Software Engineering for Responsible AI: An Empirical Study and Operationalised Patterns

• URL: http://arxiv.org/abs/2111.09478v1
• Date: Thu, 18 Nov 2021 02:18:27 GMT
• Title: Software Engineering for Responsible AI: An Empirical Study and Operationalised Patterns
• Authors: Qinghua Lu, Liming Zhu, Xiwei Xu, Jon Whittle, David Douglas, Conrad Sanderson
• Abstract summary: We propose a template that enables AI ethics principles to be operationalised in the form of concrete patterns. These patterns provide concrete, operationalised guidance that facilitate the development of responsible AI systems.
• Score: 20.747681252352464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although artificial intelligence (AI) is solving real-world challenges and transforming industries, there are serious concerns about its ability to behave and make decisions in a responsible way. Many AI ethics principles and guidelines for responsible AI have been recently issued by governments, organisations, and enterprises. However, these AI ethics principles and guidelines are typically high-level and do not provide concrete guidance on how to design and develop responsible AI systems. To address this shortcoming, we first present an empirical study where we interviewed 21 scientists and engineers to understand the practitioners' perceptions on AI ethics principles and their implementation. We then propose a template that enables AI ethics principles to be operationalised in the form of concrete patterns and suggest a list of patterns using the newly created template. These patterns provide concrete, operationalised guidance that facilitate the development of responsible AI systems.

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