This paper critically examines existing modes of participation in design practice and machine learning. Cautioning against 'participation-washing', it suggests that the ML community must become attuned to possibly exploitative and extractive forms of community involvement and shift away from the prerogatives of context-independent scalability.

pdf/participation-is-not-a-design-fix-for-machine-learning-2w48wimehv.pdf

Participation is not a Design Fix for Machine Learning

In 1996, Accountability in a Computerized Society [95] issued a clarion call concerning the erosion of accountability in society due to the ubiquitous delegation of consequential functions to computerized systems. Nissenbaum [95] described four barriers to accountability that computerization presented, which we revisit in relation to the ascendance of data-driven algorithmic systems—i.e., machine learning or artificial intelligence—to uncover new challenges for accountability that these systems present. Nissenbaum’s original paper grounded discussion of the barriers in moral philosophy; we bring this analysis together with recent scholarship on relational accountability frameworks and discuss how the barriers present difficulties for instantiating a unified moral, relational framework in practice for data-driven algorithmic systems. We conclude by discussing ways of weakening the barriers in order to do so.

/pdf/accountability-in-an-algorithmic-society-relationality-3nhjn8u6.pdf

Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning

Recent advances in diffusion models and large language models have underpinned a new generation of powerful and accessible tools, and some of the most publicly visible applications are for artistic endeavour. Such tools, however, provide little scope for deeper understanding of AI systems, while the growing public interest in them can eclipse notice of the vibrant community of artists who have long worked with other forms of AI. We explore the potential for AI Art – particularly work in which AI is both tool and topic – to facilitate public AI literacies and consider how tactics developed before the current generative AI boom have continued relevance today. We look at the strategies of critical AI artists to scaffold public understanding of AI and enhance legibility for non-experts. This paper also investigates how collaborations between artists and AI researchers and designers can illuminate key technical and social issues relevant to the development of AI. The study entailed workshops between three professional artists who work with AI and a cross-disciplinary set of academic participants. This paper reports on these workshops and presents the intentions and strategies expressed by the artists, as well as insights of relevance to the research community on public AI literacies. We find that critical AI art can link underlying technical systems to structural issues of power and facilitate experiential learning that is situated and embodied, valuing interpretation over explanation. The findings also demonstrate the importance of transdisciplinary conversations around art, ethics and the political economy of AI technologies and how these dialogues may feed into AI design processes.

AI in the Public Eye: Investigating Public AI Literacy Through AI Art

Power and information asymmetries between people and digital technology companies are further legitimized through contractual agreements that fail to provide meaningful consent and contestability. In particular, the Terms-of-Service (ToS) agreement, is a contract of adhesion where companies effectively set the terms and conditions of the contract. Whereas, ToS reinforce existing structural inequalities, we seek to enable an intersectional accountability mechanism grounded in the practice of algorithmic reparation. Building on existing critiques of ToS in the context of algorithmic systems, we return to the roots of contract theory by recentering notions of agency and mutual assent. We evolve a multipronged intervention we frame as the Terms-we-Serve-with (TwSw) social, computational, and legal framework. The TwSw is a new social imaginary centered on: (1) co-constitution of user agreements, through participatory mechanisms; (2) addressing friction, leveraging the fields of design justice and critical design in the production and resolution of conflict; (3) enabling refusal mechanisms, reflecting the need for a sufficient level of human oversight and agency including opting out; (4) complaint and algorithmic harms reporting, through a feminist studies lens and open-sourced computational tools; and (5) disclosure-centered mediation, to disclose, acknowledge, and take responsibility for harm, drawing on the field of medical law. We further inform our analysis through an exploratory design workshop with a South African gender-based violence reporting AI startup. We derive practical strategies for communities, technologists, and policy-makers to leverage a relational approach to algorithmic reparation and propose there's a need for a radical restructuring of the “take-it-or-leave-it” ToS agreement.

Terms-we-serve-with: Five dimensions for anticipating and repairing algorithmic harm

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. Much is at stake in the question of how impact assessment regimes structure the relationships between parties—who must report to whom is as important as what gets reported. In widely used sociological descriptions of how accountability is structured through institutions, an “actor” (i.e., the developer) is accountable to a “forum” (i.e., regulatory agencies) empowered to pass judgements on and demand changes from the actor or enforce sanctions. However, we contend a robust accountability relationship centering on assessment documentation requires a triadic relationship, wherein the forum is also accountable to another entity: the public. Typically, as is the case with environmental impact assessments, the public makes demands upon the forum’s judgements and procedures through the courts, thereby establishing a minimum standard of due diligence. 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.

pdf/a-relationship-and-not-a-thing-a-relational-approach-to-udii7eq5.pdf

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

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