Machine learning (ML) 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 ML 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.

中文翻译：

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机器人学习中的公平与偏见


机器学习（ML）显着增强了机器人的能力，使它们能够在人类环境中执行各种任务并适应我们不确定的现实世界。最近各个机器学习领域的工作都强调了考虑公平性的重要性，以确保这些算法不会重现人类偏见，从而导致歧视性结果。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，了解这种偏见的影响对于防止针对某些人群的意外行为至关重要。在这项工作中，我们从跨越技术、伦理和法律挑战的跨学科角度提出了关于机器人学习公平性的第一份调查。我们提出了对偏见来源和由此产生的歧视类型的分类。使用来自不同机器人学习领域的示例，我们研究了不公平结果的场景以及缓解它们的策略。我们通过涵盖不同的公平定义、伦理和法律考虑以及公平机器人学习的方法来展示该领域的早期进展。通过这项工作，我们的目标是为公平机器人学习的突破性发展铺平道路。