. As in the PtP configuration, in order to enhance transient stability, the FDC injects damping power between each pair of converters, while the ADC injects synchronizing power. It has been stated that in PtP, the ADC can be considered as the emulation of an AC line (as depicted in Figure 6), thus in MTDC, when the ADC is applied between more than 2 converters, it can be interpreted as the emulation of a set of AC lines.

In order to preserve the zero power balance in the grid when converters reach their limits, in this structure it is proposed to limit the reference of each virtual link ij, locally at each converters level. The set of constraints that must be fulfilled is defined by the fact that the sum of powers of the virtual links "connected" to a converter shouldn't exceed the power limits of the converter. For a better understanding, an example of implementation is done in the following section.

Example

The proposed control has been implemented and tested on a modified version of the New England IEEE 39-Bus System. A radial MTDC grid is installed in the test system connecting buses 2, 22 and 29, as shown in Figure 7. With respect to the original case, a 450 MW load is connected to bus 2, Generation of G9 is increased by 200 MW and G6 is increased by 250 MW.

Figure 7. Modified version of the IEEE 39-bus system

The rated power of the converters, as well as their initial conditions are summarized in Table 2. The voltage angles suggest that the tendency of the power-flow is from the right part to the left part of the system (from Converters 2 and 3 to 1). The positive and negative headroom of the converter (Δ𝑃 ℎ𝑣𝑑𝑐,𝑖 𝑚𝑎𝑥 , Δ𝑃 ℎ𝑣𝑑𝑐,𝑖 𝑚𝑖𝑛 ) are the difference between the rated power and the active power set-point. In order to test the controller, a three-phase fault is simulated in line 26-27 near bus 27 followed by the tripping of the line. For the analysis, different Clearing times are considered in the following.

Severe fault (CT=𝟏𝟒𝟎ms): In a first example, the line is tripped 140ms after fault occurrence. Figure 9(a) shows the angle separation between G9 and G1 for two situations: In the first one no supplementary control is used (CR for constant references), in the second one, the proposed control is implemented (ADC-FDC). When no supplementary control is implemented (CR), it is shown that the fault leads to the separation of generator G9. In the other hand, the implementation of the ADC-FDC strategy manages to keep the system in synchronism.

Figure 9(b) shows how active power is modulated to keep the system stable. During the first swing, Converters 1 and 2 reach their limits during some milliseconds (approximately between t=700ms and t=950ms). During this time span, Converter 3 stops the power modulations, even if it has still some available headroom (approx. 100MW). Thanks to this action, the discharge of the DC grid is avoided. In Figure 9(c) it can be seen that the DC voltages remain by far into an acceptable range of ±10% of the nominal voltage. Note that Converter 3 stops to modulate power thanks to the limitation of the virtual links in Figure 9(c). It is shown that during the mentioned time span, all the virtual link references are saturated, therefore all power references stop their modulations. It is also clear that, the value of the power references in the post-fault steady state are not the same as before the fault, especially for Converters 2 and 3. This is due to the ADC, which emulates AC lines between converters. Angle separations are bigger after the fault, so the ADC makes Converter 2 to "send" more power to Converter 3 to keep both buses closer. It can be seen that the virtual link 23 "sends" around 100MW in the post-fault steady state.

Less severe fault (CT=𝟏𝟎𝟎ms): In this example, the fault location is the same, but the line is tripped 100ms after fault occurrence. Figure 10 show the angle difference between PCCs, for both strategies. It is shown that in the CR strategy, during the first swing, angles deviate more than in the FDC-ADC case. More important is to appreciate that in the static post-fault situation, when FDC-ADC is implemented, angles tend to remain closer from each other. This means that surrounding AC lines are in a less stressed situation than in the CR case. This example shows the usefulness of the FDC-ADC control, not only for transient stability enhancement, but also for static purposes.

