An emerging body of research indicates that ineffective cross-functional
collaboration -- the interdisciplinary work done by industry practitioners
across roles -- represents a major barrier to addressing issues of fairness in
AI design and development. In this research, we sought to better understand
practitioners' current practices and tactics to enact cross-functional
collaboration for AI fairness, in order to identify opportunities to support
more effective collaboration. We conducted a series of interviews and design
workshops with 23 industry practitioners spanning various roles from 17
companies. We found that practitioners engaged in bridging work to overcome
frictions in understanding, contextualization, and evaluation around AI
fairness across roles. In addition, in organizational contexts with a lack of
resources and incentives for fairness work, practitioners often piggybacked on
existing requirements (e.g., for privacy assessments) and AI development norms
(e.g., the use of quantitative evaluation metrics), although they worry that
these tactics may be fundamentally compromised. Finally, we draw attention to
the invisible labor that practitioners take on as part of this bridging and
piggybacking work to enact interdisciplinary collaboration for fairness. We
close by discussing opportunities for both FAccT researchers and AI
practitioners to better support cross-functional collaboration for fairness in
the design and development of AI systems.

本研究旨在更好地了解业界从业人员在跨职能协同合作 AI 公平方面的现状和策略，以识别支持更有效协作的机会，并关注业界从业人员在此过程中承担的隐性劳动。

AI 公平性工业实践中的跨职能协作实践和机遇调查

Investigating Practices and Opportunities for Cross-functional  Collaboration around AI Fairness in Industry Practice

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 \citep{andreas2020task-oriented}, a dataflow (DF) based dialogue system
was introduced, showing clear advantages compared to many commonly used current
systems. This was accompanied by the release of SMCalFlow, a practically
relevant, manually annotated dataset, more detailed and much larger than any
comparable dialogue dataset. Despite these remarkable contributions, the
community has not shown further interest in this direction. What are the
reasons for this lack of interest? And how can the community be encouraged to
engage in research in this direction?
One explanation may be the perception that this approach is too complex -
both the the annotation and the system. This paper argues that this perception
is wrong: 1) Suggestions for a simplified format for the annotation of the
dataset are presented, 2) An implementation of the DF execution engine is
released\footnote{this https URL}, which can serve
as a sandbox allowing researchers to easily implement, and experiment with, new
DF dialogue designs. The hope is that these contributions will help engage more
practitioners in exploring new ideas and designs for DF based dialogue systems.

本文介绍了一种基于数据流的对话系统，并提供了一个注释数据集和程序执行引擎，旨在鼓励更多从业者探索基于数据流的对话系统的新思路和设计。

简化数据流对话设计

Simplifying Dataflow Dialogue Design

This report presents the debates, posters, and discussions of the Sim2Real
workshop held in conjunction with the 2020 edition of the "Robotics: Science
and System" conference. Twelve leaders of the field took competing debate
positions on the definition, viability, and importance of transferring skills
from simulation to the real world in the context of robotics problems. The
debaters also joined a large panel discussion, answering audience questions and
outlining the future of Sim2Real in robotics. Furthermore, we invited extended
abstracts to this workshop which are summarized in this report. Based on the
workshop, this report concludes with directions for practitioners exploiting
this technology and for researchers further exploring open problems in this
area.

本报告总结了在 2020 年 “机器人科学与系统” 会议上举办的 Sim2Real 研讨会上展开的辩论、海报展示和讨论。其中 12 位领域专家就仿真技术在机器人问题中的应用定义、可行性和重要性等问题展开了竞争性辩论。此外，本研讨会还吸引了多篇扩展摘要。基于研讨会的结果，本报告提出了未来 Sim2Real 技术的应用方向，为从业人员与研究者带来实用与启示。