Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans

Curated on Posted on by Stefaan Verhulst

Paper by John Nay: “Artificial Intelligence (AI) capabilities are rapidly advancing. Highly capable AI could cause radically different futures depending on how it is developed and deployed. We are unable to specify human goals and societal values in a way that reliably directs AI behavior. Specifying the desirability (value) of an AI system taking a particular action in a particular state of the world is unwieldy beyond a very limited set of value-action-states. The purpose of machine learning is to train on a subset of states and have the resulting agent generalize an ability to choose high value actions in unencountered circumstances. But the function ascribing values to an agent’s actions during training is inevitably an incredibly incomplete encapsulation of human values, and the training process is a sparse exploration of states pertinent to all possible futures. Therefore, after training, AI is deployed with a coarse map of human preferred territory and will often choose actions unaligned with our preferred paths.

Law-making and legal interpretation form a computational engine that converts opaque human intentions and values into legible directives. Law Informs Code is the research agenda capturing complex computational legal processes, and embedding them in AI. Similar to how parties to a legal contract cannot foresee every potential “if-then” contingency of their future relationship, and legislators cannot predict all the circumstances under which their proposed bills will be applied, we cannot ex ante specify “if-then” rules that provably direct good AI behavior. Legal theory and practice have developed arrays of tools to address these specification problems. For instance, legal standards allow humans to develop shared understandings and adapt them to novel situations, i.e., to generalize expectations regarding actions taken to unspecified states of the world. In contrast to more prosaic uses of the law (e.g., as a deterrent of bad behavior through the threat of sanction), leveraged as an expression of how humans communicate their goals, and what society values, Law Informs Code.

We describe how data generated by legal processes and the practices of law (methods of law-making, statutory interpretation, contract drafting, applications of standards, legal reasoning, etc.) can facilitate the robust specification of inherently vague human goals. This increases human-AI alignment and the local usefulness of AI. Toward society-AI alignment, we present a framework for understanding law as the applied philosophy of multi-agent alignment, harnessing public law as an up-to-date knowledge base of democratically endorsed values ascribed to state-action pairs. Although law is partly a reflection of historically contingent political power – and thus not a perfect aggregation of citizen preferences – if properly parsed, its distillation offers the most legitimate computational comprehension of societal values available. Other data sources suggested for AI alignment – surveys of preferences, humans labeling “ethical” situations, or (most commonly) the implicit beliefs of the AI system designers – lack an authoritative source of synthesized preference aggregation. Law is grounded in a verifiable resolution: ultimately obtained from a court opinion, but short of that, elicited from legal experts. If law eventually informs powerful AI, engaging in the deliberative political process to improve law takes on even more meaning…(More)”.

Can critical policy studies outsmart AI? Research agenda on artificial intelligence technologies and public policy

Curated on Posted on by Stefaan Verhulst

Paper by Regine Paul: “The insertion of artificial intelligence technologies (AITs) and data-driven automation in public policymaking should be a metaphorical wake-up call for critical policy analysts. Both its wide representation as techno-solutionist remedy in otherwise slow, inefficient, and biased public decision-making and its regulation as a matter of rational risk analysis are conceptually flawed and democratically problematic. To ‘outsmart’ AI, this article stimulates the articulation of a critical research agenda on AITs and public policy, outlining three interconnected lines of inquiry for future research: (1) interpretivist disclosure of the norms and values that shape perceptions and uses of AITs in public policy, (2) exploration of AITs in public policy as a contingent practice of complex human-machine interactions, and (3) emancipatory critique of how ‘smart’ governance projects and AIT regulation interact with (global) inequalities and power relations…(More)”.

When do Reminders work?

Curated on Posted on by Stefaan Verhulst

Paper by Kai Barron, Mette Trier Damgaard and Christina Gravert: “An extensive literature shows that reminders can successfully change behavior. Yet, there exists substantial unexplained heterogeneity in their effectiveness, both: (i) across studies, and (ii) across individuals within a particular study. This paper investigates when and why reminders work. We develop a theoretical model that highlights three key mechanisms through which reminders may operate. To test the predictions of the model, we run a nationwide field experiment on medical adherence with over 4000 pregnant women in South Africa and document several key results. First, we find an extremely strong baseline demand for reminders. This demand increases after exposure to reminders, suggesting that individuals learn how valuable they are for freeing up memory resources. Second, stated adherence is increased by pure reminders and reminders containing a moral suasion component, but interestingly, reminders containing health information reduce adherence in our setting. Using a structural model, we show that heterogeneity in memory costs (or, equivalently, annoyance costs) is crucial for explaining the observed behavior…(More)”.

