Hala Skaf-Molli

Pascal Molli
email: molli@loria.fr

Charbel Rahhal

Hala Naja-Jazzar

Collaborative Writing of XML Documents

Keywords: CSCW, XML document, DTD validation, automatic repairing

Collaborative writing is the process of two or more people working together to create a common document. People can be distributed in time, in place and across organizations. They can share writing different kinds of documents. In this paper, we focus on collaborative writing of XML documents. XML documents must be validated via a set of constraints called DTD to be considered as consistent. In cooperative writing, shared XML documents are replicated on different sites. This improves the availability of the documents. The modifications of a replica at a site are sent and integrated on all the others sites, in order to ensure the convergence of the replicas. While on each site, a replica can be validated via the DTD. After the reconciliation (also known as merging) of the different replicas, the result of merging consistent replicas may not validate the DTD. A major problem of this result, that is not possible to open a document with the tool used to edit it. To overcome this problem, we propose an automatic approach to repair inconsistency.

I. INTRODUCTION

Collaborative writing (CW) is the process of two or more people working together to create a common document. People involved in CW can be distributed in time, in place and across organizations. Collaborative writing is standard practice in technical and scientific settings; some examples include research papers, software development, proposals for funding and user manuals.

When collaboration is efficiently managed, the advantages of working in a group include increased efficiency, reduced errors and the benefits of different viewpoints and expertise [START_REF] Tammaro | Collaborative Writing Is Hard to Support: A Field Study of Collaborative Writing[END_REF], [START_REF] Noël | Empirical study on collaborative writing: What do co-authors do, use, and like?[END_REF]. If collaboration is poorly supported, it can lead to inconsistencies, misunderstandings, conflicts, redundant work and coordination problems.

In this work, we are interested in collaborative writing of XML (eXtensible Markup Language) documents. XML document usually comes with a Document Type Definition DTD that specifies the structure of the document by defining the legal building blocks of an XML document. For instance, a DTD can specify that an XML document must have only one title. This is a kind of structural constraints on the XML document. An XML document is valid (or consistent) if it conforms to its DTD. Traditionally, XML-based format environment has a built-in function to verify the validation of the XML document. In case of a violation of the DTD, an error message is displayed and the user has to modify her document else she cannot save it. This means that the author can produce only valid documents.

In collaborative writing, when several authors work together to edit a document. The shared document is usually replicated at the site of each participant [START_REF] Molli | SAMS: Synchronous, Asynchronous, Multi-Synchronous Environments[END_REF]. This allows more availability of the document, in addition, each participant has her own replica of the document, she can work insulated in her workspace. This phase of cooperation produces copies divergence [START_REF] Dourish | The Parting of the Ways: Divergence, Data Management and Collaborative Work[END_REF], [START_REF] Molli | SAMS: Synchronous, Asynchronous, Multi-Synchronous Environments[END_REF]. From times to times copies have to be resynchronized in order to integrate the contribution of the different participants.

There are several algorithms to synchronize XML, such as SO6 [START_REF] Oster | Supporting Collaborative Writing of XML Documents[END_REF] and DeltaXML [START_REF] Deltaxml | managing change in an xml environment[END_REF]. However, existing algorithms do not ensure the validation of the DTD. Therefore, the merging phase can produce a document that does not validate the DTD, even each different replica is valid. Thus, it is not possible to open the document with the tool that generated it. This constitutes a major problem.

A possible solution is to use the approach proposed by IceCube [START_REF] Kermarrec | The IceCube Approach to the Reconciliation of Divergent Replicas[END_REF]. IceCube tries to find an ordering (Scheduling) of concurrent operations that respects constraints. An operation that does not respect the constraint is canceled .The major drawbacks of IceCube are the risk of combinatorial explosion, the annulation of some operations and finally how to transform DTD to IceCube constraints. Annulation of operations is not an acceptable solution in collaborative writing.

In order to develop an environment that respects the requirements of collaborative writing of XML and the validation of the DTD. We propose an automatic approach to repair this violation.

If the result of merging different remote modifications is a corrupted document, a set of repairing actions is proposed to re-establish the consistency of the document. For example, if after the merge, the document has two titles instead of one title (according to its DTD). Possible repairing actions are:

(1) delete one of the two titles, (2) put one of the titles as a comment, and finally (3) concatenate the two titles into one title.

Generally, it is possible to repair a document in many different ways. However, in collaborative context, repairing actions must respect the intention of the user and must form a minimal set of changes to the original document. This means that the work done by a user must not disappear after repairing the inconsistency. A person who participates in collaborative writing will disagree to lose her work because inria-00432593, version 1 -16 Nov 2009 of the "inconsistency" problem.

In previous work, we described how the operational transformation approach (OT) was used as a theoretical foundation to build a generic and safe synchronizer [START_REF] Molli | Using the Transformational Approach to Build a Safe and Generic Data Synchronizer[END_REF]. Later, we defined the specific transformation functions to synchronize XML documents [START_REF] Oster | Supporting Collaborative Writing of XML Documents[END_REF]. These functions are integrated in SO6 tool. SO6 ensures convergence of the replicas but it does not ensure the validation of the DTD. It is well known that the operational transformation approach maintains syntactic consistency of replicated data but provide no guarantee on semantic consistency [START_REF] Sun | Consistency Maintenance in Real-Time Collaborative Graphics Editing Systems[END_REF], [START_REF] Skaf-Molli | Semantic Consistency for Collaborative Systems[END_REF]. XML documents can be corrupted after reconciliation. In this paper, we propose a repairing approach in order to overcome this problem.

The paper is structured as follows. Section II introduces the operational transformation approach which serves as a theoretical foundation for XML reconciliation tool SO6. Section III presents a scenario of collaborative writing of XML documents. This scenario shows the result of the application of SO6 and motivates our work. Section IV explains the repairing approach and shows how we can apply existing repairing algorithms in collaborative writing of XML documents. Section V concludes and points out some limits of our works and future work.

II. BACKGROUND

This section describes the Operational Transformation approach (OT) that is the theoretical foundation of the generic and safe synchronizer. OT [START_REF] Ellis | Concurrency Control in Groupware Systems[END_REF] is an optimistic replication model used in real-time group editors domain. OT considers n sites, each site owns a copy of shared data. When a site performs an update, it generates a corresponding operation, which is first executed locally and then broadcasted to other sites. Every operation is processed in four steps: (a) generated on one site, (b) broadcasted to other sites, (c) received by other sites, (d) executed on other sites.

The execution context of a received operation op i may be different from its generation context. In this case, the integration of op i by other sites may lead to inconsistencies between replicas. For instance, we consider two sites site 1 and site 2 working on a shared data of type string of characters initially equal to the string "efect". A string of characters can be modified with the operation ins(p,c) for inserting a character c at position p in the string. We assume the position of the first character in a string is 0. user 1 and user 2 generate and execute two concurrent operations op 1 =ins(2,f) and op 2 =ins(5,s), respectively. When op 1 is received and executed on site 2 , it produces the expected string "effects". But, when op 2 is received on site 1 , since it does not take into account that op 1 has been executed before it, its execution leads to the state "effecst". Finally, the copies of site 1 and site 2 do not converge.

