Workshop Details

The Digital Services Act, currently being finalized by the EU lawmaker, is a landmark piece of platform regulation with respect to illegal content online, content moderation processes, and user-platform dynamics more generally. In this talk, I will provide some insights into the main ingredients and underpinnings of this law, through a CS & Law research lens. This will include the way in which the DSA anticipates the development, adoption of effective risk management by dominant platforms, the data-intensive setup for increasing platform accountability, and some of the specific provisions related to different algorithmic systems, such as recommender systems and advertisement targeting. Notably, DSA risk management processes will be required to effectively scrutinize algorithmic content moderation systems for their potential impacts (on the availability of illegal content, the protection of fundamental rights and risks of manipulation). At the same time, it is clear that when mitigating content moderation risks, platforms can be expected to put forward more automated solutions.

[Video]

Bio:

Joris van Hoboken is an Associate Professor at the Institute for Information Law (IViR), University of Amsterdam and a Professor of Law at the Vrije Universiteit Brussels (VUB). At the Vrije Universiteit Brussels (VUB), he is appointed to the Chair ‘Fundamental Rights and Digital Transformation’. Joris works on the intersection of fundamental rights protection and the governance of platforms and internet-based services. Previously, Joris worked at the Information Law Institute (ILI) at NYU Law School, the NYU Stern Center for Business & Human Rights and CornellTech. He obtained his PhD from the University of Amsterdam on the topic of search engines and freedom of expression (2012) and has graduate degrees in Law and Theoretical Mathematics. Joris was a member of the EU Observatory on the Online Platform Economy, and a member of the Transatlantic High-Level Working Group on Content Moderation Online and Freedom of Expression. At IViR, he is currently running the DSA Observatory project, with a focus on upcoming implementation and enforcement of the DSA and questions of access to justice and content moderation.

Calls to regulate digital platforms to reduce harms have produced new legislation in Europe and the UK. In the US, efforts to regulate have so far foundered in part because First Amendment protection of speech forestalls many forms of regulation. Indeed, regulatory strategies in any liberal democracy that focus on de-emphasizing certain kinds of speech will run into freedom of expression problems. Other regulatory strategies that focus on product design, rather than speech, are more robust to free speech challenges. Regulating product design also has the benefit of addressing root causes of platform harms -- business and technical models -- rather than evanescent outputs of those models. Among the “by design” methods of regulation is an insistence on “friction by design.” The idea is that encouraging or mandating speed bumps in online communications will disrupt virality, reduce the size of groups, prompt people to engage in Type 2 thinking, and provide more opportunities for platform enforcement of their terms of service, among other goals. I will argue that for friction by design to have any real purchase, it is important to theorize the regulatory purpose of added friction and consider the possibilities that users will simply route around added frictions. If mandated friction ends up being an unwelcome “nudge” or, even worse, a deceptive pattern, it will only exacerbate online harms. However, if designed well, friction by design can function as a content-neutral safety precaution. This is an area that would benefit greatly from collaboration among the disciplines of law, psychology, and engineering.

[Video]

Bio:

Ellen P. Goodman @ellgood, is a professor of law and associate dean for strategic initiatives at Rutgers Law School. She co-directs and co-founded the Rutgers Institute for Information Policy & Law (RIIPL) and is a Senior Fellow at the German Marshall Fund.  She has published widely on media and telecommunications law, smart cities and algorithmic governance, freedom of expression, and advertising law. Goodman is currently a Knight Foundation grantee for a project relating to digital platform transparency and has served on Pittsburgh and Philadelphia algorithmic accountability and smart city task forces. Her short-form writing has appeared in the Washington Post, Guardian, Slate, Los Angeles Times, Democracy Journal, etc.  She served in the Obama Administration as a Distinguished Visiting Scholar with the Federal Communications Commission, and has been a visiting scholar at the London School of Economics and the University of Pennsylvania. She has been the recipient of Ford Foundation, Democracy Fund, and Geraldine R. Dodge grants for work on advancing new public media models and public interest journalism. Prior to joining the Rutgers faculty, Goodman was a partner at the law firm of Covington & Burling LLP, where she practiced in the information technology area. She is a graduate of Harvard College and Harvard Law School, clerked for Judge Norma Shapiro on the Eastern District of Pennsylvania, and has three children.

