Abstract

Objective

To investigate how the general public trades off explainability versus accuracy of artificial intelligence (AI) systems and whether this differs between healthcare and non-healthcare scenarios.

Materials and Methods

Citizens’ juries are a form of deliberative democracy eliciting informed judgment from a representative sample of the general public around policy questions. We organized two 5-day citizens’ juries in the UK with 18 jurors each. Jurors considered 3 AI systems with different levels of accuracy and explainability in 2 healthcare and 2 non-healthcare scenarios. Per scenario, jurors voted for their preferred system; votes were analyzed descriptively. Qualitative data on considerations behind their preferences included transcribed audio-recordings of plenary sessions, observational field notes, outputs from small group work and free-text comments accompanying jurors’ votes; qualitative data were analyzed thematically by scenario, per and across AI systems.

Results

In healthcare scenarios, jurors favored accuracy over explainability, whereas in non-healthcare contexts they either valued explainability equally to, or more than, accuracy. Jurors’ considerations in favor of accuracy regarded the impact of decisions on individuals and society, and the potential to increase efficiency of services. Reasons for emphasizing explainability included increased opportunities for individuals and society to learn and improve future prospects and enhanced ability for humans to identify and resolve system biases.

Conclusion

Citizens may value explainability of AI systems in healthcare less than in non-healthcare domains and less than often assumed by professionals, especially when weighed against system accuracy. The public should therefore be actively consulted when developing policy on AI explainability.

中文翻译：

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在 AI 决策中权衡准确性和可解释性：来自 2 个公民陪审团的调查结果

摘要

客观的

调查公众如何权衡人工智能 (AI) 系统的可解释性与准确性，以及这在医疗保健和非医疗保健场景之间是否存在差异。

材料和方法

公民陪审团是协商民主的一种形式，它围绕政策问题从具有代表性的公众样本中引出知情判断。我们在英国组织了两个为期 5 天的公民陪审团，每个陪审团有 18 名陪审员。陪审员在 2 个医疗保健和 2 个非医疗保健场景中考虑了 3 个具有不同准确性和可解释性水平的 AI 系统。在每种情况下，陪审员投票支持他们喜欢的系统；投票进行了描述性分析。关于他们偏好背后考虑因素的定性数据包括转录的全体会议录音、实地观察笔记、小组工作的成果和陪审员投票的自由文本评论；定性数据按场景、每个人工智能系统和跨人工智能系统进行主题分析。

结果

在医疗保健场景中，陪审员更喜欢准确性而不是可解释性，而在非医疗保健环境中，他们要么同等重视可解释性，要么高于准确性。陪审员对准确性的考虑考虑了决策对个人和社会的影响，以及提高服务效率的潜力。强调可解释性的原因包括增加个人和社会学习和改善未来前景的机会，以及增强人类识别和解决系统偏差的能力。

结论

与非医疗领域相比，公民可能更不重视医疗保健领域 AI 系统的可解释性，也没有专业人员通常认为的那样重视，尤其是在与系统准确性进行权衡时。因此，在制定人工智能可解释性政策时，应积极咨询公众。