A relationship and not a thing: A relational approach to algorithmic accountability and assessment documentation

• URL: http://arxiv.org/abs/2203.01455v1
• Date: Wed, 2 Mar 2022 23:22:03 GMT
• Title: A relationship and not a thing: A relational approach to algorithmic accountability and assessment documentation
• Authors: Jacob Metcalf, Emanuel Moss, Ranjit Singh, Emnet Tafese, Elizabeth Anne Watkins
• Abstract summary: We argue that developers largely have a monopoly on information about how their systems actually work. We argue that robust accountability regimes must establish opportunities for publics to cohere around shared experiences and interests.
• Score: 3.4438724671481755
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Central to a number of scholarly, regulatory, and public conversations about algorithmic accountability is the question of who should have access to documentation that reveals the inner workings, intended function, and anticipated consequences of algorithmic systems, potentially establishing new routes for impacted publics to contest the operations of these systems. Currently, developers largely have a monopoly on information about how their systems actually work and are incentivized to maintain their own ignorance about aspects of how their systems affect the world. Increasingly, legislators, regulators and advocates have turned to assessment documentation in order to address the gap between the public's experience of algorithmic harms and the obligations of developers to document and justify their design decisions. However, issues of standing and expertise currently prevent publics from cohering around shared interests in preventing and redressing algorithmic harms; as we demonstrate with multiple cases, courts often find computational harms non-cognizable and rarely require developers to address material claims of harm. Constructed with a triadic accountability relationship, algorithmic impact assessment regimes could alter this situation by establishing procedural rights around public access to reporting and documentation. Developing a relational approach to accountability, we argue that robust accountability regimes must establish opportunities for publics to cohere around shared experiences and interests, and to contest the outcomes of algorithmic systems that affect their lives. Furthermore, algorithmic accountability policies currently under consideration in many jurisdictions must provide the public with adequate standing and opportunities to access and contest the documentation provided by the actors and the judgments passed by the forum.

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