In the operational transformation (OT) approach, before being executed, received operations are transformed regarding concurrent operations that were already executed on the local copy. This transformation is performed by calling transformation functions.

Definition A transformation function T takes two concurrent operations, op 1 and op 2 , must be defined on a same state S. The function computes a new operation op 1 equivalent to op 1i.e. has the same effects -but defined on the state S = S op 2 . S is the state resulting from the execution of op 2 on state S.

Using OT approach, our previous example is now executed as follows. When op 2 is received on site 1 , op 2 needs to be transformed regarding op 1 . The integration algorithm calls the transformation function T(op 2 =ins(5,s),op 1 =ins(2,f)) = ins(6,s) = op 2 . The insertion position of op 2 is incremented since op 1 has inserted an f before s in state "efect". After the execution of op 2 , the state of site 1 becomes "effects". On the contrary, when op 1 is received on site 2 , the transformation does not modify op 1 's parameters since f is inserted before s. Thus, op 1 is executed as-is and the state of site 2 is "effects". On this scenario, OT approach has ensured that both copies converge to the same value.

The OT approach distinguishes two main components: an integration algorithm and a set of transformation functions. The integration algorithm is in charge of reception, diffusion and execution of operations. When necessary, it calls transformation functions. This algorithm does not depend on type of replicated data. The transformation functions merge concurrent modifications by serializing two concurrent operations. These functions are specific to a particular type of replicated data such as string of characters, XML documents, calendars or file system.

OT approach aims to achieve convergence of copies. a) Convergence: As every optimistic replication algorithm, OT approach aims to ensure eventual consistency. This means that if no updates are performed for a long period of time, all updates will eventually propagate through the system and all the copies will converge towards a same value. In other words, when the system is idle (no operation in pipes), all copies are identical.

To ensure convergence, it has been proved [START_REF] Suleiman | Concurrent Operations in a Distributed and Mobile Collaborative Environment[END_REF] that the underlying transformation functions must satisfy two properties: Definition The TP 1 property defines a state equivalence. The state generated by the execution of op 1 followed by T (op 2 , op 1 ) must be the same as the state generated by the execution of op 2 followed by T (op 1 , op 2 ):

op 1 •T (op 2 , op 1 ) ≡ op 2 •T (op 1 , op 2 )
Definition The TP 2 property ensures that the transformation of an operation regarding a sequence of concurrent operations does not depend on the order in which operations of this sequence were transformed:

T (op 3 , op 1 • T (op 2 , op 1 )) = T (op 3 , op 2 •T (op 1 , op 2 ))
The operational transformation approach could be used to design a reconciliation framework able to reconciliate divergent copies of any type of data. In order to build such a framework, the following task have to be completed. First, an integration algorithm must be chosen ; regarding this algorithm, TP 2 property may be required on underlying transformation functions. Second, operations which could be performed on shared data types must be defined. Finally, the required transformation functions for all combination of operations have to be provided.

III. MOTIVATING EXAMPLE

Now imagine that three authors (U serU , U serV and U serW ) working together to modify the following XML document. This document has the following constraints (DTD):

• C1 (Uniqueness): The element TITLE must be unique.

• C2 (Existence): The element A must be followed by the element B i.e. it is not possible to have A without B (A;B). • C3 (Alternative): There is an alternative between element C and D (C or D and not both of them) Each author has his own replica of the initial document. The author U serU starts to modify his copy. We simplified the operation profiles in order to improve readability. U serU produces the following sequence of operations:

1) op 1 = update(C2, CU 2);. The update operation is interpreted as delete(C1) followed by insert(CU 2) af ter B2.

2) op 2 = update(T 1, T U 1) 3) op 3 = Insert(< A > A1 < /A >) bef ore B1 userU produces the following valid XML document: In order to integrate the different modifications, we need to apply a reconciliation algorithm. We used the SO6 tool [START_REF] Oster | Supporting Collaborative Writing of XML Documents[END_REF] with XML transformation functions. SO6 is the only available merging tool that ensures the convergence of the replicas. By using SO6, all sites eventually converge to the following XML document: The merged document does not respect the DTD:

<?xml
<?xml
• It violates the constraint C1. For instance, there are two titles in the first element DATA. • It violates also the constraint C2, there is an element A without B .

• Finally, it violates the alternative constraint C3 in the second element DATA. As we can see, in spite that individual modifications of authors respect the DTD the merged document does not. The resulting document will be considered as corrupted when trying to open it. To overcome this problem, we can imagine three solutions.

1) The first solution is to combine OT with DTD validation. This means, that the merging algorithm has to take into account constraints violation. Therefore, we have to write and prove transformation functions. This means, for example, for a DTD with 20 different elements we have to write 400 transformation functions. This is not realistic.

2) The second solution is to use the approach proposed by IceCube [START_REF] Kermarrec | The IceCube Approach to the Reconciliation of Divergent Replicas[END_REF]. IceCube tries to find an ordering (Scheduling) that respects the constraints. There are two kind of constraints Static constraints and dynamic constraints.

The first one restricts a priori the possible orderings between two actions. For example, an update operation on an object x must precede a delete operation of that object. Therefore, the order Update before Delete is safe (acceptable), however, delete followed by update is unsafe. A dynamic constraint is a precondition of the operation, it is used to check that the state of the object universe at simulation time is compatible with the one observed during isolated execution. The major drawbacks of IceCube are the risk of combinatorial explosion, the annulation of some operations and finally how to transform DTD to IceCube constraints 3) The last solution is to repair violation after the merge.

The idea of repairing constraints after violations is largely studied in active database domains [START_REF] Ceri | Automatic generation of production rules for integrity maintenance[END_REF], [START_REF] Gertz | An extensible framework for repairing constraint violations[END_REF], [START_REF] Nentwich | xlinkit: a consistency checking and smart link generation service[END_REF], [START_REF] Nentwich | Consistency management with repair actions[END_REF]. While in traditional database, a transaction that violates constraints is rolled-back. In active database, database management system reacts autonomously to inconsistencies, by triggering a set of repairs actions capable of progressive elimination of constraints violation until a consistent state is reached. Final state can be chosen within a subspace of states as compliant as possible to the original intention of the transaction. We propose a repairing approach for collaborative writing of XML documents. We believe that repairing approach is a reasonable solution to reestablish the consistency of the merged document.

IV. AUTOMATIC REPAIRING

We studied most repairing algorithms in active databases, most of them are not applicable for XML documents, they are adapted for relational data model [START_REF] Razafimahatratra | Rparation automatique dans un environnement collaboratif[END_REF]. Only the work proposed by Xlinkit [START_REF] Nentwich | xlinkit: a consistency checking and smart link generation service[END_REF], [START_REF] Nentwich | Consistency management with repair actions[END_REF] is applicable.

Xlinkit is a framework for maintaining consistency of distributed XML documents. It defines constraints language based on first order logic with XPath syntax. The framework proposes a validation engine for XML document in addition of repairing algorithm.

In the following sections, we show how we can adapt Xlinkit to our context.

