Machine Learning (ML) decision-making algorithms are now widely used in
predictive decision-making, for example, to determine who to admit and give a
loan. Their wide usage and consequential effects on individuals led the ML
community to question and raise concerns on how the algorithms differently
affect different people and communities. In this paper, we study fairness
issues that arise when decision-makers use models (proxy models) that deviate
from the models that depict the physical and social environment in which the
decisions are situated (intended models). We also highlight the effect of
obstacles on individual access and utilization of the models. To this end, we
formulate an Equity Framework that considers equal access to the model, equal
outcomes from the model, and equal utilization of the model, and
consequentially achieves equity and higher social welfare than current fairness
notions that aim for equality. We show how the three main aspects of the
framework are connected and provide an equity scoring algorithm and questions
to guide decision-makers towards equitable decision-making. We show how failure
to consider access, outcome, and utilization would exacerbate proxy gaps
leading to an infinite inequity loop that reinforces structural inequities
through inaccurate and incomplete ground truth curation. We, therefore,
recommend a more critical look at the model design and its effect on equity and
a shift towards equity achieving predictive decision-making models.

该论文研究了机器学习决策算法在预测决策中的使用，强调了公平问题以及如何实现公正决策，提出了一个平等框架并给出了平等评分算法和指导决策者的问题。

公平架构：超越预测结果均等的公平

The Equity Framework: Fairness Beyond Equalized Predictive Outcomes

In many predictive decision-making scenarios, such as credit scoring and
academic testing, a decision-maker must construct a model that accounts for
agents' propensity to "game" the decision rule by changing their features so as
to receive better decisions. Whereas the strategic classification literature
has previously assumed that agents' outcomes are not causally affected by their
features (and thus that strategic agents' goal is deceiving the
decision-maker), we join concurrent work in modeling agents' outcomes as a
function of their changeable attributes. As our main contribution, we provide
efficient algorithms for learning decision rules that optimize three distinct
decision-maker objectives in a realizable linear setting: accurately predicting
agents' post-gaming outcomes (prediction risk minimization), incentivizing
agents to improve these outcomes (agent outcome maximization), and estimating
the coefficients of the true underlying model (parameter estimation). Our
algorithms circumvent a hardness result of Miller et al. (2020) by allowing the
decision maker to test a sequence of decision rules and observe agents'
responses, in effect performing causal interventions through the decision
rules.

本文提出了在实现线性设置中优化三种不同决策者目标的决策规则的有效算法，包括准确预测智能体事后结果 (预测风险最小化)，激励智能体改进这些结果 (智能体结果最大化)，以及估计真实基础模型的系数 (参数估计)。其中的算法绕过了 Miller 等人的一个困难结果，允许决策者测试一系列决策规则并观察智能体的响应，从而通过决策规则实现因果干预。