Many sets of ethics principles for responsible AI have been proposed to allay
concerns about misuse and abuse of AI/ML systems. The underlying aspects of
such sets of principles include privacy, accuracy, fairness, robustness,
explainability, and transparency. However, there are potential tensions between
these aspects that pose difficulties for AI/ML developers seeking to follow
these principles. For example, increasing the accuracy of an AI/ML system may
reduce its explainability. As part of the ongoing effort to operationalise the
principles into practice, in this work we compile and discuss a catalogue of 10
notable tensions, trade-offs and other interactions between the underlying
aspects. We primarily focus on two-sided interactions, drawing on support
spread across a diverse literature. This catalogue can be helpful in raising
awareness of the possible interactions between aspects of ethics principles, as
well as facilitating well-supported judgements by the designers and developers
of AI/ML systems.

本文讨论了负责任的人工智能伦理原则的多个方面，并提出了 10 个值得关注的潜在矛盾、权衡和其他交互作用。通过综述文献资料，重点分析了双向交互作用，并提供了一份有助于设计人员和开发人员了解伦理原则各方面相互关系的指南。

实现负责任 AI：伦理方面的紧张和权衡

Implementing Responsible AI: Tensions and Trade-Offs Between Ethics  Aspects

Responsible AI has been widely considered as one of the greatest scientific
challenges of our time and the key to increase the adoption of AI. A number of
AI ethics principles frameworks have been published recently. However, without
further best practice guidance, practitioners are left with nothing much beyond
truisms. Also, significant efforts have been placed at algorithm-level rather
than system-level, mainly focusing on a subset of mathematics-amenable ethical
principles (such as fairness). Nevertheless, ethical issues can occur at any
step of the development lifecycle crosscutting many AI and non-AI components of
systems beyond AI algorithms and models. To operationalize responsible AI from
a system perspective, in this paper, we present a Responsible AI Pattern
Catalogue based on the results of a Multivocal Literature Review (MLR). Rather
than staying at the principle or algorithm level, we focus on patterns that AI
system stakeholders can undertake in practice to ensure that the developed AI
systems are responsible throughout the entire governance and engineering
lifecycle. The Responsible AI Pattern Catalogue classifies the patterns into
three groups: multi-level governance patterns, trustworthy process patterns,
and responsible-AI-by-design product patterns. These patterns provide a
systematic and actionable guidance for stakeholders to implement responsible
AI.

本文提供了一种基于多声学文献综述 (Multivocal Literature Review，MLR) 结果的负责任 AI 模式目录，该目录系统地提供了适用于 AI 系统利益相关者的模式，以确保开发的 AI 系统在整个治理和工程生命周期内负责任。