Global security risks are increasingly governed through complex data infrastructures using forms of algorithmic regulation and automated decision-making. This paper explores the challenges that such global security infrastructures pose for how we think about law and regulation and shows how automated content moderation practices are reconfiguring the capacity of legal norms to ground authority, protect rights and constrain power. My analysis focuses on the detection and removal of terrorist and violent extremist content (TVEC) online and empirically follows one particular regulatory device: the hash-sharing database of the Global Internet Forum to Counter Terrorism (GIFCT). I argue that grappling with the challenges of data-driven security and content moderation in the TVEC online space requires analysing how legal practices, regulatory techniques and data infrastructures emerge and co-evolve together by following their dynamic interrelations as socio-technical assemblages. Drawing from Science and Technology Studies (STS), recent socio-legal debates on legal materiality and critical infrastructure studies scholarship, my paper experiments with an approach (infralegalities) for mapping global security infrastructures in action and foregrounding their regulatory effects, in order to make their broader political and normative stakes visible and contestable.

Bio:
Gavin Sullivan (@g_sullivan) is a Reader in International Human Rights Law at Edinburgh Law School, The University of Edinburgh. His research focuses on the politics of global security law, algorithmic regulation and accountability using socio-legal methods. He was recently awarded a UK Research and Innovation Future Leaders Fellowship for his project, Infra-Legalities: Global Security Infrastructures, Artificial Intelligence and International Law, examining how AI and automated decision-making is reshaping global security law and governance. His first book, The Law of the List: UN Counterterrorism Sanctions and the Politics of Global Security Law won two ISA best book awards in 2021. Gavin is currently a Visiting Fellow at the Princeton University Center for Human Values, a member of the ‘Transparency’ and ‘Technical Approaches’ working groups of the Global Internet Forum to Counter Terrorism (GIFCT) and on the Editorial Committee of Transnational Legal Theory.

End-to-end encryption (E2EE) is increasingly adopted in all kinds of communication, including secure messaging, video, audio, email, file sharing, and web browsing.  As E2EE systems expand and grow, so too do the needs and challenges for content moderation in these systems.  This talk describes a systemization of knowledge of content moderation under E2EE.  We will first taxonomize general content moderation approaches and extract some key design choices for content moderation in E2EE services.  We then survey the current cryptography literature on content moderation in end-to-end encryption, which focuses mainly on (1) detecting rule violations by private content scanning, and (2) authenticity in user reporting.  We conclude by proposing some fruitful areas of future research that are under-explored in the current literature.

[Video]

Bio:

Sarah Scheffler is a postdoctoral research associate at Princeton University's Center for Information Technology Policy, studying topics at the intersection of cryptography and law.  Her recent works focus on content moderation in end-to-end encryption, compelled decryption, and zero-knowledge proofs.  She received her Ph.D. in computer science from Boston University in 2021, working with Prof. Mayank Varia.

Public and policy debate about content moderation has overwhelmingly focused on removal: social media platforms deleting content and suspending users, or opting not to. But removal is not the only available remedy. Reducing the visibility of problematic content is becoming an increasingly common part of platform governance. Platforms use machine learning classifiers to identify content that is misleading enough, harmful enough, offensive enough that, while they do not warrant removal according to the site’s guidelines, they warrant reducing their visibility by demoting them in algorithmic rankings and recommendations, or excluding them entirely. In this talk, I will make the case that reduction techniques should be understood as part of content moderation, and consider the implications of using recommendation in this way. 