A. DTD constraints

In this section, we show how we can use Xlinkit constraints language to describe the DTD constraints of the collaborative writing example in the section III.

The uniqueness constraint (C1: only one title) can be written as: ∀d ∈ "/example/data"((∃t ∈ "$d/title") ∧ (∀x ∈ "$d/title"(∀y ∈ "$d/title"("$x = $y ))))

The sequence constraint (C2: A ; B) can be written as: ∀d ∈ "/example/data"((∀x ∈ "$d/A"(∃y ∈ "d/B"))

Finally, the alternative constraint( C3: C or D) can be written as: ∀x ∈ "/example/data"((∀y ∈ $x/C (¬(∃z ∈ "$x/D"))

The constraints language is based on first order logic with XPath syntax in order to select sets of elements [START_REF] Nentwich | xlinkit: a consistency checking and smart link generation service[END_REF].

B. Repairing algorithm

The repairing algorithm is a kind of rewriting process [START_REF] Nentwich | Consistency management with repair actions[END_REF]. According to the constraint expression, some rewriting rules can be applied. Figure 1 shows some of these rules [START_REF] Nentwich | Consistency management with repair actions[END_REF].

We describe these rules by applying them to the motivating example. We examine the violation of the existence constraint. Xlinkit generates an inconsistency link to element /EXAM P LE/DAT A [START_REF] Deltaxml | managing change in an xml environment[END_REF]: there is an A element without B.

The constraint C2 is proceeded as follows: As we can see the algorithm proposes a set of repairing actions and it is up to the user to choose one. In the context of collaborative writing, the first solution is not acceptable since it implies the deletion of the whole element i.e. lost of many data. The second solution proposes to locate the deletion to the only concerned element i.e. deletes the element A for which there is no B. Again with this solution there is a lost of data. The final solution proposes to add a new element, this is the best one since there is no loose of data.

Xlinkit proposes a set of repairing actions. The user has to choose one. In collaborative writing this not an acceptable solution since different users can choose different repairing actions. For example, U serU can choose the second repairing action while U serW chooses the third one. In this case, there is a divergence, the synchronization algorithm is restarted, then the repairing algorithm restarted. There is an infinite loop of synchronization and repairing cycles.

A possible solution to this problem is to let the system chooses a repairing action in a deterministic way. This allows to ensures that all sites will apply the same repairing actions and therefore they will converge toward the same valid XML document.

We developed a system that set priorities to repairing actions. Each action is decorated with a priority. Action with priority 1 is preferable to action with priority -1. We attribute the highest priority to the Insertion action. It has a priority 1. A deletion action has a priority < 1. To establish an order among deletion actions, we propose to use the size of the deleted data. If we apply priorities rules to the previous example, the system will generates:

1) {{ delete 1 priority -1 }} 2) {{ delete Direct locater(x) priority 0 }} 3) {{ add Lookup $d/B Direct locater(x) priority 1 }} The third action has the highest priority. Therefore, it will be chosen as a repairing action. All sites will have an element B with a default value as shown below. In the same way, the function D i is recursively applied to repair uniqueness and alternative constraints.

V. CONCLUSION

Collaborative writing is the process by which more than one author share opinions and contribute to the content of a document. Collaborative writing is standard practice in technical and scientific settings; some examples include research papers, software development, proposals for funding and user manuals.

In this paper, we address the collaborative writing of XML document. While some XML reconciliation algorithms ensure convergence, they do not ensure any guarantee about the validation of the DTD.

We propose an automatic repairing approach to reestablish consistency. We show the proposed solution through an example. This solution is a first step to handle inconsistency of merged XML document. We validated the approach through an implementation.

As a perspective of this work, we plan to use the group 'undo' operation [START_REF] Weiss | Compensation in collaborative editing[END_REF] as repairing action. Currently, repairing algorithms only consider insert, delete or update operation. We believe that a group undo can improve significantly the quality of reparation.

1 )

 1 function D i generates delete operation for the inconsistent d; {{ delete 1}} where 1 is the locater number returned by Xlinkit. 2) It then calls D i recursively to process the universal quantifier :D i [∀x ∈ "$d/A"]. 3) D i generates delete operation {{ delete Direct locater(x)}}. And finally the existence quantifier is proceed. This generates the action: {{ add Lookup $d/B Direct locater(x)}}. The algorithm proposes three possible repairing actions (cf figure??).

Fig. 2 .

 2 Fig. 2. Possible repairing actions

Fig. 1 .

 1 Fig. 1. Rules for repairing

version="1.0" encoding="UTF-8"?>

	<EXAMPLE> <DATA> <TITLE>TU1</TITLE> <A>A1</A> <B>B1</B> </DATA> <DATA> <TITLE>T2</TITLE> <A>A2</A> <B>B2</B> <C>CU2</C> </DATA> </EXAMPLE> Concurrently, the author U serV produces the following operations: 1) op <?xml version="1.0" encoding="UTF-8"?> <EXAMPLE> <DATA> <TITLE>TV1</TITLE> </DATA> <DATA> <TITLE>T2</TITLE> <A>A2</A> <B>B2</B> <C>C2</C> </DATA> </EXAMPLE> At the same time, the third author U serW produces the following operations: 1) op 6 = delete(C2) 2) op 7 = insert(< D > DW 2 < /D >) af ter B2 U serW produces the following valid XML document: <?xml version="1.0" encoding="UTF-8"?> <EXAMPLE> <DATA> <TITLE>T1</TITLE> <B>B1</B> </DATA> <DATA> <TITLE>T2</TITLE> <A>A2</A> <B>B2</B> <D>DW2</D> </DATA> </EXAMPLE>

4 = update(T 1, T V 1)

2) op 5 = delete(B1) So U serV is producing the following valid XML document:

version="1.0" encoding="UTF-8"?>

	<EXAMPLE>
	<DATA>
	<TITLE>TU1</TITLE>
	<TITLE>TV1</TITLE>
	<A>A1</A>
	</DATA>
	<DATA>
	<TITLE>T2</TITLE>
	<A>A2</A>
	<B>B2</B>
	<C>CU2</C>
	<D>DW2</D>
	</DATA>
	</EXAMPLE>


Rachel Griffin

The Politics of Algorithmic Censorship: Automated Moderation and its Regulation

As social media have become vital channels for all kinds of political communication and cultural production, the regulation of social media content has also become a central arena for the contemporary politics of censorship. This chapter offers an overview of the functioning of algorithmic content moderation: the automated monitoring and filtering of user-generated content by online platforms, guided by their legal obligations, in-house content policies and commercial objectives. Given the concentrated power of a small number of multinational social media companies, many scholars consider that their de facto power to regulate users' speech represents a new and concerning form of private censorship. This power is also routinely leveraged by state authorities, through both formal legal obligations and various forms of more informal cooperation and influence.

