E-Nudging Justice: The Role of Digital Choice Architecture in Online Courts

Curated on Posted on by Stefaan Verhulst

Paper by Ayelet Sela: “Justice systems around the world are launching online courts and tribunals in order to improve access to justice, especially for self-represented litigants (SRLs). Online courts are designed to handhold SRLs throughout the process and empower them to make procedural and substantive decisions. To that end, they present SRLs with streamlined and simplified procedures and employ a host of user interface design and user experience strategies (UI/UX). Focusing on these features, the article analyzes online courts as digital choice environments that shape SRLs’ decisions, inputs and actions, and considers their implications on access to justice, due process and the impartiality of courts. Accordingly, the article begins to close the knowledge gap regarding choice architecture in online legal proceedings. 

Using examples from current online courts, the article considers how mechanisms such as choice overload, display, colorfulness, visual complexity, and personalization influence SRLs’ choices and actions. The analysis builds on research in cognitive psychology and behavioral economics that shows that subtle changes in the context in which decisions are made steer (nudge) people to choose a particular option or course of action. It is also informed by recent studies that capture the effect of digital choice architecture on users’ choices and behaviors in online settings. The discussion clarifies that seemingly naïve UI/UX features can strongly influence users of online courts, in a manner that may be at odds with their institutional commitment to impartiality and due process. Moreover, the article challenges the view that online court interfaces (and those of other online legal services, for that matter) should be designed to maximize navigability, intuitiveness and user-friendliness. It argues that these design attributes involve the risk of nudging SRLs to make uninformed, non-deliberate, and biased decisions, possibly infringing their autonomy and self-determination. Accordingly, the article suggests that choice architecture in online courts should aim to encourage reflective participation and informed decision-making. Specifically, its goal should be to improve SRLs’ ability to identify and consider options, and advance their own — inherently diverse — interests. In order to mitigate the abovementioned risks, the article proposes an initial evaluation framework, measures, and methodologies to support evidence-based and ethical choice architecture in online courts….(More)”.

Law as Data: Computation, Text, and the Future of Legal Analysis

Curated on Posted on by Stefaan Verhulst

Book edited by Michael A. Livermore and Daniel N. Rockmore: “In recent years, the digitization of legal texts, combined with developments in the fields of statistics, computer science, and data analytics, have opened entirely new approaches to the study of law. This volume explores the new field of computational legal analysis, an approach marked by its use of legal texts as data. The emphasis herein is work that pushes methodological boundaries, either by using new tools to study longstanding questions within legal studies or by identifying new questions in response to developments in data availability and analysis.

By using the text and underlying data of legal documents as the direct objects of quantitative statistical analysis, Law as Data introduces the legal world to the broad range of computational tools already proving themselves relevant to law scholarship and practice, and highlights the early steps in what promises to be an exciting new approach to studying the law….(More)”.

Review into bias in algorithmic decision-making

Curated on Posted on by Stefaan Verhulst

Interim Report by the Centre for Data Ethics and Innovation (UK): The use of algorithms has the potential to improve the quality of decision- making by increasing the speed and accuracy with which decisions are made. If designed well, they can reduce human bias in decision-making processes. However, as the volume and variety of data used to inform decisions increases, and the algorithms used to interpret the data become more complex, concerns are growing that without proper oversight, algorithms risk entrenching and potentially worsening bias.

The way in which decisions are made, the potential biases which they are subject to and the impact these decisions have on individuals are highly context dependent. Our Review focuses on exploring bias in four key sectors: policing, financial services, recruitment and local government. These have been selected because they all involve significant decisions being made about individuals, there is evidence of the growing uptake of machine learning algorithms in the sectors and there is evidence of historic bias in decision-making within these sectors. This Review seeks to answer three sets of questions:

  1. Data: Do organisations and regulators have access to the data they require to adequately identify and mitigate bias?
  2. Tools and techniques: What statistical and technical solutions are available now or will be required in future to identify and mitigate bias and which represent best practice?
  3. Governance: Who should be responsible for governing, auditing and assuring these algorithmic decision-making systems?

Our work to date has led to some emerging insights that respond to these three sets of questions and will guide our subsequent work….(More)”.