[Video]

Bio:

Tarleton Gillespie is a senior principal researcher at Microsoft Research, an affiliated associate professor in the Department of Communication and Department of Information Science at Cornell University, author of Wired Shut: Copyright and the Shape of Digital Culture (MIT, 2007) , co-editor of Media Technologies: Essays on Communication, Materiality, and Society (MIT, 2014), and author of Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions that Shape Social Media (Yale, 2018).

In Larry Lessig’s 2000 book Code, he identifies four factors that regulate behavior: the law, social norms, architecture, and the market.  The significance of the factor of the market is often underappreciated.  Market forces are important to internet content in two distinct senses: the cost of moderation, and the financial pressures that steer corporate decision making. First, with enough resources, perfect moderation is possible.  With realistic resources, the task is challenging. These factors shape the forms that content moderation and internet content take in a fundamental way. Second, I’ll argue that publicly traded internet platforms driven to maximize shareholder value are unlikely to do the right thing for either individuals or communities.  Consequently, we need more non-profit social media driven by values.

[Video]

Bio:

Amy Bruckman is Regents’ Professor and Senior Associate Chair in the School of Interactive Computing at the Georgia Institute of Technology. Her research focuses on social computing, with interests in online collaboration, understanding across difference, and online moderation. Bruckman received her Ph.D. from the MIT Media Lab in 1997, and a B.A. in physics from Harvard University in 1987. She is a Fellow of The ACM and a member of the SIGCHI Academy.

Content moderation on online social platforms is broken. As a result of their inability to moderate adequately, platforms have become host to all manner of undesirable content and behavior, from hate and harassment targeted at marginalized groups to the spread of misinformation that is harmful to society. In this talk, I argue that many of today’s content moderation systems fall short because they are operating from two flawed premises—that it is possible to come up with a single set of policies to enforce consistently across a large and diverse user base, and that users should have frictionless power to post and yet no responsibility towards (or power to shape) the collective governance of a social space. Instead, my research focuses on how to design the governance structures, processes, and tooling of online social platforms so that they empower users and communities to have greater agency and voice in governance. My contributions include: 1) new tools at the community level to support more powerful and flexible collective design and execution of policies, 2) new tools at the user level to support individuals with moderating what they personally see, and 3) reimagining processes at the platform level to legitimize platform-wide decisions.

[Video]

Bio:

Amy Zhang is an Assistant Professor at University of Washington's Paul G. Allen School of Computer Science & Engineering, where she leads the Social Futures Lab, a lab dedicated to reimagining social and collaborative systems to empower people and improve society. She is a faculty member of the UW Center for an Informed Public, a visiting researcher at the Allen Institute for AI, and Belfer Fellow at the Anti-Defamation League. Prior to joining UW, she was a Postdoctoral Fellow at Stanford CS, and completed her PhD at MIT CSAIL. During her PhD, she was an affiliate and 2018-19 Fellow at the Berkman Klein Center at Harvard University, a Google PhD Fellow, and an NSF Graduate Research Fellow.

For the last three years, Massachusetts State Senator Becca Rausch has been the target of identity-based and other hatred on social media as a Jewish woman and advocate for vaccine infrastructure and public health protections. These attacks only intensified during the COVID-19 pandemic – even spilling over into in-person aggression – amidst the spread of disinformation and far-right content online. She and her staff face the daily challenge of content moderation within the uncertain constraints of the First Amendment and her duty to provide an accessible platform for her constituents despite her “trolls,” most of whom are out-of-district. This presentation discusses the limitations of elected officials’ online content moderation, underscoring the need for federal statutory reform and social media platforms protecting their users and society against algorithmic bias, self-radicalization, disinformation, and online hate.

[Video]

Senator Rausch’s bio can be found here.