In this context, the chapter argues that public and private censorship cannot be clearly distinguished. Ultimately, all moderation of user content plays out against the backdrop of complex entanglements between public institutions and private companies. As such, moderation is alwaysmore or less directlyinfluenced by public policy, but also by platform companies' commercial objectives. To illustrate these dynamics, the chapter uses a well-publicised case in which Meta (owner of Facebook and Instagram) was revealed to be censoring drill music videos following informal requests from UK police. This case is used to illustrate some of the key political concerns raised by contemporary forms of algorithmic censorshipnotably including a lack of transparency and accountability mechanisms governing state intervention, and the replication in algorithmic decision-making software of institutional racism and other historic biases against marginalised groups. The chapter concludes by discussing how algorithmic moderation on social media is likely to develop in the future, highlighting some recent regulatory and technological developments which suggest that state and corporate control of online media will continue to intensify.

Introduction

London's Metropolitan Police has an entire unit dedicated to a music genre. Probably to no one's great surprise, it is one primarily popular among young, working-class people of colour 1 . « Project Alpha », which in 2022 employed around 30 officers 2 , monitors UK drill music, a rap subgenre popular in deprived neighbourhoods of London, whose lyrics often reference local gang rivalries and real or imagined violent incidents. 3 Due to its alleged potential to encourage violent crime, drill music has been a frequent target of police intervention. This ranges from the use of drill lyrics in criminal trials as evidence of gang affiliations and violent behaviour, to interventions by armed police in the filming of music videos, and even injunctions banning artists from performing certain songs 4 . However, much of Project Alpha's activity also plays out online. Officers monitor the social media accounts of drill artists perceived as dangerous, and request that platforms remove tracks they believe could encourage violence 5 .

In 2022, this practice was brought to international attention as the subject of a decision by the Oversight Board 6an operationally independent body established by Meta, parent company of Facebook, Instagram and WhatsApp, to advise on how the company regulates users' social media content 7 . At the Met's request, Instagram had removed a music video by drill artist Chinx (OS). The Board concluded that this inadequately respected users' freedom of expression, especially in light of the track's artistic nature, the tenuous evidence that it constituted a threat of violence, and the lack of transparency around Meta's cooperation with police 8 . Perhaps more interestingly 9 , the Board's decision also described Meta's decisionmaking process in detail. It revealed that Meta had originally removed the video in response to an informal email from the Met, and that 164 similar posts were subsequently removed, mostly through an automated system used to automatically scan for copies of previouslyremoved content. This was one of 992 Met Police requests for social media platforms to remove music content between June 2021 and May 2022, 10 100% of which related to drill music 11 . Platforms had complied in 89% of cases 12 .

As this case illustrates, social media have become a key arena for the contemporary politics of censorship. Cultural production, media consumption and interpersonal communication are increasingly intermediated by a relatively small number of dominant online platforms 13 , which now represent key points of control over media and communications 14 . Content moderationprocesses by which platforms identify and remove, or otherwise address, content deemed illegal, harmful or undesirableis widely recognised as essential to create 1 For background on the long history of institutional racism in the Met see TRILLING 2023 2 ECONOMIST 2022 3 JALLOH 2023 4 FATSIS 2019;KEENAN 2021;[START_REF] Quinn | Introduction to special issue: Prosecuting and Policing Rap[END_REF] ; JALLOH 2023 5 ECONOMIST 2022 6 OVERSIGHT BOARD 2022A 7 KLONICK 2020 8 OVERSIGHT BOARD 2022A 9 Many commentators have questioned the Board's capacities to effectively hold Meta accountable, in particular because its remit and goals are set by Meta. However, one clearly positive outcome is its access to and publication of otherwise non-public information about how Meta's moderation systems work. See DOUEK 2024 10 OVERSIGHT BOARD 2022B 11 Ibid. 12 Ibid. 13 SRNICEK 2016; POELL ET AL., 2022 usable and safe online spaces 15 . Yet many scholars and activists are concerned about the power of the multinational conglomerates that control today's dominant platformsincluding social media, but also other powerful intermediaries like search engines and app storesto regulate media and communications in accordance with their own commercial interests 16 . As Project Alpha illustrates, these private systems of speech regulation can also be leveraged by state authorities for their own purposes 17 .

These concerns are complicated further by platforms' increasing capacities to moderate content automatically, rather than relying on human workers to manually review content 18 . Automated monitoring enables far more effective and pervasive corporate control over what can be said online: all user content and communications can be scanned before they are uploaded, and undesirable behaviour pre-empted before it even takes place 19 . It creates new possibilities for discriminatory censorship 20 , given that algorithmic decision-making software has well-studied tendencies to replicate, intensify and scale up existing patterns of social inequality 21 . As this chapter will show, these new, efficient tools of surveillance and control also offer new possibilities for state interference.

This chapter offers a brief introduction to algorithmic content moderation as a 21 st -century channel for censorship. This term has most traditionally been understood as referring to regulation of speech by state authorities 22 . However, in an age where companies like Meta, Google and Apple effectively regulate billions of users, and control essential chokepoints to reach public attention, many scholars and activists consider that they exercise power akin to governments and that their moderation practices raise similar normative concerns 23 . Importantly, however, as the chapter will show, presenting corporate content moderation as comparable to but distinct from state censorship does not accurately reflect the institutional reality 24 . Ultimately, commercial content moderation always takes place « in the shadow of the law » 25 , influenced though not wholly determined by state policies 26 . There is a spectrum of state involvement encompassing direct legal mandates to censor specific content, informal requests like Project Alpha's, and various more diffuse and indirect forms of influence.

After introducing the basic functioning of algorithmic moderation, the chapter explores some key normative concerns it raises 27 . The Oversight Board's decision on the Chinx (OS) drill video is used as a case study which usefully illustrates not only some typical technical and institutional processes involved in content moderation, but also the complex entanglements of state and corporate power that are at play. The chapter concludes by discussing the potential future outlook for algorithmic censorship, which in an era of « platform capitalism » 28 will surely remain a key area of political intervention and contestation.

Algorithmic content moderation

Content moderation has historically been understood as referring to the policies and processes by which platforms decide whether to remove user content 29 . As platforms' moderation operations have expanded in size and complexity, alongside the development of a professional « trust and safety » community 30 and the burgeoning academic field of « platform governance » 31 , moderation is now increasingly understood as encompassing a range of other possible interventions to address harmful or undesirable content. This can include for example labelling possible misinformation with fact-checking information, recommending content to fewer users, or making content and accounts harder to find without removing them entirely 32 . In principle these interventions are based on public « community guidelines » setting out what users are or are not allowed to postthough in practice such guidelines offer a general, incomplete, and often actively misleading picture of the internal rulebooks, informal practices and selective standards that actually determine how content is moderated 33 .

Historically, moderation was primarily undertaken by large teams of workers, typically employed through outsourcing companies, often in Global South countries with lower labour costs. Their poor working conditions and the highly stressful nature of their work have been the subject of extensive academic research and critique 34 , as well as several lawsuits 35 . Their work remains central to platforms' operations. However, due to several factorsincluding technological advances, regulatory pressures to moderate content faster and more comprehensively, and commercial pressures to do so in a cost-effective and scalable waymoderation is also increasingly now undertaken through automated filtering software 36 .

