Controlling Counterfactual Harm in Decision Support Systems Based on Prediction Sets
控制基于预测集的决策支持系统中的反事实伤害AI 解读 ↓
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控制基于预测集的决策支持系统中的反事实伤害
- 研究问题
- 预测集限制人类可选标签时，如何控制本可独立答对却因使用系统而答错的反事实伤害发生频率？
- 方法与思路
- 作者用结构因果模型定义伤害，在两种单调性假设下分别估计或界定发生频率，再用共形风险控制设计系统。
- 材料陈述的贡献
- 摘要称提出条件性的伤害频率控制框架，并用两项人类实验数据验证，展示预测准确率与反事实伤害之间的权衡。
- 阅读关注点
- 应核对强弱单调性假设的含义及可检验性，区分伤害的点估计与界限，并查看共形风险保证所需的数据条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；强单调性不可验证，摘要也未说明风险保证的完整适用条件。
原文摘要
Decision support systems based on prediction sets help humans solve multiclass classification tasks by narrowing down the set of potential label values to a subset of them, namely a prediction set, and asking them to always predict label values from the prediction sets. While this type of systems have been proven to be effective at improving the average accuracy of the predictions made by humans, by restricting human agency, they may cause harm$\unicode{x2014}$a human who has succeeded at predicting the ground-truth label of an instance on their own may have failed had they used these systems. In this paper, our goal is to control how frequently a decision support system based on prediction sets may cause harm, by design. To this end, we start by characterizing the above notion of harm using the theoretical framework of structural causal models. Then, we show that, under a natural, albeit unverifiable, monotonicity assumption, we can estimate how frequently a system may cause harm using only predictions made by humans on their own. Further, we also show that, under a weaker monotonicity assumption, which can be verified experimentally, we can bound how frequently a system may cause harm again using only predictions made by humans on their own. Building upon these assumptions, we introduce a computational framework to design decision support systems based on prediction sets that are guaranteed to cause harm less frequently than a user-specified value using conformal risk control. We validate our framework using real human predictions from two different human subject studies and show that, in decision support systems based on prediction sets, there is a trade-off between accuracy and counterfactual harm.