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On the meaning of uncertainty for ethical AI: philosophy and practice

arXiv 2309.05529 ↗First posted 11 Sep 2023cs.AI math.ST stat.THcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 65 references that are in the map. First posted on arXiv 11 Sep 2023.

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16 of 65 references.

References · 16

  1. 2013A General Framework for Updating Belief Distributionscited by 175
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  4. 2021Robust Generalised Bayesian Inference for Intractable Likelihoodscited by 53
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  6. 2020Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriorscited by 29
  7. 2018Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with β-Divergencescited by 21
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  12. 2022Generalised Bayesian Inference for Discrete Intractable Likelihoodcited by 14
4 more
  1. 2021Deep Gaussian Process Emulation using Stochastic Imputationcited by 11
  2. 2022Uncertainty-aware deep learning methods for robust diabetic retinopathy classificationcited by 8
  3. 2023On the Stability of General Bayesian Inferencecited by 4
  4. 2015Posterior Belief Assessment: Extracting Meaningful Subjective Judgements from Bayesian Analyses with Complex Statistical Modelscited by 1

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