Algorithms frequently assist, rather than replace, human decision-makers.
However, the design and analysis of algorithms often focus on predicting
outcomes and do not explicitly model their effect on human decisions. This
discrepancy between the design and role of algorithmic assistants becomes of
particular concern in light of empirical evidence that suggests that
algorithmic assistants again and again fail to improve human decisions. In this
article, we formalize the design of recommendation algorithms that assist human
decision-makers without making restrictive ex-ante assumptions about how
recommendations affect decisions. We formulate an algorithmic-design problem
that leverages the potential-outcomes framework from causal inference to model
the effect of recommendations on a human decision-maker's binary treatment
choice. Within this model, we introduce a monotonicity assumption that leads to
an intuitive classification of human responses to the algorithm. Under this
monotonicity assumption, we can express the human's response to algorithmic
recommendations in terms of their compliance with the algorithm and the
decision they would take if the algorithm sends no recommendation. We showcase
the utility of our framework using an online experiment that simulates a hiring
task. We argue that our approach explains the relative performance of different
recommendation algorithms in the experiment, and can help design solutions that
realize human-AI complementarity.

通过利用潜在结果的框架，我们在不限制诸如建议对决策的影响的条件下，规范了协助人类决策者的推荐算法的设计，并引入了一个单调性假设，该假设导致了对算法的人类响应的直观分类， 我们通过在线实验展示了我们框架的效用，并认为我们的方法可以解释实验中不同推荐算法的相对性能，并可以帮助设计实现人工智能与人类的互补性的解决方案。

设计算法推荐以实现人工智能与人类的互补性

Designing Algorithmic Recommendations to Achieve Human-AI  Complementarity

It is known that recommendations of AI-based systems can be incorrect or
unfair. Hence, it is often proposed that a human be the final decision-maker.
Prior work has argued that explanations are an essential pathway to help human
decision-makers enhance decision quality and mitigate bias, i.e., facilitate
human-AI complementarity. For these benefits to materialize, explanations
should enable humans to appropriately rely on AI recommendations and override
the algorithmic recommendation when necessary to increase distributive fairness
of decisions. The literature, however, does not provide conclusive empirical
evidence as to whether explanations enable such complementarity in practice. In
this work, we (a) provide a conceptual framework to articulate the
relationships between explanations, fairness perceptions, reliance, and
distributive fairness, (b) apply it to understand (seemingly) contradictory
research findings at the intersection of explanations and fairness, and (c)
derive cohesive implications for the formulation of research questions and the
design of experiments.

研究人员探讨了 AI 系统推荐错误或不公平的情况下，人工干预的必要性，并提供了关于解释、公平感知、依赖和分布公平性之间关系的理论框架，但文献并没有提供确凿证据证明解释在实践中实现了这种补充关系。