Pazos on The Case for a (European?) Law of Reputational Feedback Systems

Ricardo Pazos (Universidad Autónoma de Madrid – Faculty of Law) has posted “The Case for a (European?) Law of Reputational Feedback Systems” (InDret, Vol. 3, 2021) on SSRN. Here is the abstract:

Reputational feedback systems are essential in the digital economy, as tools to build trust among traders and consumers and help the latter to make better choices. Although the number of platforms using such systems is growing, some aspects undermine their reliability, endangering the proper functioning of the market. In this context, it might be convenient to create a “law of reputational feedback systems” – a comprehensive set of rules specifically aimed at online reviews and ratings, and possibly at the European Union level with the goal of contributing to develop the digital single market. This paper aims at fostering a debate on the matter. First, it presents how important reputational feedback systems are and the weaknesses they are affected by. Then, it addresses the fragmentation argument that favours legal harmonisation, without forgetting that harmonisation has downsides, too. Afterwards, some possible rules are envisaged, considering academic or institutional initiatives and norms that already exist. Finally, to balance the discussion, this paper also offers arguments to support that further regulating reputational feedback systems, or at least doing it at the European level, could be a step in the wrong direction.

Interesting expansion of common law of reputational torts.

Castets-Renard on Human Rights and Algorithmic Impact Assessment for Predictive Policing

Céline Castets-Renard (University of Toulouse) has posted “Human Rights and Algorithmic Impact Assessment for Predictive Policing” (Constitutional Challenges in the Algorithmic Society, CUP 2021) on SSRN. Here is the abstract:

Law enforcement agencies are increasingly using algorithmic predictive policing systems to forecast criminal activity and allocate police resources. For instance, New York, Chicago, and Los Angeles use predictive policing systems built by private actors, such as PredPol, Palantir and Hunchlab, to assess crime risk and forecast its occurrence, in hope of mitigating it. More often, such systems predict the places where crimes are most likely to happen in a given time window (place-based) based on input data, such as location and timing of previously reported crimes. Other systems analyze who will be involved in a crime as either victim or perpetrator (person-based). Predictions can focus on variables such as places, people, groups or incidents. The goal is also to better deploy officers in a time of declining budgets and staffing. Such tools are mainly used in the US, but European police forces have expressed an interest in using them to protect the largest cities. Predictive policing systems and pilot projects have already been deployed , such as PredPol, used by the Kent Police in the UK.

However, these predictive systems challenge fundamental rights and guarantees of the criminal procedure (part. 2). I will address these issues by taking into account the enactment of ethical norms to reinforce constitutional rights (part. 3), as well as the use of a practical tool, namely Algorithmic Impact Assessment, to mitigate the risks of such systems (part. 4).

Coglianese on Regulating New Tech: Problems, Pathways, and People

Cary Coglianese (University of Pennsylvania Carey Law School) has posted “Regulating New Tech: Problems, Pathways, and People” (TechREG Chronicle, Issue 1) on SSRN. Here is the abstract:

New technologies bring with them many promises, but also a series of new problems. Even though these problems are new, they are not unlike the types of problems that regulators have long addressed in other contexts. The lessons from regulation in the past can thus guide regulatory efforts today. Regulators must focus on understanding the problems they seek to address and the causal pathways that lead to these problems. Then they must undertake efforts to shape the behavior of those in industry so that private sector managers focus on their technologies’ problems and take actions to interrupt the causal pathways. This means that regulatory organizations need to strengthen their own technological capacities; however, they need most of all to build their human capital. Successful regulation of technological innovation rests with top quality people who possess the background and skills needed to understand new technologies and their problems.

Erdos on Assessing UK Data Protection Reform in Transnational Context

David Erdos (University of Cambridge – Faculty of Law) has posted “Assessing UK Data Protection Reform in Transnational Context: What New Direction?” on SSRN. Here is the abstract:

This paper analyses the post-Brexit reforms to UK data protection put forward in Data: A New Direction. It is found that they are wide-ranging and significant but generally not radical. The great bulk of the proposed substantive changes to data protection (although not the most far-reaching suggestions concerning either e-privacy or automated decision-making) could be plausibly justified under the restrictions regime set out in the General Data Protection Regulation (GDPR). The reforms to the integrity duties would be deeper and pose some risk of reducing ʻaccountabilityʼ to formalistic theatre even when high risk processing is underway. Nevertheless, in principle their basic structure remains compatible with Data Protection Convention 108+ (DPC+). Proposals to shift the ICO away from a de jure focus on upholding data protection rights are difficult to square even with the DPC+. De facto the ICO is not acting as an effective enforcer of data subject rights even today, but these proposals would entrench and further this troubling reality. This points to a critical problem with the initiative, namely, its lack of balance vis-à-vis the interests of the data subject. A reform package which sought to marry more robust and accountable enforcement for individuals with some liberalisation of the substance and process of data protection would offer a better way forward.

