Counterfactuals and counterfactual reasoning underpin numerous techniques for
auditing and understanding artificial intelligence (AI) systems. The
traditional paradigm for counterfactual reasoning in this literature is the
interventional counterfactual, where hypothetical interventions are imagined
and simulated. For this reason, the starting point for causal reasoning about
legal protections and demographic data in AI is an imagined intervention on a
legally-protected characteristic, such as ethnicity, race, gender, disability,
age, etc. We ask, for example, what would have happened had your race been
different? An inherent limitation of this paradigm is that some demographic
interventions -- like interventions on race -- may not translate into the
formalisms of interventional counterfactuals. In this work, we explore a new
paradigm based instead on the backtracking counterfactual, where rather than
imagine hypothetical interventions on legally-protected characteristics, we
imagine alternate initial conditions while holding these characteristics fixed.
We ask instead, what would explain a counterfactual outcome for you as you
actually are or could be? This alternate framework allows us to address many of
the same social concerns, but to do so while asking fundamentally different
questions that do not rely on demographic interventions.

基于背推式反事实，我们探讨了一种新的范式，以固定法律保护特征的同时考虑替代初始条件，从而解释您作为现实或潜在自身的反事实结果。这种替代框架允许我们解决许多相同的社会问题，但在提出根本不依赖于人口统计学介入的不同问题。

公平和追索中的反事实推理的新范型

A New Paradigm for Counterfactual Reasoning in Fairness and Recourse

Collaborative filtering algorithms find useful patterns in rating and
consumption data and exploit these patterns to guide users to good items. Many
of the patterns in rating datasets reflect important real-world differences
between the various users and items in the data; other patterns may be
irrelevant or possibly undesirable for social or ethical reasons, particularly
if they reflect undesired discrimination, such as discrimination in publishing
or purchasing against authors who are women or ethnic minorities. In this work,
we examine the response of collaborative filtering recommender algorithms to
the distribution of their input data with respect to a dimension of social
concern, namely content creator gender. Using publicly-available book ratings
data, we measure the distribution of the genders of the authors of books in
user rating profiles and recommendation lists produced from this data. We find
that common collaborative filtering algorithms differ in the gender
distribution of their recommendation lists, and in the relationship of that
output distribution to user profile distribution.

通过公开的书籍评分数据，我们研究协同过滤推荐算法对于内容创作者性别分布的响应，发现常见的协同过滤算法在推荐书单的性别分配方面存在差异，并且与用户档案分布的关系也存在差异。