Machine learning models are commonly used to detect toxicity in online conversations. These models are trained on datasets annotated by human raters. We are exploring how raters’ self-described identities impact how they annotate toxicity in online comments, can create more inclusive machine learning models, and such specialized rarer pools provide more nuanced ratings than those by random raters. Using these models to empower targets  to self-manage their online harassment is another direction we continue to explore, by developing a PMCR (Prevention-Monitoring-Crisis-Recovery) harassment framework of user needs, and launching tools that can be used in the wild like Harassment Manager.

Bio:

Tesh (Nitesh) Goyal is the Head of User Research on Responsible AI Tools at Google Research. His work at Google has led to launch of ML based tools like Harassment Manager to empower targets of online harassment, ML based moderation to reduce online toxic content production on platforms like OpenWeb, and multiple NLP based tools that reduce biased sensemaking in criminal justice. He received his MSc in Computer Science from UC, Berkeley and RWTH Aachen, prior to receiving his PhD from Cornell University in Information Science. His research has been supported by German Govt. Fellowship, National Science Foundation, and MacArthur Genius Grant. He has published in top-tier HCI conferences and journals (CHI, CSCW, JASIST, ICTD, ICIC and Ubicomp/IMWUT), received two best paper honorable mention awards (CHI, CSCW), one nomination (ICTD Journal) and has been covered in popular press (eg. TheVerge, Everything in Moderation).

Tesh has served on the Organization Committee for ACM SIGCHI conferences including Tech Program Chair for coming CHI 2023, GDI Chair at CHI 2021, Doctoral Consortium Chair at IMX 2021, D&I Lunch Chair at CHI 2018-2020, and over 10 times as Associate Chair at multiple CHI and CSCW conferences since 2016. Tesh has also been appointed as Adjunct Professor at NYU Computer Science Department.

This paper takes a legal perspective on the phenomenon of ‘shadow banning’, i.e. undisclosed content moderation actions. It focuses on the transparency rights introduced in the EU’s new Digital Services Act (DSA), which lays out a procedural due process framework for all content moderation actions. This paper shows how this due process framework can be interpreted as prohibiting shadow bans, at least on paper. Yet it highlights a crucial interpretational difficulty held in common by the DSA’s due process framework and by the shadow banning imaginary it responds to: how to define moderation actions, particularly as regards ‘demotion’ or ‘downranking’. What users experience as a targeted and top-down ‘demotion’ sanction can also be the result of more systemic and audience-driven volatilities in content ranking. To distinguish these, the DSA’s project of individual due process will require a detailed engagement with the technical architectures of different ranking systems, in furtherance of an essentially normative delineation between a normalized default and a problematized exception; between routine curation and exceptional moderation. If at all enforceable, therefore, the DSA’s individual due process framework will necessarily disregard the more structural dimensions of ranking governance—how platforms exercise power not just by ruling on the downranking exception but by writing and continually revising the rules of the ranking game.

[Video]

Bio:

            Paddy Leerssen is a PhD Candidate at the institute for Information Law (IViR) University of Amsterdam. His research focuses on the regulation of transparency in social media platforms under EU law.

Once called the "metaphysics of law," copyright is highly case-specific and at times incredibly confusing or inconsistent. Despite this, everyday technology users are constantly faced with making decisions about what they can and can't do when it comes to "copies" of digital content, and fear of misunderstanding or getting into trouble lead to chilling effects. Moreover, the technical mechanisms designed to police copyright infringement (e.g., digital rights management and algorithmic content moderation) are often flawed. This talk examines research and case studies surrounding digital copyright and how they connect to useability, platform policies, and algorithmic bias.

[Video]

Bio:

Casey Fiesler is an assistant professor in Information Science (and Computer Science by courtesy) at University of Colorado Boulder. She researches and teaches in the areas of technology ethics, internet law and policy, and online communities. Her work on research ethics for data science, ethics education in computing, and broadening participation in computing is supported by the National Science Foundation, and she is the recipient of an NSF CAREER Award. She is also a fellow with the Silicon Flatirons Center for Law Technology and Entrepreneurship and the Center for Democracy and Technology, as well as a member of the legal committee for the Organization for Transformative Works. She holds a PhD in Human-Centered Computing from Georgia Tech and a JD from Vanderbilt Law School.