Studying Crime and Place with the Crime Open Database

Curated on Posted on by Stefaan Verhulst

M. P. J. Ashby in Research Data Journal for the Humanities and Social Sciences: “The study of spatial and temporal crime patterns is important for both academic understanding of crime-generating processes and for policies aimed at reducing crime. However, studying crime and place is often made more difficult by restrictions on access to appropriate crime data. This means understanding of many spatio-temporal crime patterns are limited to data from a single geographic setting, and there are few attempts at replication. This article introduces the Crime Open Database (code), a database of 16 million offenses from 10 of the largest United States cities over 11 years and more than 60 offense types. Open crime data were obtained from each city, having been published in multiple incompatible formats. The data were processed to harmonize geographic co-ordinates, dates and times, offense categories and location types, as well as adding census and other geographic identifiers. The resulting database allows the wider study of spatio-temporal patterns of crime across multiple US cities, allowing greater understanding of variations in the relationships between crime and place across different settings, as well as facilitating replication of research….(More)”.

Blockchain and Public Record Keeping: Of Temples, Prisons, and the (Re)Configuration of Power

Curated on Posted on by Stefaan Verhulst

Paper by Victoria L. Lemieux: “This paper discusses blockchain technology as a public record keeping system, linking record keeping to power of authority, veneration (temples), and control (prisons) that configure and reconfigure social, economic, and political relations. It discusses blockchain technology as being constructed as a mechanism to counter institutions and social actors that currently hold power, but whom are nowadays often viewed with mistrust. It explores claims for blockchain as a record keeping force of resistance to those powers using an archival theoretic analytic lens. The paper evaluates claims that blockchain technology can support the creation and preservation of trustworthy records able to serve as alternative sources of evidence of rights, entitlements and actions with the potential to unseat the institutional power of the nation-state….(More)”.

Secrecy, Privacy and Accountability: Challenges for Social Research

Curated on Posted on by Stefaan Verhulst

Book by Mike Sheaff: “Public mistrust of those in authority and failings of public organisations frame disputes over attribution of responsibility between individuals and systems. Exemplified with examples, including the Aberfan disaster, the death of Baby P, and Mid Staffs Hospital, this book explores parallel conflicts over access to information and privacy.

The Freedom of Information Act (FOIA) allows access to information about public organisations but can be in conflict with the Data Protection Act, protecting personal information. Exploring the use of the FOIA as a research tool, Sheaff offers a unique contribution to the development of sociological research methods, and debates connected to privacy and secrecy in the information age. This book will provide sociologists and social scientists with a fresh perspective on contemporary issues of power and control….(More)”.

Supreme Court rules against newspaper seeking access to food stamp data

Curated on Posted on by Stefaan Verhulst

Josh Gerstein at Politico: “The Supreme Court on Monday handed a victory to businesses seeking to block their information from being disclosed to the public after it winds up in the hands of the federal government.

The justices ruled in favor of retailers seeking to prevent a South Dakota newspaper from obtaining store-level data on the redemption of food stamp benefits, now officially known as the Supplemental Nutrition Assistance Program, or SNAP.

The high court ruling rejected a nearly half-century-old appeals court precedent that allowed the withholding of business records under the Freedom of Information Act only in cases where harm would result either to the business or to the government’s ability to acquire information in the future.

The latest case was set into motion when the U.S. Department of Agriculture refused to disclose the store-level SNAP data in response to a 2011 FOIA request from the Argus Leader, the daily newspaper in Sioux Falls, South Dakota. The newspaper sued, but a federal district court ruled in favor of the USDA.

The Argus Leader appealed, and the U.S. Appeals Court for the 8th Circuit ruled that the exemption the USDA was citing did not apply in this case, sending the issue back to a lower court. The district court was tasked with determining whether the USDA was covered by a separate FOIA exemption governing information that would cause competitive injury if released.

That court ruled in favor of the newspaper, at which point the Food Marketing Institute, a trade group that represents retailers such as grocery stores, filed an appeal in lieu of the USDA….(More)”.

