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This paper addresses the importance of situating explainable AI approaches within human social interactions to improve model transparency. The paper focuses on the concept of “social transparency,” which incorporates the context of those social interactions into explanations of AI systems. Interviews with AI users and practitioners ground the paper’s offering of a conceptual framework for identifying and measuring social transparency in order to improve AI decision making, increasing trust in AI, and nurturing broader values of AI explainability.

Uphol Ehsan et al.

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This article reports on an investigation by a team of journalists that found that applicants of color were significantly more likely than White applicants to be denied home loans based on nationwide 2019 data. In an analysis of more than 2 million loan applications, this disparity ranged from 40% more likely for Latino applicants to 80% more likely for Black applicants, despite comparable metrics and credit scores. Given these findings, the article questions the use of traditional credit scoring models and automated underwriting systems.

Emmanuel Martinez and Lauren Kirchner, The Markup
August 25, 2021

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This article identifies and explores a gap between commonly used statistical measures of fairness and rulings and evidentiary standards of the European Court of Justice. The authors suggest that current standards to bring discrimination claims limit the potential for a standardized system of addressing algorithmic discrimination in the EU because they are too contextual and open to interpretation. Additionally, the authors argue that law provides little guidance on addressing cases when algorithms, not humans, are the discriminators. The authors propose conditional demographic disparity as an appropriate statistical measure of fairness to harmonize legal and industry perspectives.

Sandra Wachter, Brent Mittelstadt, & Chris Russell, Computer L. & Security Rev.

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This report incorporates analysis of existing literature and interviews with experts and various stakeholders to determine how automated systems can best support and include traditionally marginalized populations. The report focuses on the problem of algorithmic bias embedded in data and systems. The report proposes a “Digital Bill of Rights” that articulates seven core rights designed to ensure that systems meet expectations for fairness, accountability, and transparency.

Kristine Gloria, Aspen Digital

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This article focuses on the role of open-source software (OSS) in the adoption and use of AI and machine learning and argues that this critical infrastructure is not subject to adequate oversight. The significance of OSS is clear: it helps speed AI adoption, reduce AI bias through means such as open-source explainable AI tools, and can improve tech sector competitiveness. However, OSS tools also carry risks, namely reducing competitiveness and giving a small number of technology companies an outsized role in determining AI standards. This paper contends that increasing oversight of OSS tools is a critical step in emerging efforts to define fair and responsible use of AI.

Alex Engler, Brookings

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This paper surveys existing and emerging frameworks for AI governance and points to common emphases with respect to reliability, transparency, accountability, and fairness. The authors argue that use of AI has intensified concerns about fairness and call for development of more specific and comprehensive standards by national, regional, and global standard-setting bodies to define fairness and to clarify the role of human intervention in the development and use of AI models in the financial system.

Jermy Prenio & Jeffery Yong, FSI Insights