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.

翻译：许多负责任人工智能的伦理原则集已被提出，以缓解对AI/ML系统误用和滥用的担忧。这些原则集的核心方面包括隐私、准确性、公平性、鲁棒性、可解释性和透明度。然而，这些方面之间存在潜在紧张关系，给试图遵循这些原则的AI/ML开发者带来困难。例如，提高AI/ML系统的准确性可能会降低其可解释性。作为将原则付诸实践的持续努力的一部分，本文整理并讨论了核心方面之间10种显著的紧张关系、权衡及其他相互作用。我们主要关注双向相互作用，并借鉴了分散在不同文献中的支持依据。该目录有助于提高对伦理原则各方面之间可能相互作用的认识，并为AI/ML系统的设计者和开发者做出有理有据的判断提供便利。