CONCLUSIONS

This paper proposes supplementary controllers for active power modulations of embedded HVDC systems allowing to enhance the transient stability of the surrounding AC system. It has been proved that HVDC systems controlled in constant power mode, do not intrinsically increase transient stability margins as an AC line naturally does. Therefore supplementary controllers as the one presented in this paper are necessary.

The proposed controllers are based on the injection of damping and synchronising power as well as the compensation of power disturbances. These controllers use measurements of angles, instantaneous frequencies and powers flowing in the AC network. For the MTDC configuration a new decentralized control structure is presented. This structure uses the concept of Virtual Links in order to assure the zero power balance of the MTDC grid even when converters reach their limits. EMT simulation results, using benchmark power systems, show the effectiveness of the proposed controllers in PtP as well as in MTDC configuration. It has been also shown how this controllers are effective not only for transient stability enhancement, but also for static purposes.

BIBLIOGRAPHY

Figure 1 .

 1 Figure 1. Modified Klein-Rogers-Kundur's two-area systems under comparison.

Figure 2 .

 2 Figure 2. Angle difference between areas.

Figure 3 .

 3 Figure 3. Control implementation for an HVDC link

(a) Case 1 :Figure 5 .

 15 Figure 5. Active power through the AC/DC corridor. The rated power of the HVDC is 350MVA

Figure 4 .

 4 Figure 4. Angle difference between areas

  𝛿,𝑖𝑗 (𝜃 ℎ𝑣𝑑𝑐,𝑖 -𝜃 ℎ𝑣𝑑𝑐,𝑗 -𝜃 ̅ 𝑖𝑗 ) 𝑖𝑗 = 𝑘 𝜔,𝑗𝑖 ; 𝑘 𝛿,𝑖𝑗 = 𝑘 𝛿,𝑗𝑖

Figure 9 .

 9 Figure 9. System response after a 140ms three phase fault

Figure 10 .

 10 Figure 10. Angle separations using different strategies after a 100ms fault

Table 2 .

 2 Converters characteristics and set-pointsThe implementation of the controller on Converter 1 is depicted in Figure8(all converters are equipped with an equivalent one). The control receives frequency and angle measurements from other stations to apply the ADC and FDC. The ADC and FDC action of each pair of converters (or Virtual link) are limited to a value (𝑃 𝑙𝑖𝑛𝑘,𝑖𝑗 𝑚𝑎𝑥 , 𝑃 𝑙𝑖𝑛𝑘,𝑖𝑗 𝑚𝑖𝑛 ) and recombined to generate the power reference of the station. The limits of each virtual link are summarized in Table3. They are designed to respect the available headroom of the converters (e.g. Δ𝑃 ℎ𝑣𝑑𝑐,1 𝑚𝑎𝑥 ≥ 𝑃 𝑙𝑖𝑛𝑘,12 𝑚𝑎𝑥 -𝑃 𝑙𝑖𝑛𝑘,31 𝑚𝑖𝑛 ). It must also be noted that the limits of a virtual link 𝑖𝑗, are applied to the virtual link reference in Converter 𝑖 as well as in Converter 𝑗. Virtual Link 𝑖𝑗 𝑘 𝜔,𝑖𝑗 [MW/Hz] 𝑘 𝛿,𝑖𝑗 [MW/deg] 𝜃 ̅ 𝑖𝑗 [deg] 𝑃 𝑙𝑖𝑛𝑘,𝑖𝑗

	Converter 𝑖	PCC Voltage [pu]	Rated power [MVA]	𝑃 ℎ𝑣𝑑𝑐0,𝑖 [MW]	𝑄 ℎ𝑣𝑑𝑐0,𝑖 [MVAR]	Δ𝑃 ℎ𝑣𝑑𝑐,𝑖 𝑚𝑎𝑥 [MW]	𝑚𝑖𝑛 Δ𝑃 ℎ𝑣𝑑𝑐,𝑖 [MW]
	1	1∠0°	700	-450	100	1150	-250
	2	1.05∠15.24°	400	200	60	200	-600
	3	1.01 ∠ 6.8°	400	250	60	150	-750

Table 3 .

