Putting AI ethics to work: are the tools fit for purpose?

Ayling, Jacqui; Chapman, Adriane

doi:10.1007/s43681-021-00084-x

Cited by 93 publications

(60 citation statements)

References 62 publications

(78 reference statements)

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“…In previous work [19], the predictive algorithm was model-fit and analyzed based on US-city population levels. For comparison, Oakland, California is 55.93 square miles with a population of 440,646 giving a population density of 7,878.53/sq mi 3 .…”

Section: Realisticmentioning

confidence: 99%

A Data-driven analysis of the interplay between Criminological theory and predictive policing algorithms

Chapman

Grylls

Ugwudike

et al. 2022

2022 ACM Conference on Fairness, Accountability, and Transparency

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Previous studies have focused on the biases and feedback loops that occur in predictive policing algorithms. These studies show how systemically and institutionally biased data leads to these feedback loops when predictive policing algorithms are applied in real life.We take a step back, and show that the choice in algorithm can be embedded in a specific criminological theory, and that the choice of a model on its own even without biased data can create biased feedback loops. By synthesizing "historical" data, in which we control the relationships between crimes, location and time, we show that the current predictive policing algorithms create biased feedback loops even with completely random data. We then review the process of creation and deployment of these predictive systems, and highlight when good practices, such as fitting a model to data, "go bad" within the context of larger system development and deployment. Using best practices from previous work on assessing and mitigating the impact of new technologies, we highlight where the design of these algorithms has broken down. The study also found that multidisciplinary analysis of such systems is vital for uncovering these issues and shows that any study of equitable AI should involve a systematic and holistic analysis of their design rationalities. CCS Concepts: • Applied computing → Law, social and behavioral sciences; • Software and its engineering → Designing software; • Theory of computation → Models of computation.

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Section: Realisticmentioning

confidence: 99%

A Data-driven analysis of the interplay between Criminological theory and predictive policing algorithms

Chapman

Grylls

Ugwudike

et al. 2022

2022 ACM Conference on Fairness, Accountability, and Transparency

Self Cite

View full text Add to dashboard Cite

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“…In light of AI practitioners' needs for support in addressing the ethical dimensions of AI [22], technology companies, researchers at FAccT, AIES, CHI, and other venues, and others have developed numerous tools and resources to support that work, with many such resources taking the form of toolkits [17,31,33,41,43,54,60]. Several papers have performed systemic meta-reviews and empirical analyses of AI ethics toolkits [5,33,43,54]. Ayling and Chapman [5] perform a descriptive analysis of AI ethics toolkits, identifying stakeholder types common across toolkits, and stages in the organizational lifecycle at which various toolkits are applied.…”

Section: Introductionmentioning

confidence: 99%

“…Several papers have performed systemic meta-reviews and empirical analyses of AI ethics toolkits [5,33,43,54]. Ayling and Chapman [5] perform a descriptive analysis of AI ethics toolkits, identifying stakeholder types common across toolkits, and stages in the organizational lifecycle at which various toolkits are applied. Lee and Singh [33] take a normative look at six open source fairness toolkits, using surveys and interviews with practitioners to understand the strengths and weaknesses of these tools.…”

Section: Introductionmentioning

confidence: 99%

“…These claims help connect the toolkits' practices to a broader set of practices or frameworks that may be more widely accepted or understood, helping to legitimize the toolkits' perspectives and practices, and providing a useful tactical alignment between the toolkit and existing organizational practices and resources. 5 Several toolkits adopt the language of "responsible innovation. Other toolkits look to external laws and standards as a legitimate basis for action; ethics is thus conceptualized as complying and acting in accordance with the law.…”

mentioning

confidence: 99%

“…Other toolkits look to external laws and standards as a legitimate basis for action; ethics is thus conceptualized as complying and acting in accordance with the law. Audit-AI, a tool that measures discriminatory patterns in data and machine learning predictions, explicitly cites U.S. labor regulations set by the Equal Employment Opportunity 5 Perhaps surprisingly, almost none of the toolkits provide an explicit discussion of philosophical ethical frameworks. (While toolkits may implicitly draw on different ethical theories, our focus in this analysis is on the explicit theories, discourses, and frameworks that are referred to in the text of the toolkits and their supporting documentation).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics

Wong¹,

Madaio²,

Merrill³

2022

Preprint

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Numerous toolkits have been developed to support ethical AI development. However, toolkits, like all tools, encode assumptions in their design about what work should be done and how. In this paper, we conduct a qualitative analysis of 27 AI ethics toolkits to critically examine how the work of ethics is imagined and how it is supported by these toolkits. Specifically, we examine the discourses toolkits rely on when talking about ethical issues, who they imagine should do the work of ethics, and how they envision the work practices involved in addressing ethics. We find that AI ethics toolkits largely frame the work of AI ethics to be technical work for individual technical practitioners, despite calls for engaging broader sets of stakeholders in grappling with social aspects of AI ethics, and without contending with the organizational and political implications of AI ethics work in practice. Among all toolkits, we identify a mismatch between the imagined work of ethics and the support the toolkits provide for doing that work. We identify a lack of guidance around how to navigate organizational power dynamics as they relate to performing ethical work. We use these omissions to chart future work for researchers and designers of AI ethics toolkits.CCS Concepts: • Social and professional topics → Codes of ethics; Computing occupations; Computing organizations.

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Moderating manipulation: Demystifying extremist tactics for gaming the (regulatory) system

Mattheis,

Kingdon

2023

Policy & Internet

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Due to its ease of scalability and broad applicability, the use of artificial intelligence (AI) and machine learning in platform management has gained prominence. This has led to widespread debates about the use of deplatforming as the default tool for repeated or severe violations of terms or service. But technologically deterministic approaches are not infallible and can be predictable based on their actions. This opens the door for manipulation of media content and technological affordances to become tactical options for actors seeking to subvert regulation. Existing discussions often neglect the topic of manipulation of content, algorithms, or platform affordances as a primary aspect of the strategies used by extremists in relation to the difficulties of moderation from a policy perspective. This study argues that it is essential to understand how extremists and conspiracy theorists use manipulation tactics to ‘game’ the current policy, regulatory and legislative systems of content moderation. Developing approaches that attend to manipulation as a strategy and focus on platform and context‐specific tactics will generate more effective policies, platform rules, AI developments and moderation procedures. This study analyses and demystifies three primary tactics, which the authors categorize as numerology, borderlands and merchandising, regularly used by extremists online in their strategies to ‘game’ content moderation. We provide case examples from a variety of ideologies including far‐right, QAnon and male supremacism to highlight the tactics rather than ideological nature of such manipulation. We conclude with a discussion of how demystification processes could be incorporated into content moderation settings. This study contributes new insights about evasion tactics to the content moderation discussion and expands current understanding of how platforms can develop sociotechnical remedial measures.

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Putting AI ethics to work: are the tools fit for purpose?

Cited by 93 publications

References 62 publications

A Data-driven analysis of the interplay between Criminological theory and predictive policing algorithms

A Data-driven analysis of the interplay between Criminological theory and predictive policing algorithms

Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics

Moderating manipulation: Demystifying extremist tactics for gaming the (regulatory) system

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