Two main forms of algorithmic moderation can be distinguished: hash-matching and AI classifiers. Hash-matching is a cryptographic tool used to automatically remove exact or nearexact copies of content previously identified by moderators. It works by creating a unique « hash code » for a given file, typically an image or video; later, (near-)identical copies will produce the same code, so they can efficiently be matched to databases of previouslyremoved content 37 . Hash-matching tools have prominently been used to address child sexual abuse material and terrorism-related content. In both cases, platforms have commercial and regulatory incentives to cooperate to effectively police such content: they are high priorities for policymakers and law enforcement, they bring significant reputational and commercial risks, and it is widely accepted that they should never be allowed on any platform. This has 28 led to the development of industry-wide databases, which mean that content removed by one platform can be automatically identified and removed across all participating platforms 38 .

Platforms have also relied on hash matching for other content types, including content that is not necessarily illegal, but merely banned under their in-house policiesas happened in the Chinx (OS) case. The Oversight Board's decision revealed that after the video was initially removed in response to the Met's email, it was added to Meta's « Media Matching Service », an in-house tool which stores hash codes for content removed under its rules on « dangerous individuals and organisations », abuse and nudity 39 . In effect, once a human moderator decides to remove content in a particular instance, hash-matching allows this decision to be « scaled up » across the platform, enforcing the ban on that image or video in a continuous and comprehensive way that would not be possible when relying only on human review.

« Fingerprinting » technologies function similarly to hash-matching, in that they scan content and match it to databases of known files, but are designed to be more sensitive to variations and modifications 40 . These are very widely used to enforce music copyrights. Large platforms have long implemented such systems under commercial agreements with major music labels, which are important business partners. 41 They are now also legally obliged to do so under the EU's 2019 Copyright Directive 42 . Often, instead of removing content that makes use of registered copyright works, fingerprinting systems are used to redirect advertising revenue from the user posting the content to the copyright owner. This practice has attracted criticism, since it may involve transformative uses of copyright material (e.g. remixes or reviews) which do not infringe copyright and represent a user's own copyright-protected creative work 43 . Hash-matching and fingerprinting systems which simply look for copies of existing files are incapable of assessing and respecting these exceptions from copyright protection.

In contrast to hash-matching and fingerprinting, AI classifiers are used to analyse previouslyunknown content. AI tools are built on training datasets from which they can learn patterns to apply to new content: for example, learning that certain words and linguistic features are associated with hate speech, or certain visual patterns with nudity 44 . Smaller platforms often outsource software development, purchasing standardised moderation software like Google's Perspective 45 , while larger platforms with more technical resources are more likely to build or customise their own tools. To do so, they may rely on existing industry-standard datasets, like the ImageNet corpus of labelled pictures 46 . Alternatively, they can build in-house tools or fine-tune existing models using datasets of their own moderators' previous decisions: for example, a platform could fine-tune a general-purpose language model like OpenAI's GPT series (the basis for the popular chatbot programme ChatGPT) to detect hate speech, by feeding it examples of text that was previously labelled as hate speech by moderators 47 .

As this suggests, algorithmic moderation does not operate fully autonomously from human intervention. Hash-matching databases and classifiers are built to reproduce previous 38 Ibid. 39 49 . Even the most advanced AI tools have limited abilities to reliably assess the meaning of content in context 50 , so a significant workforce of moderators remains necessary to review and correct automated content classifications 51 , as well as providing fresh training data to improve and evaluate AI classifiers and to update them as platforms' rules and policies evolve 52 . And of course, all moderation tools are designed and maintained by platforms' software engineers, to enforce policies written and updated by « trust and safety » staff. Algorithmic moderation decisions are thus best understood as coproduced by networks of technologies and human actors, rather than entirely automated 53 .

Algorithmic moderation as private censorship

Although they extend and build on manual review and monitoring, rather than replacing it, algorithmic moderation tools afford new possibilities that were not technically or operationally feasible when relying primarily on manual moderation. Law and technology scholar Jennifer Cobbe argues that two key characteristics of « algorithmic censorship » raise particular normative concerns. First, it allows moderation to operate more comprehensively and at a larger scale. Human moderators could only ever review a tiny subset of the content posted on platforms with millions or billions of userstypically focusing on content that other users have reported as objectionable 54 but every single post can be scanned at the point of upload by AI classifiers and hash-matching software. Second, as a consequence, platforms can take a « more active, interventionist approach » to moderation, blocking content before it is even posted rather than waiting for it to garner a reaction from other users 55 . Moreover, for Cobbe, pervasive algorithmic filtering implies not only more comprehensive and effective corporate control over online communications, but a qualitative shift in the nature of this control. Content deemed objectionable will not merely be reviewed and sanctioned for non-compliance with rules: it will be technically impossible to post 56 . For many researchers, activists and civil society organisations, this new form of corporate speech regulation represents a new, crucial arena for political struggles over censorship and freedom of expression 57 . Other scholars place more emphasis on the continuities between the power of today's corporate social media, and the outright censorship and more subtle corporate influence that have always characterised commercial media 58 . However, the increasingly pervasive and opaque forms of control enabled by algorithmic moderation 59 , and the unprecedented concentration of this control with a small number of global corporationsmost prominently including Meta, Google (which owns YouTube) and TikTok, all with billions-strong user bases across multiple servicesare widely considered to pose new and concerning threats to freedom of expression 60 .

Today's dominant social media platforms depend on advertiser funding, and this creates incentives to filter and regulate user speech in ways that are familiar from older advertiserfunded media systems 61with concerning implications for the freedom and diversity of the public sphere. For example, platforms' policies on hate speech, abuse and harassment are often operationalised using algorithmic tools applying industry-standard metrics of « toxicity ». Yet this metric is technically defined not according to legal or other normative definitions of abusive behaviour, but as speech likely to make other people leave a conversation 62 . Thus, in a subtle redefinition, policies which nominally aim to protect vulnerable users are operationalised using tools that centre platforms' interests: keeping people engaged with conversations and producing more content and ad revenue.

Moderation policies and practices are also significantly influenced by advertisers' concerns about « brand safety », meaning that running ads alongside controversial or offensive content could create negative associations with their brands 63 . For example, almost all major platforms completely ban not only pornography, but any kind of nudity and content considered sexually suggestive, in large part because most advertisers do not consider it « brand safe ». This has often been applied indiscriminately to content that references LGBTQIA+ identitieswhether deliberately, because they are considered potentially offensive to the broad « mainstream » audiences that most advertisers wish to target, or inadvertently, because algorithmic moderation tools often associate keywords like « lesbian » and « bisexual » with their use in commercial porn aimed at heterosexual audiences 64 . Considering Meta's commercial incentives as an advertising company sheds new light on its treatment of UK drill music. Music videos made by popular celebrities with « mainstream » appeal are lucrative vehicles for advertising and drivers of user engagement. For these reasons, researchers have observed that they often seem to escape platforms' otherwise draconian regulation of sexually suggestive content. 65 For example, as highlighted by sociologist and pole dancing influencer Carolina Are, major platforms have not taken action against the music video for Grammy-award-winning rapper Cardi B's single « Money », which features nudity and pole dancing, even though much less suggestive pole dancing content by less well-known users is routinely removed. 66 In contrast, UK drill videos represent a relatively niche rap genre popular among young working-class people of colour, who are not a particularly lucrative audience for advertisers 67 . References to crime, violence, and profanity are likely to be deemed offensive and « brand unsafe » 68 within moderation systems that have been described as implementing a white « respectability politics » 69 . Videos like Chinx (OS)'s thus offer little value to today's dominant advertising-funded platforms. 60 More broadly, « algorithmic bias »the tendency of algorithmic decision-making tools to replicate and exacerbate unequal treatment of already-marginalised social groupsis a pervasive problem in content moderation, as in many other fields where algorithmic decisionmaking tools are used 70 . Moderation systems are trained on data from historical decisionsmeaning, typically, snap judgments made under intense time pressure by low-paid and poorlytreated workers, which are likely to be influenced by conscious and unconscious bias 71 . They are built to reflect the worldviews of predominantly white, male, Western executives and developers 72 , and the commercial priorities of companies whose advertising-based business models incentivise them to focus on suppressing content most likely to be deemed « objectionable » by advertisers and mainstream Western audiences, rather than the content most harmful to their global user bases 73 . Inevitably, then, these systems reproduce existing stereotypes, biases and social inequalities.