 3 Control gains


domain: info shs

text:
Luciana Benotti

Karën Fort

Min-Yen Kan

Yulia Tsvetkov

Understanding Ethics in NLP Authoring and Reviewing

With NLP research now quickly being transferred into real-world applications, it is important to be aware of and think through the consequences of our scientific investigation. Such ethical considerations are important in both authoring and reviewing. This tutorial will equip participants with basic guidelines for thinking deeply about ethical issues and review common considerations that recur in NLP research. The methodology is interactive and participatory, including case studies and working in groups. Importantly, the participants will be co-building the tutorial outcomes and will be working to create further tutorial materials to share as public outcomes.

Motivation and structure

In late 2021, the Association for Computational Linguistics' executive committee appointed an Ethics Committee to investigate long-term ethical issues of the community's research and legislate any policy and workflow changes to the authoring, reviewing and other processes. The committee surveyed the constituency's opinions, wants and needs, finding that the majority of respondents felt that clear guidelines on acceptable practices regarding authoring and reviewing were needed. Specifically, in response to the question "What do you think are the most urgent tasks for the global *CL ethics committee?", 50% of respondents highlighted the need for more resources and discussion forums to raise awareness in the community about ethical issues in research and to clarify ethical review policies, 36% specifically mentioned the importance of creating dedicated training materials for authors and reviewers, and 26% encouraged more outreach initiatives to facilitate discussion about ethical research in the community.

This tutorial proposal thus follows from the mandate from the survey, such that more interactive opportunities exist to best communicate and train our membership on ethical guidelines and research practices.

The tutorial also draws on related, successful past tutorials on NLP reviewing and socially responsible NLP (≈100 participants) [START_REF] Cohen | Reviewing natural language processing research[END_REF][START_REF] Tsvetkov | Socially responsible NLP[END_REF], where some of the proposed tutorial instructors have been involved.

We propose a hybrid tutorial to best allow equitable access to the topic of this tutorial, especially to familiarize new community members and those who cannot afford access to attend physically. We plan to have dedicated presenters that can coordinate activities for the expected online participants. We may plan to use specific e-resources that can help facilitate virtual group discussions (e.g., Padlet, PollEverywhere, Google Docs, Slack).

We intend to make the tutorial presentation materials publicly available, in alignment with the stated goals of the tutorials. As an example, annotated presentation slides (with presenter notes) will be made available, such that tutorial participants can bring exercises of different lengths into classroom settings for research groups as well as undergraduate and graduate classes. We will organize a separate website via a Github repository1 (to be owned by the ACL) to centralize our tutorial resources for long-term and public access.

However, due to the sensitive and formative nature of the small-group discussions, we will not record the small-group discussions so that participants can speak freely and off-the-record. The plenary, lecture-styled sessions (Sessions 1 and 7) may be recorded live, or pre-recorded offline.

This proposal tutorial aligns with the theme track "Reality Check"of ACL 2023. Most of the challenges addressed by the theme track, including out-of-domain generalization, adversarial attacks, spurious patterns (both linguistic and social), insensitivity to basic linguistic perturbations such as negation, sensitivity to perturbations that should not matter (e.g., order and wording of prompts), are deeply related to ethical considerations of NLP research. In particular, proper discussion of risks (e.g., failure modes and vulnerabilities to adversarial attacks) and limitations (the scope of your claims, not overselling) is an integral to the theme and also for ethics authoring and reviewing. Finally, the theme track raises the question "what is an improvement in the real-world?", which is directly related to the social impact issues addressed by ethics reviewing.