We present Orlando, a programming language for expressing transfers of rights in real property, and Littleton, an online and freely available implementation that can interpret legal "programs" written in Orlando, diagram the resulting property interests, and model the consequences of future events. Orlando and Littleton take advantage of the ways in which the highly formalized language used by lawyers, scholars, and teachers to express property transfers already has significant features of a programming language, one whose semantics are the doctrines of property law. Formalizing future interests helps students and teachers to visualize and experiment with the rules of property law. Moreover, the process of formalization is itself deeply illuminating about property doctrine and theory.

[Video]

Bio:

James Grimmelmann is the Tessler Family Professor of Digital and Information Law at Cornell Law School and Cornell Tech, where he directs CTRL-ALT, the Cornell Tech Research Lab in Applied Law and Technology. He studies how laws regulating software affect freedom, wealth, and power. He helps lawyers and technologists understand each other, applying ideas from computer science to problems in law and vice versa. He is the author of the textbook Internet Law: Cases and Problems and numerous articles on search engines, digital copyright, online governance, content moderation, and other topics in computer and Internet law.

List of Posters

Public Verification for Private Hash Matching

Presenter: Anunay Kulshrestha, Princeton University

The increasing implementation of end-to-end encryption (E2EE) poses unprecedented challenges for content moderation, because communications services lack access to plaintext content. Efforts to combat child sexual abuse material (CSAM) have become a particular global flashpoint: the predominant method of detection in non-E2EE settings, perceptual hash matching, is unavailable in E2EE settings. Recent advances in applied cryptography enable privacy-preserving hash matching, where a service can identify a match without accessing non-matching content and without disclosing the hash set. These designs, especially a high-profile proposal by Apple for identifying CSAM in its iCloud Photos service, have attracted widespread criticism for creating security, privacy, and free expression risks.

In this work, we aim to advance scholarship and dialog about private hash matching systems. We systematize considerations for deployment of private hash matching systems, developing a set of questions that remain unresolved for implementation. Next, we describe how law and public policy could respond to concerns about private hash matching by providing guardrails for implementations. Finally, we contribute three novel cryptographic protocols for improving public confidence in private hash matching systems: (1) proof that child safety groups approved the hash set; (2) proof that if a user's content is a false positive match, the user will receive eventual notification of the match; and (3) proof that particular lawful content is not present in the hash set. The protocols that we describe are practical, efficient, and compatible with existing constructions for private hash matching.

Approaches to Content Moderation in End-to-end Encrypted Systems

Presenter: Lucy Qin, Brown University

This poster presents on a published report that assesses current technical proposals for the detection of unwanted content in end-to-end encrypted (E2EE) services against the guarantees of E2EE. We find that technical approaches for user-reporting and meta-data analysis are the most likely to preserve privacy and security guarantees for end-users. Both provide effective tools that can detect significant amounts of different types of problematic content on E2EE services, including abusive and harassing messages, spam, mis- and disinformation, and CSAM. Conversely, we find that other techniques that purport to facilitate content detection in E2EE systems have the effect of undermining key security guarantees of E2EE systems. The full report is available here: https://cdt.org/insights/outside-looking-in-approaches-to-content-moderation-in-end-to-end-encrypted-systems/

ε-Differential Privacy, and a Two Step

Presenter: Nathan Reitinger, University of Maryland

Sharing data in the 21st century is fraught with error. Most commonly, data is freely accessible, surreptitiously stolen, and easily capitalized in the pursuit of monetary maximization. But when data does find itself shrouded behind the veil of “personally identifiable information,” data becomes nearly sacrosanct, impenetrable without dense consideration of ambiguous statutory law—inhibiting utility. Both outcomes are abhorrent, unnecessarily stifling innovation or indiscriminately pilfering privacy.