Human moderators' decisions are also undoubtedly inflected by such prejudices and stereotypes, as are the rules and policies they apply. However, algorithmic moderation does not only replicate biases in its training data, but can also amplify them 74 , For example, commenting on the Chinx (OS) decision, leading platform regulation scholar Daphne Keller noted that moderators working under intense time pressure are particularly likely to be influenced by (un)conscious biases towards seeing Black men as violent or threatening 75 . In this case, the use of hash-matching means any such bias did not just play out in decisions about individual postsit led to an immediate blanket ban on sharing Chinx (OS)'s music video, anywhere on Instagram. In turn, AI classifiers trained on past moderation decisions like these will « learn » that videos containing groups of Black men and other lyrical, musical and visual tropes associated with drill music are associated with violence and threats. Such biases will thus be intensified and durably encoded into the architecture of the platform.

Algorithmic moderation and state censorship

Algorithmic moderation thus affords new possibilities for corporations to control online speech in line with their commercial objectives. However, it also represents a channel for the exercise of state power. Influencing how powerful platforms regulate their userswhat legal scholar Jack Balkin has termed « new school speech regulation »offers new possibilities for governments to pursue longstanding policy objectives 76 . These range from surveillance and control of speech deemed extremist or politically subversive 77 , to responding to « moral panics » over media content regarded as dangerous to children 78 . State censorship of online media may be particularly obvious in authoritarian countries like China, where platforms are subject to explicit and exacting requirements to censor speech deemed politically objectionable by state authorities 79 . Howeveras the Met Police's Project Alpha illustrates - policymakers and law enforcement agencies in more democratic countries have also routinely leveraged the power of dominant social media platforms to regulate online speech 80 . This has often involved hard regulation. The EU's 2022 Digital Services Act serves as a leading example 81 of a legislative framework which aims simultaneously to ensure more comprehensive and accurate moderation of illegal or harmful content, and to protect users' freedom of expression 82 . With the latter goal in mind, platforms are required to transparently publish their moderation policies 83 , and any time they moderate an item of contentwhich includes interventions short of removing it entirely, like making it less visible to other usersthey must inform the user involved, explaining the reason for the decision and whether it involved algorithmic moderation tools or reporting by other users 84 . Users are entitled to demand a review of the decision, and to appeal to out-of-court dispute resolution institutions 85 . These new protections for users, accompanied by provisions stipulating that content moderation should be objective and proportionate way and should have regard to users' fundamental rights, are intended to avoid arbitrary or discriminatory censorship.

However, scholars have raised doubts about the capacities of these vague, aspirational principles and individualistic procedural rights to address the pervasive and opaque forms of corporate control enabled by algorithmic moderation 86 . And at the same time, other DSA provisions and EU measures (such as the 2019 Copyright Directive and 2021 Terrorist Content Regulation) require or strongly incentivise platforms to expand automated moderation as a way of addressing issues that governments regard as policy priorities, such as extremist content 87 . Overall, the DSA could be understood as a kind of double movement, in which the EU encourages the continued expansion, standardisation and refinement of algorithmic moderation systems, while employing fundamental rights and other safeguards to prevent some of the most egregious forms of arbitrary or discriminatory censorship that they might produceultimately helping to institutionalise and legitimise them.

State intervention in platform governance has also frequently involved more indirect or informal channels, which continue to coexist with formal regulation. This can take several forms. As in the example of the Met's Project Alpha, public authorities may wish to effect the removal of specific items of content they deem illegal or (potentially) harmful. Courts or other competent public authorities can order platforms to remove illegal content (a process which is also formalised and subjected to procedural safeguards in the DSA, though the relevant substantive and procedural rules will vary by country 88 ). Alternatively, public authorities may informally request, rather than ordering, content removaleither reporting content using the same interfaces available to all platform users, or using informal channels such as emailing personal contacts, as in the Chinx (OS) case. Designated police teams now exist in many countries (including the UK, Israel and several EU member states, as well as Europol) that focus on monitoring and reporting specific types of social media content 89 : often terrorism- related, but also including areas such as undocumented migration 90 and Covid misinformation 91 , and of course in the case of the UK rap music.

Importantly, these requests do not generally need to clearly demonstrate the content's illegality. They may only claim that the content violates the platforms' in-house content policies, and suggest that the platform may therefore wish to remove it. 92 As illustrated by the Oversight Board's freedom of information requestwhich revealed that 89% of Project Alpha's drill-related requests resulted in removal 93these suggestions are generally likely to be followed, even if they are not particularly well substantiated. Platforms not only face legal risks if they are later determined to have continued hosting illegal content after it was brought to their attention, 94 but generally benefit from maintaining good relationships with law enforcement and politicians and being seen to take swift action on harmful content.

As well as interventions targeting specific items of content, state authorities can use various forms of soft power and informal influence to shape platforms' general moderation policies and practices. For example, policymakers may encourage and shape the content of nominally voluntary self-regulatory efforts by platforms. Examples include the « Christchurch Call » on terrorist content spearheaded by then-New Zealand Prime Minister Jacinda Ardern and French President Emmanuel Macron 95 , or the EU's codes on hate speech and disinformation, which involved active participation and guidance from the European Commission 96 . Policymakers may also communicate informally with platforms or publicly demand that they do more to address harmful content, aiming to influence their policies using reputational pressure, personal relationships, and (more or less implicit) threats of future, harder regulation 97 . For example, during urban riots protesting racist police violence in summer 2023, French government ministers both met privately with executives from major platforms, and made several public statements to the effect that platforms should do more to suppress content encouraging violence or property damage 98 .

