Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspects

• URL: http://arxiv.org/abs/2304.08275v4
• Date: Fri, 6 Sep 2024 04:17:47 GMT
• Title: Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspects
• Authors: Conrad Sanderson, David Douglas, Qinghua Lu,
• Abstract summary: We focus on two-sided interactions, drawing on support spread across a diverse literature. This catalogue can be helpful in raising awareness of the possible interactions between aspects of ethics principles.
• Score: 21.133468554780404
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many sets of ethics principles for responsible AI have been proposed to allay concerns about misuse and abuse of AI/ML systems. The underlying aspects of such sets of principles include privacy, accuracy, fairness, robustness, explainability, and transparency. However, there are potential tensions between these aspects that pose difficulties for AI/ML developers seeking to follow these principles. For example, increasing the accuracy of an AI/ML system may reduce its explainability. As part of the ongoing effort to operationalise the principles into practice, in this work we compile and discuss a catalogue of 10 notable tensions, trade-offs and other interactions between the underlying aspects. We primarily focus on two-sided interactions, drawing on support spread across a diverse literature. This catalogue can be helpful in raising awareness of the possible interactions between aspects of ethics principles, as well as facilitating well-supported judgements by the designers and developers of AI/ML systems.

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