Tutorial Content

Type: 1/2 day, Introductory Expected Attendees: 100 Audience: Authors and reviewers, interested parties Desired Location: Preferably ACL (Toronto, Canada) Prerequisites: Introductory background in natural language processing and deep learning, including a basic familiarity of commonly-used approaches to text classification and generation, and standard NLP tasks. Fluent command of English.

Ethical consideration overarch our duties as researchers and scientists. As members of our community, and representatives of our works to both the general public and practitioners, we need to consider the ramifications of our work. The need for a better understanding of ethics is reflected in both authoring and reviewing, key functions of our community's peer review process.

Unintended and harmful ethical lapses and consequences can be largely avoided through contin-uing communication. Rather than assume that research is purely an intellectual pursuit, our tutorial invites participants to consider ethics as an integral component of the holistic framework of impactful research work. Table 1 presents our proposed tutorial's outline. Our aim is to provide handson experience with ethical issues through a smallgroup activity, both at the physical conference and in breakout rooms for online participants.

Ethics requires healthy debate and deep thought, and for these reasons, our structure incorporates a Socratic exercise, where participants spend a large part of the session discussing a concrete case of problematic research. A Community of Inquiry2 approach will be taken such that participants engage in role-playing and discussing about ethical issues through reading 1-2 problematic hypothetical research abstracts from a curated set ( § 2.1). Using Socratic-style questioning, presenters guide the participants to engender discussion and realise ethical issues in the works.

Importantly, the participants will be co-building the tutorial outcomes and will be working to create further tutorial materials to share as public outcomes of the exercises. For many issues in ethics, the evolving discussion creates more value than the actual conclusions. This is why we propose such a dialectic approach.

To encapsulate the exercise, the presenters will first introduce the key ways that ethics impacts authoring and reviewing (Segment 1), summarise the group discussions' key points (Segment 6) and conclude with pointers to references and other training materials (Segment 7), including best practices for authoring ethical consideration sections [START_REF] Benotti | Ethics consideration sections in natural language processing papers[END_REF] and reviewing.

Due to the necessary interactivity of the session, we plan to limit the registrations for the tutorial to 100. This is to cater to having approximately a 25:1 ratio for presenters to participants. A larger volume than this jeopardizes the necessary interactive nature of the tutorial, which requires input from all participants.

Case studies

In the interactive portion of the tutorial, we will discuss research abstracts and will facilitate group discussions guided by critical questions about the proposed technology. Participants will be encour-aged to discuss the following questions:

• Ethics of the research question: Would answering this research question advance science without violating social contracts? What are potentials for misuse?

• Social impact of the proposed technology and its potential dual use: Who could benefit from such a technology? Who can be harmed by such a technology? Could sharing data and models have major effects on people's lives?

• Privacy: Who owns the data? Understanding the differences between published versus publicized data, understanding the concept of user consent, and thinking about implicit assumptions of users on how their data will be used.

• Bias in data: What are possible artifacts in data, given population-specific distributions? How representative is this data to address the target task?

• Social bias and unfairness in models: Is there sufficient control for confounding variables and corner cases? Does the system optimize for the "right" objective? Could the system amplify data bias?

• Is the proposed evaluation sufficient? Is there a utility-based evaluation beyond accuracy; e.g., measurements of false positive and false negative rates as measurements of fairness? What is "the cost" of misclassification and fault (in)tolerance?

Our case studies will be hypothetical; i.e., we will not use abstracts from existing studies but will create abstracts that will allow us to highlight potential ethical issues covering multiple, diverse ethics-related topics, including human subjects research and institutional review board (IRB) approval, bias and fairness, privacy, misinformation, toxicity/content moderation, energy considerations/green AI. We will develop several representative case studies for participants to choose from; we show an example below that illustrates multiple problematic aspects within one study, which was adapted from an actual problematic recent study.

The following abstract introduces an unethical research question, a demographically biased data set, a data collection procedure that violates user privacy, a problematic evaluation procedure, and claims/potential applications that can lead to significant harms to individuals.

