Responsible AI by Design in Practice

TL;DR

This paper discusses a practical approach for organizations to implement responsible AI by designing systems that minimize risks like bias and lack of transparency, based on real-world organizational experience.

Contribution

It presents a company-wide methodology for responsible AI deployment, offering practical insights and lessons learned from a large organization's implementation.

Findings

Effective organizational strategies for responsible AI

Practical methods to reduce AI bias and unfairness

Guidelines for scalable AI risk management

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.