机器人学习中的公平性与偏见
Fairness and Bias in Robot Learning
AI总结：
本文首次从技术、伦理和法律跨学科视角综述机器人学习中的公平性问题，提出偏见来源与歧视类型的分类法，并梳理公平性定义与公平学习方法，为该领域发展奠定基础。
摘要：
机器学习显著增强了机器人的能力，使其能够在人类环境中执行广泛的任务，并适应我们不确定的现实世界。近期各个机器学习领域的研究都强调了考虑公平性的重要性，以确保这些算法不会复制人类偏见，从而产生歧视性结果。随着机器人学习系统在我们的日常生活中执行越来越多的任务，理解此类偏见的影响以防止对某些人群产生非预期行为至关重要。在这项工作中，我们从跨学科视角出发，首次对机器人学习中的公平性进行了综述，涵盖技术、伦理和法律挑战。我们提出了一种针对偏见来源及其所导致歧视类型的分类法。通过来自不同机器人学习领域的示例，我们考察了不公平结果的场景以及缓解这些结果的策略。我们通过介绍不同的公平性定义、伦理和法律考量以及公平机器人学习的方法，展示了该领域的早期进展。通过这项工作，我们旨在为公平机器人学习的突破性发展铺平道路。
英文摘要：
Machine learning has significantly enhanced the abilities of robots, enabling them to perform a wide range of tasks in human environments and adapt to our uncertain real world. Recent works in various machine learning domains have highlighted the importance of accounting for fairness to ensure that these algorithms do not reproduce human biases and consequently lead to discriminatory outcomes. With robot learning systems increasingly performing more and more tasks in our everyday lives, it is crucial to understand the influence of such biases to prevent unintended behavior toward certain groups of people. In this work, we present the first survey on fairness in robot learning from an interdisciplinary perspective spanning technical, ethical, and legal challenges. We propose a taxonomy for sources of bias and the resulting types of discrimination due to them. Using examples from different robot learning domains, we examine scenarios of unfair outcomes and strategies to mitigate them. We present early advances in the field by covering different fairness definitions, ethical and legal considerations, and methods for fair robot learning. With this work, we aim to pave the road for groundbreaking developments in fair robot learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