Abstract: Faces contain more information about sexual orientation than can be perceived by the human brain. We used deep neural networks to extract features from over 35 thousand facial images. Given a single facial image, a classifier could correctly distinguish between gay and heterosexual men in 80% of cases, and in 70% of cases for women. Accuracy increased to 90% and 80%, respectively, given five facial images per person. Facial features employed by the classifier included both fixed (e.g., nose shape) and transient facial features (e.g., grooming style). Consistent with the prenatal hormone theory of sexual orientation, gay men and women tended to have gender-atypical facial morphology, expression, and grooming styles. Prediction models aimed at gender alone detected with 55% and 53% accuracy for gay males and gay females, respectively. Such findings advance our understanding of the origins of sexual orientation and the limits of human perception. Given that organizations are using computer vision algorithms to detect people's intimate traits, our findings expose a threat to the privacy and safety of gay men and women.

Readings

We will cover a diversity of primary research on ethics, sourced beyond the presenters' own works, in the plenary sessions of the tutorial. Also, due to the abbreviated length of the 1/2-day format, our tutorial will cross reference sources from the list, rather than specifically require participants to do readings before the tutorial.

A full reading list of over 200 works has been cross-compiled by the full ACL Ethics Committee, sourced from university courses on NLP Ethics and related topics. The list available on Github3 . The list can be updated by pull requests and is sortable by both topic and publication type. Topics and readings include the following among others: data usage [START_REF] Drugan | Shared resources, shared values? ethical implications of sharing translation resources[END_REF][START_REF] Couillault | Evaluating corpora documentation with regards to the ethics and big data charter[END_REF][START_REF] Mieskes | A quantitative study of data in the NLP community[END_REF][START_REF] Bender | Data statements for natural language processing: Toward mitigating system bias and enabling better science[END_REF][START_REF] Kann | Towards realistic practices in lowresource natural language processing: The development set[END_REF][START_REF] Rogers | just what do you think you're doing, dave?' a checklist for responsible data use in NLP[END_REF][START_REF] Gebru | Datasheets for datasets[END_REF], crowdsourcing [START_REF] Bederson | Web workers unite! addressing challenges of online laborers[END_REF][START_REF] Fort | Last words: Amazon Mechanical Turk: Gold mine or coal mine?[END_REF][START_REF] Callison-Burch | Crowd-workers: Aggregating information across turkers to help them find higher paying work[END_REF][START_REF] Fort | Crowdsourcing for language resource development: Criticisms about amazon mechanical turk overpowering use[END_REF][START_REF] Hara | A data-driven analysis of workers' earnings on amazon mechanical turk[END_REF][START_REF] Toxtli | Quantifying the invisible labor in crowd work[END_REF]), biases (Blodgett et al., 2020), language diversity [START_REF] Tatman | Gender and dialect bias in YouTube's automatic captions[END_REF][START_REF] Jurgens | Incorporating dialectal variability for socially equitable language identification[END_REF][START_REF] Zmigrod | Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology[END_REF][START_REF] Tan | Mind your inflections! Improving NLP for non-standard Englishes with Base-Inflection Encoding[END_REF][START_REF] Koenecke | Racial disparities in automated speech recognition[END_REF][START_REF] Bird | Decolonising speech and language technology[END_REF], rigorous and meaningful evaluation [START_REF] Caglayan | Curious case of language generation evaluation metrics: A cautionary tale[END_REF][START_REF] Ethayarajh | Utility is in the eye of the user: A critique of NLP leaderboards[END_REF][START_REF] Antoniak | Bad seeds: Evaluating lexical methods for bias measurement[END_REF][START_REF] Tan | Reliability testing for natural language processing systems[END_REF], environmental impact [START_REF] Strubell | Energy and policy considerations for deep learning in NLP[END_REF][START_REF] Zhou | Establishing an evaluation metric to quantify climate change image realism[END_REF][START_REF] Henderson | Towards the systematic reporting of the energy and carbon footprints of machine learning[END_REF] 3 Presenters (listed in alphabetical order) Luciana Benotti (luciana.benotti@unc.edu.ar, she/her) is an Associate Professor at the Universidad Nacional de Córdoba, in Argentina. Her research interests cover many aspects of situated and grounded language, including the study of misunderstandings, bias, stereotypes, and clarification requests. She is the elected chair of the NAACL executive board and is also serving as a member at large of the ACL Ethics committee.