Smart cities: reviewing the debate about their ethical implications

Curated on Posted on by Stefaan Verhulst

Paper from Marta Ziosi, Benjamin Hewitt, Prathm Juneja, Mariarosaria Taddeo & Luciano Floridi: “This paper considers a host of definitions and labels attached to the concept of smart cities to identify four dimensions that ground a review of ethical concerns emerging from the current debate. These are: (1) network infrastructure, with the corresponding concerns of control, surveillance, and data privacy and ownership; (2) post-political governance, embodied in the tensions between public and private decision-making and cities as post-political entities; (3) social inclusion, expressed in the aspects of citizen participation and inclusion, and inequality and discrimination; and (4) sustainability, with a specific focus on the environment as an element to protect but also as a strategic element for the future. Given the persisting disagreements around the definition of a smart city, the article identifies in these four dimensions a more stable reference framework within which ethical concerns can be clustered and discussed. Identifying these dimensions makes possible a review of the ethical implications of smart cities that is transversal to their different types and resilient towards the unsettled debate over their definition…(More)”.

Designing a Data Sharing Tool Kit

Curated on Posted on by Stefaan Verhulst

Paper by Ilka Jussen, Julia Christina Schweihoff, Maleen Stachon and Frederik Möller: “Sharing data is essential to the success of modern data-driven business models. They play a crucial role for companies in creating new and better services and optimizing existing processes. While the interest in data sharing is growing, companies face an array of challenges preventing them from fully exploiting data sharing opportunities. Mitigating these risks and weighing them against their potential is a creative, interdisciplinary task in each company. The paper starts precisely at this point and proposes a Tool Kit with three Visual Inquiry Tool (VIT) to work on finding data sharing potential conjointly. We do this using a design-oriented research approach and contribute to research and practice by providing three VITs that help different stakeholders or companies in an ecosystem to visualize and design their data-sharing activities…(More)”.

AI Audit Washing and Accountability

Curated on Posted on by Stefaan Verhulst

Paper by Ellen P. Goodman and Julia Trehu: “Algorithmic decision systems, many using artificial intelligence, are reshaping the provision of private and public services across the globe. There is an urgent need for algorithmic governance. Jurisdictions are adopting or considering mandatory audits of these systems to assess compliance with legal and ethical standards or to provide assurance that the systems work as advertised. The hope is that audits will make public agencies and private firms accountable for the harms their algorithmic systems may cause, and thereby lead to harm reductions and more ethical tech. This hope will not be realized so long as the existing ambiguity around the term “audit” persists, and until audit standards are adequate and well-understood. The tacit expectation that algorithmic audits will function like established financial audits or newer human rights audits is fanciful at this stage. In the European Union, where algorithmic audit requirements are most advanced, to the United States, where they are nascent, core questions need to be addressed for audits to become reliable AI accountability mechanisms. In the absence of greater specification and more independent auditors, the risk is that AI auditing becomes AI audit washing. This paper first reports on proposed and enacted transatlantic AI or algorithmic audit provisions. It then draws on the technical, legal, and sociotechnical literature to address the who, what, why, and how of algorithmic audits, contributing to the literature advancing algorithmic governance…(More)“.

Big Data and Official Statistics

Curated on Posted on by Stefaan Verhulst

Paper by Katharine G. Abraham: “The infrastructure and methods for developed countries’ economic statistics, largely established in the mid-20th century, rest almost entirely on survey and administrative data. The increasing difficulty of obtaining survey responses threatens the sustainability of this model. Meanwhile, users of economic data are demanding ever more timely and granular information. “Big data” originally created for other purposes offer the promise of new approaches to the compilation of economic data. Drawing primarily on the U.S. experience, the paper considers the challenges to incorporating big data into the ongoing production of official economic statistics and provides examples of progress towards that goal to date. Beyond their value for the routine production of a standard set of official statistics, new sources of data create opportunities to respond more nimbly to emerging needs for information. The concluding section of the paper argues that national statistical offices should expand their mission to seize these opportunities…(More)”.