We propose a novel, two-step test which creates future-proof, bright-line rules around the sharing of legally-protected data. The crux of our test centers on identifying a legal comparator between a particular data sanitization standard—differential privacy, assessing “mechanisms” or “recipes” for data manipulation—and statutory law. Step one identifies a proxy value which may be easy calculated from an ε-differentially private mechanism: “re-identification risk”; step two looks for a corollary in statutory law, assessing the maximum “re-identification risk” a statute tolerates when permitting confidential data sharing. If step one is lower than or equal to step two, any output derived using the mechanism may be considered legally shareable; the mechanism is (statute,ε)-differentially private.

True, our test lacks a healthy dose of justiciability, making it difficult to predict what particular mechanism attributes will be appropriate for which particular statutes. That precision is lacking, however, does not displace the true value of our test—its ability to provide confidence to data stewards hosting legally-protected data. This confidence, in turn, may give rise to risk-free, privacy-protected data sharing, greasing the wheels on advancements in science and technology, rather than stifling innovation with high-penalty, low-description statutes.

Resolving Online Content Disputes in the age of Artificial Intelligence: Legal and Technological Solutions

Presenter: Faye Wang, Brunel University London (presenting remotely)

The common universal route of seeking solutions for copyright infringement on online platforms is that when rightsholders notice that their copyrighted content was infringed online, they may initiate a notice and takedown procedure to the operators of online platforms, seek solutions from online dispute resolution (ODR) and alternative dispute resolution (ADR) services, and when all fails, they may then file a lawsuit in courts .Nowadays, it is possible that notice and take down procedures and ODR services may be assisted by AI technology. Technical measures for blocking injunctions may also involve the consideration of appropriate AI technology. It is increasingly common for established online platforms to adopt voluntary AI-assisted technological solutions (such as ‘automated filtering software’ or ‘automated content moderation tools’) to minimise their legal risks for infringing content on their platforms before a notice and takedown legal procedure takes place. In Europe, the EC Directive on Electronic Commerce 2000 prohibits imposing intermediaries with general monitoring obligations but grants intermediaries with responsibilities to remove illegal content under the ‘notice and takedown’ regime. The proposed Digital Service Act 2020 also affirms the prohibition of general monitoring obligations. However, it is arguable that the Copyright Directive in 2019 supports the use of automated filtering system to detect illegal content which may appear to contradict with the principle of prohibiting imposing ‘general monitoring obligations’. However, in the most recent European Court of Justice joint cases of YouTube and Elsevier, the Court also upholds the use of automated filtering systems to benefit from liability exemption.

This poster reviews current regulations for the liability of hosting service providers in notice and takedown procedures with reference to Europe, US and China. It also provides the interpretation of the meaning of ‘best efforts’ to prevent future uploads by hosting service providers as newly introduced in Article 17 of the Copyright Directive 2019. It proposes possible legal and technological solutions to resolve copyright-related disputes over the Internet with the assistance of AI.

Part 1: Empowering End Users to Make their own Choices on Harassment, Misinformation, Free expression, and More

Today we mostly rely on platforms and/or administrators to moderate content for us, and are often dissatisfied when they fail to do so effectively.  In my group, we are exploring an alternative approach: empowering all individuals on the platform to make their own moderation decisions for themselves.   I will argue that existing systems can and should be changed to give individual users greater autonomy in moderation.   In this and the next few talks, we'll discuss a few different systems that we've built to explore this idea.  I'll start with Squadbox, a system that helps users coordinate their own friends as moderators to protect them from harassment.

