We review practical challenges in building and deploying ethical AI at the
scale of contemporary industrial and societal uses. Apart from the purely
technical concerns that are the usual focus of academic research, the
operational challenges of inconsistent regulatory pressures, conflicting
business goals, data quality issues, development processes, systems integration
practices, and the scale of deployment all conspire to create new ethical
risks. Such ethical concerns arising from these practical considerations are
not adequately addressed by existing research results. We argue that a holistic
consideration of ethics in the development and deployment of AI systems is
necessary for building ethical AI in practice, and exhort researchers to
consider the full operational contexts of AI systems when assessing ethical
risks.

讨论建立和推广符合伦理 AI 的实践中存在的实际挑战，包括监管压力、冲突商业目标、数据质量、开发过程、系统集成实践和规模问题，认为要考虑 AI 系统的全面运营上下文，才能有效应对这些伦理风险。

超越公平度量：实践中的道路障碍和伦理人工智能挑战

Beyond Fairness Metrics: Roadblocks and Challenges for Ethical AI in  Practice

Recent research has helped to cultivate growing awareness that machine
learning systems fueled by big data can create or exacerbate troubling
disparities in society. Much of this research comes from outside of the
practicing data science community, leaving its members with little concrete
guidance to proactively address these concerns. This article introduces issues
of discrimination to the data science community on its own terms. In it, we
tour the familiar data mining process while providing a taxonomy of common
practices that have the potential to produce unintended discrimination. We also
survey how discrimination is commonly measured, and suggest how familiar
development processes can be augmented to mitigate systems' discriminatory
potential. We advocate that data scientists should be intentional about
modeling and reducing discriminatory outcomes. Without doing so, their efforts
will result in perpetuating any systemic discrimination that may exist, but
under a misleading veil of data-driven objectivity.

介绍数据科学社区应该关注机器学习和数据挖掘过程中潜在的歧视问题，探讨歧视的测量和开发过程中如何改进以减少系统潜在的歧视性，并呼吁数据科学家应该有意识地建模和减少歧视结果的产生。