Risk assessment instrument (RAI) datasets, particularly ProPublica's COMPAS
dataset, are commonly used in algorithmic fairness papers due to benchmarking
practices of comparing algorithms on datasets used in prior work. In many
cases, this data is used as a benchmark to demonstrate good performance without
accounting for the complexities of criminal justice (CJ) processes. However, we
show that pretrial RAI datasets can contain numerous measurement biases and
errors, and due to disparities in discretion and deployment, algorithmic
fairness applied to RAI datasets is limited in making claims about real-world
outcomes. These reasons make the datasets a poor fit for benchmarking under
assumptions of ground truth and real-world impact. Furthermore, conventional
practices of simply replicating previous data experiments may implicitly
inherit or edify normative positions without explicitly interrogating
value-laden assumptions. Without context of how interdisciplinary fields have
engaged in CJ research and context of how RAIs operate upstream and downstream,
algorithmic fairness practices are misaligned for meaningful contribution in
the context of CJ, and would benefit from transparent engagement with normative
considerations and values related to fairness, justice, and equality. These
factors prompt questions about whether benchmarks for intrinsically
socio-technical systems like the CJ system can exist in a beneficial and
ethical way.

利用风险评估工具数据集（RAI），特别是 ProPublica 的 COMPAS 数据集来比较算法性能是常见的，然而我们发现这些数据集可能会存在大量的测量偏差和错误，而且算法公平性应用于 RAI 数据集在对现实世界结果提出要求时具有局限性。此外，如何进行算法公平性实践是受到了对公平，正义和平等价值观的透明参与的影响，而且这些因素引出了一个问题，即这些在本质上是社会技术系统的基准是否可以以有益和合乎道德的方式存在。

COMPAS 繁琐：RAI 数据集与算法公平基准之间的混乱关系

It's COMPASlicated: The Messy Relationship between RAI Datasets and  Algorithmic Fairness Benchmarks

Formulating data science problems is an uncertain and difficult process. It
requires various forms of discretionary work to translate high-level objectives
or strategic goals into tractable problems, necessitating, among other things,
the identification of appropriate target variables and proxies. While these
choices are rarely self-evident, normative assessments of data science projects
often take them for granted, even though different translations can raise
profoundly different ethical concerns. Whether we consider a data science
project fair often has as much to do with the formulation of the problem as any
property of the resulting model. Building on six months of ethnographic
fieldwork with a corporate data science team---and channeling ideas from
sociology and history of science, critical data studies, and early writing on
knowledge discovery in databases---we describe the complex set of actors and
activities involved in problem formulation. Our research demonstrates that the
specification and operationalization of the problem are always negotiated and
elastic, and rarely worked out with explicit normative considerations in mind.
In so doing, we show that careful accounts of everyday data science work can
help us better understand how and why data science problems are posed in
certain ways---and why specific formulations prevail in practice, even in the
face of what might seem like normatively preferable alternatives. We conclude
by discussing the implications of our findings, arguing that effective
normative interventions will require attending to the practical work of problem
formulation.

探讨数据科学中问题制定的复杂性，指出问题制定中的规范评估常常被视为理所当然，然而这些评估可能会引发截然不同的伦理问题，并提出了实际问题制定工作的重要性。