Karën Fort (karen.fort@sorbonne-universite.fr, she/her) is an Associate Professor at Sorbonne Université and does her research at LORIA in Nancy, France. She has been working on ethics in NLP since 2014. She was co-chair of the first two ethics committees in the field (EMNLP 2020 and NAACL 2021) and is co-chair of the ACL ethics committee. She has been a member of the Sorbonne IRB between 2019 and 2022 and she teaches ethics at undergraduate and graduate level in Paris, Nancy, and the University of Malta.

Min-Yen Kan (kanmy@comp.nus.edu.sg, he/him): Associate Professor at the National University of Singapore and a co-chair of the ACL Ethics Committee. He has taught over 5,000 graduate and undergraduate students on his research interests in digital libraries, information retrieval and natural language processing. Yulia Tsvetkov (yuliats@cs.washington.edu, she/her) is an Assistant Professor at the Paul G. Allen School of Computer Science and Engineering at the University of Washington, USA. Her research focuses on computational ethics, multilingual NLP, and machine learning for NLP. She developed a course on Computational Ethics in NLP and is teaching it at both undergraduate and graduate levels since 2017, and she is a co-chair of the ACL Ethics Committee.

Diversity considerations

The instructors of this tutorial are affiliated in different geographic regions. Luciana Benotti is in Latin America, Kären Fort in Europe, Min-Yen Kan in Asia and Yulia Tsvetkov in North America. Three of them identify with the female gender and one with the male gender. All of them are part of the ACL Ethics committee. We will promote this tutorial to all the ACL members but in particular to affinity groups such as Masakane, Lat-inX, North Africans, disabled in AI, indigenous in AI, Khipu and similar groups with the help of EquiCL. EquiCL is the only Big Interest Group in the ACL, its scope is equity and diversity and its current officers are Marine Carpuat (chair), Aline Villavicencio (secretary), Zeerak Waseem (communication with workshops and affinity groups). We think it is crucial to reach a diverse audience for this tutorial.

Ethical considerations

We are well aware that we do not compose a perfectly diverse committee and commit to pay close attention to ensure all participants' points of views are faithfully acknowledged.

We decided to use synthetic case studies in the form of abstracts, rather than real and complete articles, in order to preserve the anonymity of the authors, to refrain from personal criticism, and to allow the participants to focus more on the discussion than on the reading. We will create a variety of abstracts, with different forms, exemplifying different ethical issues, however, they will not cover all the possible ethical issues in the domain. Finally, the synthetic case studies will be clearly identified as such.

Here are examples of other abstracts our team is developing and may field during the actual tutorial.

Abstract: Do people feel better on when on vacation? We study the social media sentiment of families when on vacation and during normal periods. Using a snowball sampling method, we solicited participants active on various social media platforms (N = 1, 337) to voluntarily disclose when and where they went on vacation. We then crawled our participants social media posts to determine whether specific time periods and destinations for vacations were correlated with higher levels of enjoyment. Initial analysis showed that such features do yield significant prediction performance improvement: for example, vacations taken close-by tended to have markedly higher satisfaction. Our model, VACAY-OK, accounts for such factors in novel gating network architecture, improving joint vacation period and sentiment detection by over 10% F1. Our analysis validates patterns of interest: vacations with immediate family exhibit a bimodal distribution, and that ones without in-laws have a significantly higher satisfaction and detection rate. For robustness in identification, we collect and study the pattern and text of social media posts by parents, children, extended family members and friends of vacation-goers. To ensure both the accessibility and the reproducibility of our experiments, we have gathered the raw social media data, culled with permission from the original posters, and released it as a zip archive, available without moderation, in the footnote.

