Negligent Discrimination

Oppenheimer, David B.

doi:10.2307/3312446

Cited by 10 publications

(5 citation statements)

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“…Notes 2 This is the application of studies in the field of cognitive psychology to the area of prohibition of discrimination. See Lawrence 1987;Oppenheimer 1993;Krieger 1995;Wax 1999;and Blasi 2002. 3 By the term "advertisement," we refer to commercial advertisements, that is, messages that are intended to persuade people to buy a product.…”

Section: Explainsmentioning

confidence: 99%

Advertisements, Stereotypes, and Freedom of Expression

Cohen-Eliya¹,

Hammer²

2004

Journal of Social Philosophy

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Section: Explainsmentioning

confidence: 99%

Advertisements, Stereotypes, and Freedom of Expression

Cohen-Eliya¹,

Hammer²

2004

Journal of Social Philosophy

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“…The extent to which an AI system's behaviour is perceived as ethical also depends on the input data, which could be incomplete or biased (Kim, 2017), and the context in which the system is applied (Lauer, 2020). Hence, the impact an AI system has on its environment is not always intuitive and may not be consistent over time.…”

Section: The Matrixmentioning

confidence: 99%

The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems

et al. 2023

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Organisations that design and deploy artificial intelligence (AI) systems increasingly commit themselves to high-level, ethical principles. However, there still exists a gap between principles and practices in AI ethics. One major obstacle organisations face when attempting to operationalise AI Ethics is the lack of a well-defined material scope. Put differently, the question to which systems and processes AI ethics principles ought to apply remains unanswered. Of course, there exists no universally accepted definition of AI, and different systems pose different ethical challenges. Nevertheless, pragmatic problem-solving demands that things should be sorted so that their grouping will promote successful actions for some specific end. In this article, we review and compare previous attempts to classify AI systems for the purpose of implementing AI governance in practice. We find that attempts to classify AI systems proposed in previous literature use one of three mental models: the Switch, i.e., a binary approach according to which systems either are or are not considered AI systems depending on their characteristics; the Ladder, i.e., a risk-based approach that classifies systems according to the ethical risks they pose; and the Matrix, i.e., a multi-dimensional classification of systems that take various aspects into account, such as context, input data, and decision-model. Each of these models for classifying AI systems comes with its own set of strengths and weaknesses. By conceptualising different ways of classifying AI systems into simple mental models, we hope to provide organisations that design, deploy, or regulate AI systems with the vocabulary needed to demarcate the material scope of their AI governance frameworks.

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“…To demonstrate legal compliance, auditors typically rely on technical methods for gathering evidence about the properties and impact AI systems have (Kim, 2017). Similarly, technical robustness and legal compliance are often prerequisites for considering an AI system ethical (Keyes et al, 2019).…”

Section: Technical Legal and Ethics-based Approachesmentioning

confidence: 99%

Auditing of AI: Legal, Ethical and Technical Approaches

Mökander

2023

DISO

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AI auditing is a rapidly growing field of research and practice. This review article, which doubles as an editorial to Digital Society’s topical collection on ‘Auditing of AI’, provides an overview of previous work in the field. Three key points emerge from the review. First, contemporary attempts to audit AI systems have much to learn from how audits have historically been structured and conducted in areas like financial accounting, safety engineering and the social sciences. Second, both policymakers and technology providers have an interest in promoting auditing as an AI governance mechanism. Academic researchers can thus fill an important role by studying the feasibility and effectiveness of different AI auditing procedures. Third, AI auditing is an inherently multidisciplinary undertaking, to which substantial contributions have been made by computer scientists and engineers as well as social scientists, philosophers, legal scholars and industry practitioners. Reflecting this diversity of perspectives, different approaches to AI auditing have different affordances and constraints. Specifically, a distinction can be made between technology-oriented audits, which focus on the properties and capabilities of AI systems, and process-oriented audits, which focus on technology providers’ governance structures and quality management systems. The next step in the evolution of auditing as an AI governance mechanism, this article concludes, should be the interlinking of these available—and complementary—approaches into structured and holistic procedures to audit not only how AI systems are designed and used but also how they impact users, societies and the natural environment in applied settings over time.

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Negligent Discrimination

Abstract: Law Review editors, Wendy Beetlestone andJoseph Chan, for their invaluable assistance and dedication; and to my wife Marcy Kates for her editing, encouragement, patience, support and love. 1 42 U.S.C. § § 2000e to-17 (1988).

Cited by 10 publications

References 0 publications

Advertisements, Stereotypes, and Freedom of Expression

Advertisements, Stereotypes, and Freedom of Expression

The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems

Auditing of AI: Legal, Ethical and Technical Approaches

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