Whether and how data scientists, statisticians and modellers should be accountable for the AI systems they develop remains a controversial and highly debated topic, especially given the complexity of AI systems and the difficulties in comparing and synthesising competing claims arising from their deployment for data analysis. This paper proposes to address this issue by decreasing the opacity and heightening the accountability of decision making using AI systems, through the explicit acknowledgement of the statistical foundations that underpin their development and the ways in which these dictate how their results should be interpreted and acted upon by users. In turn, this enhances (1) the responsiveness of the models to feedback, (2) the quality and meaning of uncertainty on their outputs and (3) their transparency to evaluation. To exemplify this approach, we extend Posterior Belief Assessment to offer a route to belief ownership from complex and competing AI structures. We argue that this is a significant way to bring ethical considerations into mathematical reasoning, and to implement ethical AI in statistical practice. We demonstrate these ideas within the context of competing models used to advise the UK government on the spread of the Omicron variant of COVID-19 during December 2021.

翻译：数据科学家、统计学家和建模者是否以及如何为所开发的人工智能系统承担责任，仍是一个具有争议且广受讨论的话题，尤其是在人工智能系统复杂性高、难以比较和综合其用于数据分析时产生的相互竞争的主张的情况下。本文旨在通过明确承认支撑人工智能系统开发的统计基础，以及这些基础如何决定用户应如何解读其结果并采取行动，来降低决策过程的模糊性并提高问责性，从而解决这一问题。这进而增强了（1）模型对反馈的响应能力，（2）其输出不确定性的质量和意义，以及（3）其对评估的透明度。为阐明这一方法，我们将后验信念评估扩展至复杂且相互竞争的人工智能结构，提供一条通往信念所有权的路径。我们认为，这是将伦理考量融入数学推理、并在统计实践中实现伦理人工智能的重要途径。我们以2021年12月期间用于向英国政府建议奥密克戎变异株传播情况的竞争模型为背景，展示了这些思想。