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1
Introduction
2
Background
3
Gender
4
Eric Loomis
5
Broadus Goodman
6
Assumptions
7
Operationalizing target variables
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Choosing a model
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Thresholds
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Why is this happening
11
Robert Moses bridges
12
Procurement
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Policymaking
14
Interventions
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Case law
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Administrative law
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Arbitrary and capricious
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What clause should agencies adopt
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What are the options
20
Expertise
21
Contestability
22
Law Practice
23
No Voice
24
Inhouse
25
Government vs private sector
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Policy choice
27
Eric Corbett
28
Using technology to improve public participation
29
How can the public be prepared
Description:
Explore a keynote address from FAT* 2019 that delves into the concept of algorithmic responsibility through the lens of administrative law and design. Examine how speaker Deirdre Mulligan from the University of California, Berkeley, challenges the notion of algorithmic scapegoating and proposes fostering a culture of accountability. Investigate case studies involving gender bias, criminal justice, and urban planning to understand the implications of algorithmic decision-making. Analyze the role of policymaking, procurement processes, and legal frameworks in shaping responsible AI practices. Discover potential interventions, including the application of administrative law principles and the "arbitrary and capricious" standard. Consider the importance of expertise, contestability, and public participation in AI governance. Reflect on the challenges faced by legal practitioners and government agencies in addressing algorithmic bias. Gain insights into innovative approaches for improving public engagement in technology policy decisions. Read more

Beyond Algorithmic Scapegoating - Fostering Cultures of Algorithmic Responsibility Through Administrative Law and Design

Association for Computing Machinery (ACM)
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