Finally, even in the absence of active state intervention on particular issues, all commercial content moderation arguably involves some level of state influence operating in the background. Due to several factorsincluding reputational pressures, vague legal obligations which accord significant discretion to regulators (like the DSA's requirements to take vaguely specified actions against « systemic risks » 99 ), and the ever-present threat of stricter regulation if policymakers are unsatisfied -« anticipatory obedience » which ensures moderation practices are broadly aligned with government objectives is generally a good commercial strategy, at least in valuable markets like the EU and US 100 . Platforms' policy staff often have close relationships with politicians, and indeed this may be a key reason they are hired 101 . 90 These power dynamics and ongoing relationships with policymakers are always operating in the background to influence moderation practices. Policies will be written with legal risks and government relations in mind, and may draw on government sources: for example, Meta's list of « dangerous individuals and organisations », banned and policed through algorithmic moderation tools including its Media Matching Service, is drawn from the US government's list of banned terrorist organisations 102 . State intervention at a given point in time will also continue to exert an influence on future moderation practices, through algorithmic tools which reproduce or learn from past decisions.

Overall, through all these coexisting and interrelated channels of influence, it is clear that the « private ordering » systems through which platform companies regulate their users are pervasively shaped by the influence of state actors, and offer powerful new channels for state censorship. Algorithmic moderation, in particular, enables far more granular, pervasive and pre-emptive interventions than traditional legal sanctions. Censorship regimes that subject publications to controls before they can be publishedas is now the case for all social media content subject to state-mandated algorithmic filteringhave traditionally been regarded as particularly threatening to freedom of expression, associated with authoritarian states and historic systems of media regulation that have now been largely abandoned by Western democracies 103 . Informally pressuring private actors to regulate content « voluntarily »described as « laundering » of state censorship by legal scholar Hannah Bloch-Wehba 104also evades many of the legal safeguards and other accountability mechanisms, such as public scrutiny, that would apply to official state interventions 105 .

It would go too far to say that any influence or cooperation between state actors and platforms is undesirable: it may often be justified to prevent serious illegal activity, such as the dissemination of child sexual abuse material, or to hold platforms accountable to publicinterest goals, like preventing abuse and harassment. At the same time, however, the various mechanisms of state influence identified above raise serious concerns about the suppression of political dissent and unpopular minorities. This is not only due to deliberate, targeted abuses of power by state authorities, but also because of inevitable issues of algorithmic bias and discrimination, as discussed in the previous section. Another particularly concerning aspect is the often informal, covert and unaccountable nature of these interventions. For example, the monitoring and reporting practices of the Israeli police's « Cyber Unit » were recently challenged in a lawsuit that made it all the way to the Supreme Court, but which was ultimately dismissed on the basis that the claimants could not definitively prove that any particular moderation decision had involved state intervention. 106 Project Alpha offers a vivid example of how, in the absence of strong accountability mechanisms, state power to censor online speech is likely to be abused. The Met Police's targeting of drill shows continuities with longer histories of police racism and tendencies to stigmatise « young urban black menand the forms of culture that appear tied to this population -[as] a threat to the civic mainstream » 107 despite the very tenuous evidence for links between drill music and violent crime 108 . This has real consequences for the freedom of artistic expression of the users involved, and the communities to which they belong. 109 An indepth journalistic investigation of the UK drill scene by Vice argues that police interventions have done « an exceptional job of eradicating the chances for aspiring rappers to transition toward full-time music: Debut singles and sophomore offerings disappear from YouTube, with potential up-and-comers' careers obliterated before they begin. » 110 State censorship, « laundered » through corporate moderation systems which evaluate drill music as having little commercial value, suppresses the creativity, self-expression and career aspirations of young people to whom UK society otherwise offers few opportunities and resources 111 .

Monitoring of social media content by police and private companies also feeds into broader systems of surveillance and social control 112 . In the UK, drill music content has often been used in trials as evidence of gang associations and criminal activity. Police and prosecutors use rap lyrics and videos to evoke negative stereotypes about Black culture, reinforcing racist disparities within the justice system 113 . For sociologist Lambros Fatsis, stigmatising and policing drill music helps uphold the UK's broader architecture of racial and class inequality, as « the neoliberal state accuses residents for the deterioration in their surroundings; often attributing such decline to a lack of civility and a cultural propensity for gang violence. » 114 Similar alignments between platform policies and state ideologies can be observed in other contexts. For example, the use of state terrorism blocklists to identify « dangerous individuals and organisations » means that platforms' moderation systems replicate the Islamophobic policies of state counterterrorism agencies 115 . Experts have also linked platforms' close cooperation with Israeli authorities, including the Cyber Unit, to the repeated and ongoing suppression of social media content relating to pro-Palestinian political activism 116 .

Algorithmic censorship: the future outlook

At this point, algorithmic moderation is an embedded and essential feature of online environments. It is necessary to create usable online spaces that are not quickly overwhelmed with spam and genuinely harmful content, such as the abuse, harassment and hate speech that have historically denied members of marginalised social groups equal access to public spaces 117 . It is also integral to the business models of corporate platforms that need to quickly suppress « objectionable » content, in order to keep users engaged and prove their « brand safety » to advertisers 118 . And finally, it offers state authorities powerful new tools to monitor and control types of speech deemed threateningtools they are unlikely to give up. Looking ahead, it seems likely that the role of algorithmic censorship in modulating online speech will only increase. Here, three important trends can be tentatively identified.

First, AI moderation tools are improving fast. Historically, such tools had very limited ability to analyse the meaning of speech in context; to understand nuances or implicit meanings evident to human audiences; or to analyse mixed media content like videos or text-and-image memes119 . However, at the time of writing in mid-2023, we are seeing rapid advances in the development and commercialisation of « large language models » (LLMs), like those behind ChatGPT. Trained on vast text corpora, they have markedly improved abilities to analyse and respond to text input, and can be effectively « fine-tuned » for specialist tasks (like enforcing specific content policies) using relatively little additional data 120 . Multimodal LLMs which can analyse visual and mixed media content are also seeing rapid improvements 121 . Such systems' capabilities should not be overstated: their performance is far worse outside of English and a few other « high-resource languages » 122 , and they are still nowhere close to achieving human-level comprehension 123 . In any case, the inherent indeterminacy and contestability of content policieswhat is « hateful » or « sexually suggestive » is inevitably subjectivemeans that perfectly accurate enforcement is not just technically out of reach, but intrinsically impossible. Nonetheless, the reliability of AI moderation tools and the range of criteria they can assess will likely continue improving rapidly. This has many advantages, such as reducing the incidence of arbitrary and mistaken content removalswhich tend to disproportionately impact marginalised groups 124and more effectively protecting users against harmful content. At the same time, it raises the prospect of ever more granular and pervasive corporate control over online speech 125 . Such control is easily abused for political purposes, and its exercise will ultimately be driven by platforms' commercial incentives, which may sometimes overlap with but do not inherently correspond to these public-interest goals.

Second, and relatedly, the scope of application of automated moderation tools will likely continue to expand. We have already seen a « function creep » where tools are used for more and more types of content and policy areas: for example, the Oversight Board's decisions show that Meta (and likely other leading platforms) now use hash-matching tools originally developed to address serious forms of illegal content, like child sexual abuse material, to efficiently scale up the moderation of content which may not even be illegal 126 . This expansion seems likely to continue, for multiple reasons.