[Karger Video]

Part 2: Fighting Misinformation through a User-Managed Web of Trust

Platform operators have devoted significant effort to combating misinformation on behalf of their users. Users are also stakeholders in this battle, but their efforts to combat misinformation go unsupported by the platforms. In this work, we consider three new user affordances that give social media users greater power in their fight against misinformation: (1) structured accuracy assessments of posts by users, (2) user-specified indication of trust in other users, and (3) and user configuration of social feed filters according to assessed accuracy.  Through interviews, a survey, and experiments with a prototype system implementing the affordances, we assess the potential power of these affordances to improve the quality of social information-sharing.

[Jahanbakhsh Video]

Part 3: Graffiti: The Potential for Anarchy in the Formation of Online Spaces

The social spaces we inhabit online are rarely ideal, but there is little we can do to change them without jeopardizing our social connections that exist within them. We introduce a system titled Graffiti that suggests a new ecosystem of online social spaces is possible: one where social data is not siloed into any one specific space. In this ecosystem, users can use whatever interfaces, filters, algorithms, moderators and broadcast patterns they see fit. The Graffiti API is simple enough that it is even possible for novice programmers to create or remix social spaces using nothing but HTML and CSS. There is still a separation between contextual spaces in Graffiti --- without any separation between our home, work and third spaces we will inevitably experience "context collapse" --- but Graffiti's power comes from its ability for data to exist in large sets of spaces all at once and these sets need only intersect, not overlap, with the set of spaces that someone observes to facilitate communication. An ecosystem with this amount of flexibility requires rethinking moderation, identity management, and design interventions and we discuss how these changes might interplay with existing and future laws.

[Henderson Video]

Part 4: Can it Work?

We’ve discussed three systems that give end users power to substantially control what they encounter.  Here we'll step back and think about the pros and cons of this approach.      

Bios:

Theia Henderson is a 2nd-year PhD student in computer science at MIT CSAIL, where she is a member of the Haystack group advised by David Karger. Her research in Human-Computer Interaction is focused on improving social media and other online social tools. As part of her work she builds real-world systems, but these practical designs draw inspiration from her background in theoretical computer science.

Farnaz Jahanbakhsh is currently a PhD student at MIT CSAIL advised by David Karger. Before starting at MIT, she completed a Masters in computer science at the University of Illinois at Urbana Champaign (UIUC) and, before that, a bachelors in computer engineering at Sharif University of Technology in Tehran, Iran.  Her areas of research are Human-Computer Interaction and Social Computing.

David Karger is a Professor in the Computer Science and Artificial Intelligence Laboratory in the EECS department at MIT.  His primary interest is currently in developing tools that help individuals manage information better. This involves studying people and current tools to understand where the problems are, creating and evaluating tools that address those problems, and deploying those tools to learn how people use them and iterate the whole process. His work draws on whatever fields can help: information retrieval, machine learning, databases, and algorithms, but most often human computer interaction.  Karger began his career in algorithms and continues to be interested in the topic, particularly in the application of algorithms to real world problems. This has led him to work in systems, networking, and coding and communication.

Current approaches to content moderation all assume the continuing existence of “walled gardens”: social media platforms that control who can use their services and how. But this closed architecture may be the very thing that is causing our content moderation challenges. The "Fediverse"---the emerging ecosystem of decentralized protocols like ActivityPub and its associated social media applications---offers an alternative to the status quo. This project describes the Fediverse and how it works, focusing on the contrast between open and closed protocols. It uses Mastodon, the decentralized Twitter alternative, as a case study to demonstrate how content moderation operates in the absence of a single centralized authority.

[Video]

Bio:

Alan Z. Rozenshtein is an Associate Professor of Law at the University of Minnesota Law School, a senior editor at Lawfare, and a term member of the Council on Foreign Relations. He is a co-organizer of the Cybersecurity Law and Policy Scholars Conference and the Law and Technology Workshop. Previously, he served as an Attorney Advisor with the Office of Law and Policy in the National Security Division of the U.S. Department of Justice and a Special Assistant United States Attorney in the U.S. Attorney's Office for the District of Maryland.