Yu on The Long and Winding Road to Effective Copyright Protection in China

Peter K. Yu (Texas A&M University School of Law) has posted “The Long and Winding Road to Effective Copyright Protection in China” (Pepperdine Law Review, Vol. 49, Forthcoming) on SSRN. Here is the abstract:

In November 2020, China adopted the Third Amendment to the Copyright Law, providing a major overhaul of its copyright regime. This amendment entered into effect on June 1, 2021. The last time the regime was completely revamped was in October 2001, when the Copyright Law was amended two months before China joined the World Trade Organization. Although U.S. policymakers and industry groups have had mixed reactions about the recent amendment, the new law provides an opportunity to take stock of the progress China has made in the latest round of copyright law reform.

Written for the Symposium on “Hindsight Is 2020: A Look at Unresolved Issues in Music Copyright,” this article begins by mapping the long and winding road to effective copyright protection in China, especially in relation to U.S. rights holders. It then focuses on the recent amendment, highlighting five sets of upgrades or changes while offering three closing observations. The article concludes by offering five road tips to help copyright holders accelerate the trip toward their destination of effective copyright protection.

Polle et al. on AI Standards: Thought-Leadership in AI Legal, Ethical and Safety Specifications Through Experimentation

Roseline Polle (University College London) and others have posted “Towards AI Standards: Thought-Leadership in Ai Legal, Ethical and Safety Specifications Through Experimentation” on SSRN. Here is the abstract:

With the rapid adoption of algorithms in business and society there is a growing concern to safeguard the public interest. Researchers, policy-makers and industry sharing this view convened to collectively identify future areas of focus in order to advance AI standards – in particular the acute need to ensure standard suggestions are practical and empirically informed. This discussion occurred in the context of the creation of a lab at UCL with these concerns in mind (currently dubbed as UCL The Algorithms Standards and Technology Lab). Via a series of panels, with the main stakeholders, three themes emerged, namely (i) Building public trust, (ii) Accountability and Operationalisation, and (iii) Experimentation. In order to forward the themes, lab activities will fall under three streams – experimentation, community building and communication. The Lab’s mission is to provide thought-leadership in AI standards through experimentation.

Solow-Niderman on Information Privacy and the Inference Economy

Alicia Solow-Niederman (Harvard Law School) has posted “Information Privacy and the Inference Economy” on SSRN. Here is the abstract:

Information privacy is in trouble. Contemporary information privacy protections emphasize individuals’ control over their own personal information. But machine learning, the leading form of artificial intelligence, facilitates an inference economy that strains this protective approach past its breaking point. Machine learning provides pathways to use data and make probabilistic predictions—inferences—that are inadequately addressed by the current regime. For one, seemingly innocuous or irrelevant data can generate machine learning insights, making it impossible for an individual to anticipate what kinds of data warrant protection. Moreover, it is possible to aggregate myriad individuals’ data within machine learning models, identify patterns, and then apply the patterns to make inferences about other people who may or may not be part of the original data set. The inferential pathways created by such models shift away from “your” data, and towards a new category of “information that might be about you.” And because our law assumes that privacy is about personal, identifiable information, we miss the privacy interests implicated when aggregated data that is neither personal nor identifiable can be used to make inferences about you, me, and others.

This Article contends that accounting for the power and peril of inferences requires reframing information privacy governance as a network of organizational relationships to manage—not merely a set of data flows to constrain. The status quo magnifies the power of organizations that collect and process data, while disempowering the people who provide data and who are affected by data-driven decisions. It ignores the triangular relationship among collectors, processors, and people and, in particular, disregards the co-dependencies between organizations that collect data and organizations that process data to draw inferences. It is past time to rework the structure of our regulatory protections. This Article provides a framework to move forward. Accounting for organizational relationships reveals new sites for regulatory intervention and offers a more auspicious strategy to contend with the impact of data on human lives in our inference economy.

Simon-Kerr on Credibility in an Age of Algorithms

Julia Ann Simon-Kerr (University of Connecticut – School of Law) has posted “Credibility in an Age of Algorithms” (Rutgers Law Review, Forthcoming) on SSRN. Here is the abstract:

Evidence law has a “credibility” problem. Artificial intelligence creators will soon be marketing tools for assessing credibility in the courtroom. Yet, although credibility is a vital concept in the U.S. legal system, there is deep ambiguity within the law about its function. American jurisprudence assumes that impeachment evidence tells us about a witness’s propensity for truthfulness. Yet this same jurisprudence focuses fact-finders on external qualities that are probative of a witness’s worthiness of belief but not of the risk that they will lie. Without a clear understanding of what credibility in the legal system is or should be, the terms of engagement will be set by the creators of algorithms in accordance with their interests.

This article focuses on the two main paradigms within current credibility jurisprudence as a guide to thinking about how algorithms might be brought to bear on legal credibility. It does this by analogy to two existing algorithmic products. One is the U.S. credit scoring system. The other is China’s experiment with a “social credit” scoring system. These examples reflect the actual and purported function of credibility in the law in ways that are revealing both for current practice and as we contemplate the credibility of the future.