The above abstract illustrates challenges with minors giving consent and with individuals releasing confidential data unknowingly (details shared by friends).

Abstract: In this paper, we present the largest existing language model as of today (989 trillion parameters, dataset of 968 Pb), BigBlue. We trained it using not only freely available Web content, but also Web archives and publicly-accessible EPub versions of all the books of the commericial Amazonia bookshop web storefront. The best results were attained using reinforcement learning with human feedback (RLHF) coming from crowdworkers. The model improves the state-of-the-art in Natural Language Processing (NLP) in most tasks, including sentiment analysis (+0.02 F-measure), dependency syntax (+0.015 UAS), named-entity recognition (+0.05 F-measure), etc. BigBlue is available through an API on our company's website.

The above abstract illustrates some of the many issues around LLM, including carbon footprint.

Abstract: In this paper, we present the first language model developed for Fridonian, a regional language spoken in the South-East of Austrafrancia by more than 500,000 people. We detail the collection of the corpus, the creation of the model and its evaluation on basic NLP tasks. The corpus creation itself is an achievement, as it is well-known and documented (Birdoff, 2020) that Fridonian speakers are reluctant to allow the recording of their language, for fear it will be used against them by the country's Sidonian majority. However, we managed to record 100 hours of speech in different settings: family discussions, local authority meetings, ceremonies and traditional story telling contests. We used this dataset to train a language model which we evaluated on sentiment analysis and named-entity recognition tasks, with impressive results (resp. 0.56 and 0.75 in F-measure). The created language model (FridoBERT) and the dataset (FridoSPEECH) are freely available on GitHub.

The above abstract illustrates the lack of consideration of the speakers and their needs and context.

Abstract: Is sentiment analysis dependent on culture or only on language? In this paper we describe the collection and annotation of a billion-word dataset over 7 different Spanish dialects spoken in Latin America, annotated with fine-grained sentiment analysis cues. To the best of our knowledge, this is the largest Latin American dataset currently available. We used data from public groups in Telegram where locals talk about products and services. We evaluate the state-of-the-art sentiment analysis models on it, showing that their performance is significantly lower than the state-of-the-art results on well-known benchmark for Spanish. The dataset annotation took three months. We selected crowdworkers after a careful exam to ensure they were fluent in the target dialect. We payed them 4 US dollars per hour, a fair wage considering it was two times the minimum wage in these countries. Our sentiment classifier fine-tuned per dialect outperforms previous models on Spanish by large margins. Our model has been used for 12 months by hundreds of companies in Latin America. To serve the model, we implemented a costing model to provide this service at a uniform price to all regions of the world. We conducted three usability studies in collaboration with three companies, which demonstrated that the integration of our sentiment analysis detector markedly increased users' engagement with their marketing advertisements.

Abstract: With the rise of large language models (LLMs), they have become useful in critical settings such as healthcare support, helping reduce administrative burden and improve predictive analytics. However, there is an emerging concern that LLMs encode gender and racial biases, which propagate to healthcare decisions. In this work, we propose a simple and effective approach to controlling for biases in LLMs through data anonymization, specifically focusing on patient names that often reveal gender and race. We introduce a novel and highly granular dataset of over 9.9 million patients' electronic health records annotated with patient demographics, including gender and race. We first fine-tune state-of-the-art LLMs on the raw healthcare data, and establish the presence of harmful biases in standard NLP benchmarks such as coreference resolution (10% lower F1 score for women compared to men) and named entity recognition (7% lower for White compared to Black patients). We also show that naively fine-tuned language models can be used to predict gender and race of the patients in held-out health records with high accuracy. Then, we show that a simple anonymization of the health data, by replacing patient names with generic placeholders, reduces the prediction gap in finetuned LLMs by up to 4% for gender, and up to 2.5% absolute for race. Our findings address important questions for fairness in NLP and algorithmic decision-making. Our code and data are publicly available to facilitate reproducibility.

