FairnessBiasExplainabilitycs.CY
Responsible AI by Design in Practice
Richard Benjamins, Alberto Barbado, Daniel Sierra
September 27, 2019
Key Takeaways
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Identifying AI risks is easier than solving them: While there is broad expert consensus on major AI problems—such as unfair bias and lack of explainability—organizations struggle significantly with how to actually address these issues in practice, both technically and organizationally.
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Company-wide methodology is essential: Rather than addressing AI risks in isolated projects, the paper demonstrates that large organizations need systematic, company-wide approaches to minimize undesired consequences of AI deployment at scale.
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Both technical and organizational solutions are needed: Responsible AI requires more than just technical fixes; organizations must also implement organizational changes and processes to effectively manage AI risks across their operations.
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Real-world implementation experience is scarce: The paper highlights a gap between theoretical understanding of AI risks and practical experience in deploying solutions, suggesting that case studies from organizations actively implementing responsible AI practices are valuable for the broader field.
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.