Developing Artificially Intelligent Justice

Curated on Posted on by Stefaan Verhulst

Paper by Richard M. Re and Alicia Solow-Niederman: “Artificial intelligence, or AI, promises to assist, modify, and replace human decision-making, including in court. AI already supports many aspects of how judges decide cases, and the prospect of “robot judges” suddenly seems plausible—even imminent. This Article argues that AI adjudication will profoundly affect the adjudicatory values held by legal actors as well as the public at large. The impact is likely to be greatest in areas, including criminal justice and appellate decision-making, where “equitable justice,” or discretionary moral judgment, is frequently considered paramount. By offering efficiency and at least an appearance of impartiality, AI adjudication will both foster and benefit from a turn toward “codified justice,” an adjudicatory paradigm that favors standardization above discretion. Further, AI adjudication will generate a range of concerns relating to its tendency to make the legal system more incomprehensible, data-based, alienating, and disillusioning. And potential responses, such as crafting a division of labor between human and AI adjudicators, each pose their own challenges. The single most promising response is for the government to play a greater role in structuring the emerging market for AI justice, but auspicious reform proposals would borrow several interrelated approaches. Similar dynamics will likely extend to other aspects of government, such that choices about how to incorporate AI in the judiciary will inform the future path of AI development more broadly….(More)”.

France Bans Judge Analytics, 5 Years In Prison For Rule Breakers

Curated on Posted on by Stefaan Verhulst

Artificial Lawyer: “In a startling intervention that seeks to limit the emerging litigation analytics and prediction sector, the French Government has banned the publication of statistical information about judges’ decisions – with a five year prison sentence set as the maximum punishment for anyone who breaks the new law.

Owners of legal tech companies focused on litigation analytics are the most likely to suffer from this new measure.

The new law, encoded in Article 33 of the Justice Reform Act, is aimed at preventing anyone – but especially legal tech companies focused on litigation prediction and analytics – from publicly revealing the pattern of judges’ behaviour in relation to court decisions.

A key passage of the new law states:

‘The identity data of magistrates and members of the judiciary cannot be reused with the purpose or effect of evaluating, analysing, comparing or predicting their actual or alleged professional practices.’ *

As far as Artificial Lawyer understands, this is the very first example of such a ban anywhere in the world.

Insiders in France told Artificial Lawyer that the new law is a direct result of an earlier effort to make all case law easily accessible to the general public, which was seen at the time as improving access to justice and a big step forward for transparency in the justice sector.

However, judges in France had not reckoned on NLP and machine learning companies taking the public data and using it to model how certain judges behave in relation to particular types of legal matter or argument, or how they compare to other judges.

In short, they didn’t like how the pattern of their decisions – now relatively easy to model – were potentially open for all to see.

Unlike in the US and the UK, where judges appear to have accepted the fait accompli of legal AI companies analysing their decisions in extreme detail and then creating models as to how they may behave in the future, French judges have decided to stamp it out….(More)”.

Beyond Bias: Re-Imagining the Terms of ‘Ethical AI’ in Criminal Law

Curated on Posted on by Stefaan Verhulst

Paper by Chelsea Barabas: “Data-driven decision-making regimes, often branded as “artificial intelligence,” are rapidly proliferating across the US criminal justice system as a means of predicting and managing the risk of crime and addressing accusations of discriminatory practices. These data regimes have come under increased scrutiny, as critics point out the myriad ways that they can reproduce or even amplify pre-existing biases in the criminal justice system. This essay examines contemporary debates regarding the use of “artificial intelligence” as a vehicle for criminal justice reform, by closely examining two general approaches to, what has been widely branded as, “algorithmic fairness” in criminal law: 1) the development of formal fairness criteria and accuracy measures that illustrate the trade-offs of different algorithmic interventions and 2) the development of “best practices” and managerialist standards for maintaining a baseline of accuracy, transparency and validity in these systems.

The essay argues that attempts to render AI-branded tools more accurate by addressing narrow notions of “bias,” miss the deeper methodological and epistemological issues regarding the fairness of these tools. The key question is whether predictive tools reflect and reinforce punitive practices that drive disparate outcomes, and how data regimes interact with the penal ideology to naturalize these practices. The article concludes by calling for an abolitionist understanding of the role and function of the carceral state, in order to fundamentally reformulate the questions we ask, the way we characterize existing data, and how we identify and fill gaps in existing data regimes of the carceral state….(More)”