Existing and Potential Use Cases for Blockchain in Public Procurement

Curated on Posted on by Stefaan Verhulst

Paper by Pedro Telles: “The purpose of this paper is to assess the possibility of using blockchain technology in the realm of public procurement within the EU, particularly in connection with the award of public contracts. In this context, blockchain is used as an umbrella term covering IT technologies and cryptographic solutions used to generate consensus on a distributed ledger.

The paper starts by elaborating how blockchains and distributed ledgers work in general, includ-ing the drawbacks of different blockchain models and implementations, before looking into recent developments for distributed consensus that may herald some potential.

As for public procurement, blockchain has been used in three real use cases in Aragon (Spain), Colombia and Peru, with the first two not passing from the pilot stage and the latter being deployed in production. These use cases are analysed with an emphasis in what can be learned from the difficulties faced by each project.

Finally, this paper will posit two specific areas of EU public procurement practice that might benefit from the use of blockchain technology. The first is on data management and accessibility where current solutions have been unsuccessful, such as cross-border certification data as required by the European Single Procurement Document (ESPD) and e-Certis or the difficulties with contract data collection and publication. The second, on situations of clear lack of confidence on public powers, where the downsides of blockchain technologies and the costs they entail are an advantage. Even considering these potential scenarios, the overall perspective is that the benefits of blockchain solutions do not really provide much value in the context of public procurement for now…(More)”.

Essential Elements and Ethical Principles for Trustworthy Artificial Intelligence Adoption in Courts

Curated on Posted on by Stefaan Verhulst

Paper by Carlos E. Jimenez-Gomez and Jesus Cano Carrillo: “Tasks in courts have rapidly evolved from manual to digital work. In these innovation processes, theory and practice have demonstrated that adopting technology per se is not the right path. Innovation in courts requires specific plans for digital transformation, including analysis, programmatic changes, or skills. Artificial Intelligence (AI) is not an exception.
The use of AI in courts is not futuristic. From efficiency to decision-making support, AI-based tools are already being used by U.S. courts. To cite some examples, AI tools allow the discovery of divergences, disparities, and dissonances in jurisdictional activity. At a higher level, AI helps improve internal organization. AI helps with judicial decision consistency, exploiting a large judicial knowledge base in the form of big data, and it makes the judge’s work more agile with pattern and linguistic recognition in documents, identifying schemes and conceptualizations.

AI could bring considerable benefits to the judicial system. However, the risks and challenges are also
enormous, posing unique hurdles for user trust…

This article defines AI in relation to courts to understand challenges and implications and reviews AI components with a special focus on characteristics of trustworthy AI. It also examines the importance of a new policy and regulatory framework, and makes recommendations to avoid major problems…(More)”

Lawless Surveillance

Curated on Posted on by Stefaan Verhulst

Paper by Barry Friedman: “Here in the United States, policing agencies are engaging in mass collection of personal data, building a vast architecture of surveillance. License plate readers collect our location information. Mobile forensics data terminals suck in the contents of cell phones during traffic stops. CCTV maps our movements. Cheap storage means most of this is kept for long periods of time—sometimes into perpetuity. Artificial intelligence makes searching and mining the data a snap. For most of us whose data is collected, stored, and mined, there is no suspicion whatsoever of wrongdoing.

This growing network of surveillance is almost entirely unregulated. It is, in short, lawless. The Fourth Amendment touches almost none of it, either because what is captured occurs in public, and so is supposedly “knowingly exposed,” or because of doctrine that shields information collected from third parties. It is unregulated by statutes because legislative bodies—when they even know about these surveillance systems—see little profit in taking on the police.

In the face of growing concern over such surveillance, this Article argues there is a constitutional solution sitting in plain view. In virtually every other instance in which personal information is collected by the government, courts require that a sound regulatory scheme be in place before information collection occurs. The rulings on the mandatory nature of regulation are remarkably similar, no matter under which clause of the Constitution collection is challenged.

This Article excavates this enormous body of precedent and applies it to the problem of government mass data collection. It argues that before the government can engage in such surveillance, there must be a regulatory scheme in place. And by changing the default rule from allowing police to collect absent legislative prohibition, to banning collection until there is legislative action, legislatures will be compelled to act (or there will be no surveillance). The Article defines what a minimally-acceptable regulatory scheme for mass data collection must include, and shows how it can be grounded in the Constitution…(More)”.