Responsible AI by Design in Practice
arXiv:1909.12838
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.
arXiv comment9 pages, 3 tables. Proceedings of the Human-Centered AI: Trustworthiness of AI Models & Data (HAI) track at AAAI Fall Symposium, DC, November 7-9, 2019
References in corpus (6)
- Equality of Opportunity in Supervised Learning
- Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
- Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
- What Does Explainable AI Really Mean? A New Conceptualization of Perspectives
- Support Vector Machines under Adversarial Label Contamination
- Open the Black Box Data-Driven Explanation of Black Box Decision Systems