Recent years have seen growing interest among both researchers and
practitioners in user-engaged approaches to algorithm auditing, which directly
engage users in detecting problematic behaviors in algorithmic systems.
However, we know little about industry practitioners' current practices and
challenges around user-engaged auditing, nor what opportunities exist for them
to better leverage such approaches in practice. To investigate, we conducted a
series of interviews and iterative co-design activities with practitioners who
employ user-engaged auditing approaches in their work. Our findings reveal
several challenges practitioners face in appropriately recruiting and
incentivizing user auditors, scaffolding user audits, and deriving actionable
insights from user-engaged audit reports. Furthermore, practitioners shared
organizational obstacles to user-engaged auditing, surfacing a complex
relationship between practitioners and user auditors. Based on these findings,
we discuss opportunities for future HCI research to help realize the potential
(and the mitigate risks) of user-engaged auditing in industry practice.

该研究通过对从业人员的访谈和协作设计活动，揭示了从业人员在实践中招募、激励用户审核员的挑战，以及从用户审核报告中获取可操作洞见的难度，并讨论了未来人机交互研究的机遇。

用户参与的算法审计在工业实践中的实践、挑战和机遇

Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice

In recent years, discussions about fairness in machine learning, AI ethics
and algorithm audits have increased. Many entities have developed framework
guidance to establish a baseline rubric for fairness and accountability.
However, in spite of increased discussions and multiple frameworks, algorithm
and data auditing still remain difficult to execute in practice. Many
open-source auditing tools are available, but users aren't always aware of the
tools, what they are useful for, or how to access them. Model auditing and
evaluation are not frequently emphasized skills in machine learning. There are
also legal reasons for the proactive adoption of these tools that extend beyond
the desire for greater fairness in machine learning. There are positive social
issues of public perception and goodwill that matter in our highly connected
global society. Greater awareness of these tools and the reasons for actively
utilizing them may be helpful to the entire continuum of programmers, data
scientists, engineers, researchers, users and consumers of AI and machine
learning products. It is important for everyone to better understand the input
and output differentials, how they are occurring, and what can be done to
promote FATE (fairness, accountability, transparency, and ethics) in machine-
and deep learning. The ability to freely access open-source auditing tools
removes barriers to fairness assessment at the most basic levels of machine
learning. This paper aims to reinforce the urgent need to actually use these
tools and provides motivations for doing so. The exemplary tools highlighted
herein are open-source with software or code-base repositories available that
can be used immediately by anyone worldwide.

本文强调了在机器学习中使用公平性、责任性、透明度和伦理 (FATE) 工具的紧迫性，并提供了使用开源工具的动机。

适合工作的工具：机器学习中的开源审计工具

The Right Tool for the Job: Open-Source Auditing Tools in Machine Learning

Algorithmic decision making is now widespread, ranging from health care
allocation to more common actions such as recommendation or information
ranking. The aim to audit these algorithms has grown alongside. In this paper,
we focus on external audits that are conducted by interacting with the user
side of the target algorithm, hence considered as a black box. Yet, the legal
framework in which these audits take place is mostly ambiguous to researchers
developing them: on the one hand, the legal value of the audit outcome is
uncertain; on the other hand the auditors' rights and obligations are unclear.
The contribution of this paper is to articulate two canonical audit forms to
law, to shed light on these aspects: 1) the first audit form (we coin the Bobby
audit form) checks a predicate against the algorithm, while the second
(Sherlock) is more loose and opens up to multiple investigations. We find that:
Bobby audits are more amenable to prosecution, yet are delicate as operating on
real user data. This can lead to reject by a court (notion of admissibility).
Sherlock audits craft data for their operation, most notably to build
surrogates of the audited algorithm. It is mostly used for acts for
whistleblowing, as even if accepted as a proof, the evidential value will be
low in practice. 2) these two forms require the prior respect of a proper right
to audit, granted by law or by the platform being audited; otherwise the
auditor will be also prone to prosecutions regardless of the audit outcome.
This article thus highlights the relation of current audits with law, in order
to structure the growing field of algorithm auditing.

本文着重讨论两种标准的黑盒算法审计形式：第一种审计形式（命名为 Bobby 审计形式）检查算法的谓词，而第二种审计形式（Sherlock）则更松散地进行多项调查，为结构化黑盒算法审计领域与法律的关系提供了关键见解

算法审核、算法与法律

Algorithmic audits of algorithms, and the law

A growing body of literature has proposed formal approaches to audit
algorithmic systems for biased and harmful behaviors. While formal auditing
approaches have been greatly impactful, they often suffer major blindspots,
with critical issues surfacing only in the context of everyday use once systems
are deployed. Recent years have seen many cases in which everyday users of
algorithmic systems detect and raise awareness about harmful behaviors that
they encounter in the course of their everyday interactions with these systems.
However, to date little academic attention has been granted to these bottom-up,
user-driven auditing processes. In this paper, we propose and explore the
concept of everyday algorithm auditing, a process in which users detect,
understand, and interrogate problematic machine behaviors via their day-to-day
interactions with algorithmic systems. We argue that everyday users are
powerful in surfacing problematic machine behaviors that may elude detection
via more centrally-organized forms of auditing, regardless of users' knowledge
about the underlying algorithms. We analyze several real-world cases of
everyday algorithm auditing, drawing lessons from these cases for the design of
future platforms and tools that facilitate such auditing behaviors. Finally, we
discuss work that lies ahead, toward bridging the gaps between formal auditing
approaches and the organic auditing behaviors that emerge in everyday use of
algorithmic systems.

本文探讨了用户驱动的日常算法审计过程，强调此类审计过程可以揭示正式审计中忽略的机器问题，为未来的平台和工具设计提供了宝贵的经验教训。