Data Justice in Practice: A Guide for Developers

Leslie, David; Katell, Michael; Aitken, Mhairi; Singh, Jatinder; Briggs, Morgan; Powell, Rosamund; Rincón, Cami; Perini, Antonella; Jayadeva, Smera; Burr, Christopher

doi:10.2139/ssrn.4080058

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…This means that the very acts of devising the statistical problem and of translating project goals into measurable proxies can introduce structural biases which may ultimately lead to discriminatory harm. [53] Likewise, at the Preprocessing & Feature Engineering stage, human decisions about how to group or separate input features (e.g. how to carve up categories of gender or ethnic groups) or about which input features to exclude altogether (e.g.…”

Section: Key Concept Model Developmentmentioning

confidence: 99%

AI Ethics and Governance in Practice: An Introduction

Leslie,

Rincon,

Briggs

et al. 2024

SSRN Journal

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Section: Key Concept Model Developmentmentioning

confidence: 99%

AI Ethics and Governance in Practice: An Introduction

Leslie,

Rincon,

Briggs

et al. 2024

SSRN Journal

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“…It can also help detect and mitigate discriminatory biases that may be buried within model architectures (Alikhademi et al, 2021;Zhao, Chen, et al, 2024;Zhou et al, 2020). Furnishing understandable and accessible explanations of the rationale behind system outputs can likewise help to establish the lawfulness of AI systems (e.g., their compliance with data protection law and equality law) (Chuang et al, 2024; ICO/Turing, 2020) as well as to ensure responsible and trustworthy implementation by system deployers, who are better equipped to grasp system capabilities, limitations, and flaws and to integrate system outputs into their own reasoning, judgment, and experience (ICO/Turing, 2020; Leslie, Rincón, et al, 2024). The provision of nontechnical, plain-language AI explanations also helps both to establish justified trust among impacted people and to ensure paths to actionable recourse for them when things go wrong (Ferrario & Loi, 2022;Liao et al, 2022;Luo & Specia, 2024).…”

Section: Risks From Model Scaling: Model Opacity and Complexitymentioning

confidence: 99%

Future Shock: Generative AI and the International AI Policy and Governance Crisis

Leslie,

Perini

2024

Harvard Data Science Review

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stakeholders from across industry, academia, government, and civil society, and from around the globe, had made concerted efforts to develop standards, policies, and governance mechanisms to ensure the ethical, responsible, and equitable production and use of AI systems.However, as we then show, despite these ostensibly supportive activities and background conditions, several primary drivers of future shock converged to produce an international AI policy and governance crisis in the wake of the dawning of the GenAI era. Such a crisis, we argue, was marked by the disconnect between the strengthening thrust of public concerns about the hazards posed by the hasty industrial scaling of GenAI and the absence of effectual regulatory mechanisms and needed policy interventions to address such hazards. In painting a broad-stroked picture of this crisis, we underscore two sets of contributing factors. First, there have been factors that have demonstrated the absence of various vital aspects of AI policy and governance capability and execution-and thus the absence of key preconditions for readiness and resilience in managing technological transformation. These include prevalent enforcement gaps in existing digital-and data-related laws (e.g., intellectual property and data protection statutes), a lack of regulatory AI capacity, democratic deficits in the production of standards for trustworthy AI, and widespread evasionary tactics of ethic washing and state-enabled deregulation.Second, there have been factors that have significantly contributed to the presence of a new scale and order of systemic-, societal-, and biospheric-level risks and harms. Chief among these were the closely connected dynamics of unprecedented scaling and centralization that emerged as both drivers and by-products of the GenAI revolution. We focus, in particular, on model scaling and industrial scaling. Whereas the scaling of data, model size, and compute were linked to the emergence of serious model intrinsic risks deriving from the unfathomability of training data, model opacity and complexity, emergent model capabilities, and exponentially expanding compute costs, the rapid industrialization of FMs and GenAI systems meant the onset of a new scale of systemic risks that spanned the social, political, economic, cultural, and natural ecosystems in which these systems were embedded. The brute-force commercialization of GenAI ushered in a new age of widespread exposure in which increasing numbers of impacted people and communities at large were made susceptible to the risks and harms issuing from model scaling and to new possibilities for misuse, abuse, and cascading system-level effects.Alongside these aspects of model scaling and industrial scaling, patterns of economic and geopolitical centralization only further intensified conditions of future shock. The steering and momentum of these scaling dynamics lay largely in the hands of a few large tech corporations, which essentially controlled the data, compute, and skills and knowledge infrastructures r...

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“…Verifying use of safety measures post-deployment. In safety-critical settings, regulators may want to ensure that safety measures, for example, output filters, are applied to AI models or their outputs (see, for example, Dong et al, 2024a;Leslie et al, 2024;Welbl et al, 2021). Enforcing this may require methods for auditing systems deployed in such domains to check that they do in fact have safeguards that meet these specifications.…”

Section: Verifiable Auditsmentioning

confidence: 99%

AI Governance Multi-stakeholder Convening

Greene

2022

The Oxford Handbook of AI Governance

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This chapter offers reflections and advice on AI ethics and governance from a year spent leading Partnership on AI’s multi-stakeholder Affective Computing and Ethics project, involving more than 200 engineers, scientists, lawyers, privacy and civil rights advocates, bioethicists, managers, executives, journalists, and government officials, mostly in the United States, United Kingdom, and European Union, in discussions about AI related to emotion and affect and its potential impacts on civil and human rights, with a goal of developing industry best practices and better technology policy. The author’s reflections from that year draw lessons on convening, multi-disciplinary collaboration, and affective computing and AI ethics and governance. The chapter offers a blueprint for creating the shared knowledge foundation non-technical participants need to apply their expertise. It presents question exploration as a tool for evaluating ethics risk and using “What is notice good for?” shows how a well-chosen question can serve as a catalyst for group or individual exploration of the issues, leading to insights and answers. It includes a curated list of 42 questions, catalysts for AI and ethics discussion for industry, university, news media, and policy teams; multi-disciplinary, multi-stakeholder AI ethics and governance convenings; and for individual writing and thinking, to take scholars and practitioners a step beyond or to the side of what they have been thinking about—about AI and ethics.

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Data Justice in Practice: A Guide for Developers

Cited by 3 publications

References 8 publications

AI Ethics and Governance in Practice: An Introduction

AI Ethics and Governance in Practice: An Introduction

Future Shock: Generative AI and the International AI Policy and Governance Crisis

AI Governance Multi-stakeholder Convening

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