Responsible AI by Design in Practice

Richard Benjamins · Alberto Barbado · Daniel Sierra

37 citations0/0 files storedarXiv (Cornell University)

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Ethics and Social Impacts of AI Explainable Artificial Intelligence (XAI)Safety Systems Engineering in Autonomy

Abstract

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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References

2014Big Data's Disparate Impact 2016Equality of Opportunity in Supervised Learning 2016Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy 2018Mitigating Unwanted Biases with Adversarial Learning 2011Data preprocessing techniques for classification without discrimination 2013Learning Fair Representations 2018Ethics in artificial intelligence: introduction to the special issue 2019FactSheets: Increasing trust in AI services through supplier's declarations of conformity

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2023Using artificial intelligence to advance sustainable development in industrial markets: A complex adaptive systems perspective 2021Towards intellectual freedom in an AI Ethics Global Community 2023Discerning Between the “Easy” and “Hard” Problems of AI Governance 2023A step toward building a unified framework for managing AI bias 2024Responsible AI (RAI) in Manufacturing: A Qualitative Framework

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