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：为缓解对人工智能/机器学习系统误用和滥用的担忧，业界已提出多套负责任人工智能的伦理原则。这些原则集的核心维度包括隐私性、准确性、公平性、鲁棒性、可解释性和透明度。然而，这些维度之间存在的潜在张力给遵循上述原则的AI/ML开发者带来挑战——例如，提升系统的准确性可能降低其可解释性。作为将伦理原则付诸实践的持续努力的一部分，本研究整理并讨论了10项核心维度间的显著张力、权衡关系及其他相互作用。我们主要聚焦于双向交互效应，系统梳理了跨学科文献中的支撑证据。该目录有助于提升对伦理原则维度间潜在交互作用的认知，同时为AI/ML系统设计者与开发者做出更充分的决策提供支持。