The above abstract illustrates privacy concerns with using healthcare data, releasing data publicly, the problem with assumptions about privacy -that obfuscating names does not anonymize data since many other features correlate with demographics, annotation issued with binary gender and race, US-centric assumptions about conceptualization of race, evaluation of fairness on intrinsic benchmarks that might not reflect the true use case scenarios. Additional potential discussion is on what constitues "high accuracy" when the harm from misclasification is high (the weight of wrong predictions should be high).

One Liners for Videos

Luciana [Hi. I am Luciana Benotti from Argentina and I think one of the most important parts about ethics is diversity. Ethics should take into account cultural diversities and promote decolonization.]

M in [Hi! I'm Min from the U.S. of A, but I live and teach in Singapore. My take is that ethics helps us leave no one behind.] Karen [I am Karën Fort, from France, and for me, thinking "ethically" means opening your mind and trying to consider the world in a larger sense in terms of space (what could this do to people elsewhere) and time (what would be the effect in 10 years).] Y ulia [I am Yulia Tsvetkov from Seattle, U.S. and I do research on computational ethics and teach ethics in NLP. In addition to what others have mentioned, an important aspect of ethics research for me is in finding new ways to integrate exciting scientific questions that our community studies with positive applications of our technologies and making these technologies accessible to very diverse users. ]

Meta. Then we break to a quick recap of the goals, content and intended audience for the tutorial... Block 1 @Speaker 1 Goals. The first questions we will ask is "What is ethics"? and "Why is it important towards long-term progress for NLP?" This tutorial will engage you in a dialog to co-construct answers to these questions and help you use your knowledge in your authoring and reviewing of scientific studies.

Block 2 @Speaker 2 Content. Our 1/2 day tutorial will start with framing these questions but quickly follow into an interactive case study to ground our discussions. To involve all participants in a deep manner, we will initiate small-group discussions to analyze hypothetical paper abstracts for ethical concerns.

With our open-ended dialogue completed, we will be well-equipped to apply our knowledge towards the concrete problem of reporting and reviewing scientific work, such as writing the ethics statement and understanding the process and role of ethical reviewing. We will round out our session with pointers to ethics in NLP resources, such as our community's own reading list and links to how other communities are handling ethics and ethical concerns. Block 3 @Speaker 3 Intended Audience. This tutorial is intended for all parties interested in the ethical aspects of natural language processing and computational linguistics research. We especially welcome authors and reviewers of papers, who will find the materials about ethical authoring and reviewing helpful in shaping the discourse in our community. @Speaker 1. We hope you will join us for this session, whether in person or virtually. We need your support to spread awareness of this critical aspect in today's accelerated technology adoption. Thank you!

Table 1 :

 1 Tutorial Outline. Each segments' duration is ∼30 minutes, but 3 hours in total. Segments 2-6 will be conducted in small-group interaction.

	Segment Topic	Led by
	1. Introduction and Foundations for Ethics	Presenters
	2. Case Studies: Problematic Ethical Re-	Participants
	search -First reading
	3. Structured Interaction / Dialogue	Presenters,
		Participants
	4. Case studies -Second reading (Rota-	Participants
	tion)
	5. Group Presentations	Group
		Leads
	6. Summary and Common Issues	Presenters
	7. Discussing and Troubleshooting Ethics	Presenters
	and Further Resources

https://github.com/acl-org/ ethics-tutorial, or similar (not yet published).

https://en.wikipedia.org/wiki/ Community_of_inquiry

https://github.com/acl-org/ ethics-reading-list