Paper by Kenneth P. Seastedt et al: “The availability of large, deidentified health datasets has enabled significant innovation in using machine learning (ML) to better understand patients and their diseases. However, questions remain regarding the true privacy of this data, patient control over their data, and how we regulate data sharing in a way that does not encumber progress or further potentiate biases for underrepresented populations. After reviewing the literature on potential reidentifications of patients in publicly available datasets, we argue that the cost—measured in terms of access to future medical innovations and clinical software—of slowing ML progress is too great to limit sharing data through large publicly available databases for concerns of imperfect data anonymization. This cost is especially great for developing countries where the barriers preventing inclusion in such databases will continue to rise, further excluding these populations and increasing existing biases that favor high-income countries. Preventing artificial intelligence’s progress towards precision medicine and sliding back to clinical practice dogma may pose a larger threat than concerns of potential patient reidentification within publicly available datasets. While the risk to patient privacy should be minimized, we believe this risk will never be zero, and society has to determine an acceptable risk threshold below which data sharing can occur—for the benefit of a global medical knowledge system….(More)”.
Legal Dynamism
Paper by Sandy Pentland and Robert Mahari: “Shortly after the start of the French Revolution, Thomas Jefferson wrote a now famous letter to James Madison. He argued that no society could make a perpetual constitution, or indeed a perpetual law, that binds future generations. Every law ought to expire after nineteen years. Jefferson’s argument rested on the view that it is fundamentally unjust for people in the present to create laws for those in the future, but his argument is also appealing from a purely pragmatic perspective. As the state of the world changes, laws become outdated, and forcing future generations to abide by outdated laws is unjust and inefficient.
Today, the law appears to be at the cusp of its own revolution. Longer than most other disciplines, it has resisted technical transformation. Increasingly, however, computational approaches are finding their way into the creation and implementation of law and the field of computational law is rapidly expanding. One of the most exciting promises of computational law is the idea of legal dynamism: the concept that a law, by means of computational tools, can be expressed not as a static rule statement but rather as a dynamic object that includes system performance goals, metrics for success, and the ability to adapt the law in response to its performance…
The image of laws as algorithms goes back to at least the 1980s when the application of expert systems to legal reasoning was first explored. Whether applied by a machine learning system or a human, legal algorithms rely on inputs from society and produce outputs that affect social behavior and that are intended to produce social outcomes. As such, it appears that legal algorithms are akin to other human-machine systems and so the law may benefit from insights from the general study of these systems. Various design frameworks for human-machine systems have been proposed, many of which focus on the importance of measuring system performance and iterative redesign. In our view, these frameworks can also be applied to the design of legal systems.
A basic design framework consists of five components..(More)”.
Public procurement of artificial intelligence systems: new risks and future proofing
Paper by Merve Hickok: “Public entities around the world are increasingly deploying artificial intelligence (AI) and algorithmic decision-making systems to provide public services or to use their enforcement powers. The rationale for the public sector to use these systems is similar to private sector: increase efficiency and speed of transactions and lower the costs. However, public entities are first and foremost established to meet the needs of the members of society and protect the safety, fundamental rights, and wellbeing of those they serve. Currently AI systems are deployed by the public sector at various administrative levels without robust due diligence, monitoring, or transparency. This paper critically maps out the challenges in procurement of AI systems by public entities and the long-term implications necessitating AI-specific procurement guidelines and processes. This dual-prong exploration includes the new complexities and risks introduced by AI systems, and the institutional capabilities impacting the decision-making process. AI-specific public procurement guidelines are urgently needed to protect fundamental rights and due process…(More)”.
Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans
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
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?
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
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
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
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
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)”.