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Responsible AI by Design in Practice

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arxiv 1909.12838 v2 pith:IQ3TWGHE submitted 2019-09-27 cs.CY

classification cs.CY
keywords thereconsensusconsequencesexperienceissuesorganizationsundesiredanswer
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Recently, a lot of attention has been given to undesired consequences of Artificial Intelligence (AI), such as unfair bias leading to discrimination, or the lack of explanations of the results of AI systems. There are several important questions to answer before AI can be deployed at scale in our businesses and societies. Most of these issues are being discussed by experts and the wider communities, and it seems there is broad consensus on where they come from. There is, however, less consensus on, and experience with how to practically deal with those issues in organizations that develop and use AI, both from a technical and organizational perspective. In this paper, we discuss the practical case of a large organization that is putting in place a company-wide methodology to minimize the risk of undesired consequences of AI. We hope that other organizations can learn from this and that our experience contributes to making the best of AI while minimizing its risks.

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  1. Responsible Artificial Intelligence (RAI) in U.S. Federal Government : Principles, Policies, and Practices

    cs.CY 2025-01 unverdicted novelty 3.0 of 10

    A U.S. federal RAI policy review that maps executive orders, memos, and frameworks onto five RAI principles and describes Census Bureau implementation tools, with no new empirical findings.

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