Technical advances are one relevant factor. Given their efficiency and scalability, increasingly capable and accurate AI classifiers will likely be deployed in more situations previously considered to require human assessment, even if they remain short of human-level performance. Moreover, algorithmic moderation tools are not just developed to implement pre-existing policies: the available tools also shape what kinds of policies are possible or practical 127 . The capacity to filter content instantaneously and pre-emptively across an entire platformnot only by searching for exact matches of previously-removed content or using relatively crude criteria like blanket bans on nudity, but with AI classifiers which can make increasingly nuanced and context-sensitive assessments of meaningmay open the door to entirely new content policies and enforcement practices.

The other key factor is regulatory pressure. Governments have varying capacities to effectively regulate the enormously wealthy US-based multinational companies that control leading platformsoften depending largely on how valuable their consumers are to platforms as consumers and advertising targets 128 . However, there is now concerted pressure coming from a number of influential jurisdictions and multinational coalitions to expand the scope of automated moderation. Notably, there are regulatory proposals in the EU and UK to expand automated moderation to entirely new areas like private messaging, through tools like « client-side scanning », which allows encrypted messages to be monitored using software running on personal devices 129 .

In the social media context, platforms have already effectively been required to scale up automated moderation by existing measures like the EU's DSA and Terrorist Content Regulation, the UK's proposed Online Safety Bill, and similar legislation in other jurisdictions which impose broadly-defined duties for platforms to take proactive action against various types of harms and risks. Companies generally enjoy significant discretion over how to fulfil these legal duties, yet it seems inevitable that expanding automated moderation will be their main response. Not only is it cheaper and more scalable than, for example, hiring more trust and safety staff; it is also legally attractive. Because such tools are already in such widespread use, platform companies can argue that they represent industrystandard best practices. For big tech companies, the idea that technological advances like LLMs present efficient, effective technological solutions is ideologically and commercially appealing 130 ; for smaller platforms with limited resources, purchasing off-the-shelf moderation software from larger companies represents a practical and relatively affordable way to deal with commercial and legal demands for effective moderation.

Third and finally, shifting trends in the functionalities and use of social media also suggest that new forms of algorithmic intervention will continue to emerge. For example, popular platforms have increasingly shifted away from displaying content from people users already know or follow, and towards using algorithmic recommendation systems to surface new and unknown content 131 . In this context, « visibility moderation »recommending content less prominently or otherwise making it less accessible to other users, rather than removing it entirely -has become increasingly central to platforms' management of legal, reputational and commercial risks 132 . While this can in some ways represent a more proportionate response to harmful content which is less restrictive of free speech and access to information than removing content entirelyand can certainly be presented in that way by platformsit also raises difficult questions about the legitimate uses and potential abuses of platforms' power to shape online discourse by determining what speech can reach an audience 133 .

Major social media platforms, search engines and other online tools are also increasingly developing features based on generative AI, like chatbots and image-generation tools 134 . This will present similar questions about the exercise of power through content moderation. The work of human moderators, and AI models built to replicate their evaluations of whether AIgenerated content is safe, appropriate or helpful, has been central in making generative AI tools commercially viable135 . Ultimately, the algorithmic tools that determine what kind of content should be produced and what should be blocked as unsafe or inappropriate will ultimately reflect the commercial priorities of the companies that develop themand they will likely become a new target for state intervention, both legal and informal. As AIgenerated media become ubiquitous online, ever more of what we see in the media may be shaped by pervasive state and corporate control.

French philosopher Michel Foucault, known for his seminal and sombre work on surveillance, discipline and power, stated in an interview near the end of his life that, « My point is not that everything is bad, but that everything is dangerous. »136 The position that any and all state censorship is bad is rightly regarded as extremistespecially by feminist and antiracist scholars who emphasise the dangers that unrestricted speech (on-and offline) has historically posed for minority groups 137 . Content moderation, including algorithmic moderation, is essential to create safe and inclusive spaces for online media and communication. Yet the new channels for state and corporate power outlined in this chapter pose very real dangers: suppression of political dissent, systemic discrimination, and the channelling of all online media and communication in line with the priorities of corporate advertisers, to name but a few. Current trends suggest that the role of algorithmic censorship in online media and civic life will only continue to expand. Difficult questions lie ahead about when and how it can be used legitimately, and what safeguards might prevent the worst abuses of power.

  made by human moderators 48 , or evaluations by other workers in the burgeoning industry of AI training data production

	OVERSIGHT BOARD 2022C
	40 AUDIBLE MAGIC N.D.
	41 TANG 2023
	42 BRIDY 2020
	43 SENFTLEBEN et al. 2023
	44 MONEA 2022
	45 TALAT 2021
	46 MONEA 2022

decisions

  Article 20 of the 2022 Digital Services Act requires platforms to review decisions at the request of users, « under the supervision of appropriately qualified staff, and not solely on the basis of automated means ».

	48 GORWA et al. 2020
	49 DZIEZA 2023
	50 NICHOLAS & BHATIA 2023
	51 In the EU, For a detailed analysis of the level of human review required, see GRIFFIN & STALLMAN 2023
	52 DZIEZA 2023
	53 BELLANOVA & DE GOEDE 2021
	54 GILLESPIE 2018
	55 COBBE 2020, p. 739
	56 Ibid.
	57 YORK 2021; JØRGENSEN 2019
	58 PICKARD 2022
	59 COBBE 2020

[START_REF] Duarte | Mixed Messages? The Limits of Automated Social Media Content Analysis[END_REF]

[START_REF] Dzieza | AI Is a Lot of Work[END_REF][START_REF] Wong | America Already Has an AI Underclass[END_REF]

BERNSTEIN 1994, p. 226


Camille Megy
email: camille.megy@centralesupelec.fr

Massol Olivier

E-Ccgt Elec

Elec Producer

VRE Producer

Market power in Power-to-Gas and related markets? Preliminary insights for the upcoming interrelated power, gas, and hydrogen industries

Market power in Power-to-Gas and related markets?

Preliminary insights for the upcoming interrelated power, gas, and hydrogen industries.

Our analysis is based on a stylized partial equilibrium model that simulates the interactions between the markets for natural gas, power and hydrogen.

The model is formulated as a deterministic, discrete-time, finite-horizon oligopoly model that explicitly considers the imperfectly competitive structures prevailing in these three sectors.

The model is solved as an instance of a Mixed Complementarity Model (MCP)

Agents' maximization problems

An extensive use of PTG may raise social and environmental concerns

Social, because even if , the change in short-term welfare associated with the addition of PTG is positive, its distribution is unequal.

Environmental, because PTG use could indirectly lead to an increase in carbonbased electricity generation

POWER-TO-GAS?

Currently produced from gas, hydrogen can be produced from renewable electricity by electrolysis of water. This technology, called Power-to-Gas (PTG), is one solution considered to:

Provide flexibility to the electricity system Replace coal, oil, natural gas, and conventional hydrogen for uses where energy needs cannot economically be met by electricity Contribute to the energy security by decreasing dependency on fossil fuels icons are from Flaticon PTG is an early technology with high investment costs. First movers in PTG are firms with a strong oligopolistic presence in either the power, gas, or H2 markets

This study aims at comparing the market outcomes obtained under different asset-ownership structures for PTG in an imperfect power, gas and hydrogen system.

Do Industrial Organization considerations matter for the operation of Power-to-Gas?