[Paper Review] Fairness and Bias in Robot Learning

Laura Londoño, Juana Valeria Hurtado+4 more|arXiv (Cornell University)|Jul 7, 2022

Ethics and Social Impacts of AI4 citations

TL;DR

This survey presents the first interdisciplinary framework for fairness in robot learning, integrating technical, ethical, and legal perspectives. It introduces a four-level taxonomy of bias (data, model, implementation, socio-structural), proposes fairness-aware methods, and offers actionable guidelines to detect, mitigate, and prevent bias in robot learning systems to ensure equitable human-robot interaction.

ABSTRACT

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.

Motivation & Objective

• To address the growing concern of algorithmic bias in robot learning systems that may lead to discriminatory behavior toward specific human groups.
• To identify and categorize sources of bias across the robot learning lifecycle, from data collection to deployment.
• To integrate technical, ethical, and legal perspectives to develop a holistic framework for fairness-aware robot learning.
• To propose practical guidelines and mitigation strategies for developers to ensure socially responsible and inclusive robot behavior.
• To promote the adoption of fairness-aware practices in robot learning to enhance trust, safety, and societal acceptance.

Proposed method

• Proposes a four-level taxonomy of bias: data-level (data collection and processing), model-level (training and learning), implementation-level (deployment and operation), and socio-structural conditions.
• Categorizes sources of bias into societal, historical, measurement, and representation bias, linking each to specific stages of robot learning.
• Introduces fairness definitions, metrics, and mitigation techniques tailored to robot learning, including constrained optimization and online evaluation frameworks.
• Integrates ethical and legal considerations into the robot learning pipeline, emphasizing compliance with non-discrimination principles and human-centered design.
• Recommends the use of inclusive development teams and diverse training data to reduce representation bias and improve fairness.
• Proposes online evaluation and continuous monitoring systems to detect and correct unfair behaviors in real time during deployment.

Experimental results

Research questions

• RQ1 What are the primary sources and types of bias that can emerge in robot learning systems across different stages of development and deployment?
• RQ2 How can fairness be formally defined and measured in robot learning, particularly in human-robot interaction and decision-making contexts?
• RQ3 What technical, ethical, and legal strategies can be employed to detect, prevent, and mitigate bias in robot learning systems?
• RQ4 How can interdisciplinary collaboration improve the fairness and inclusivity of robot learning systems in real-world human-centered environments?
• RQ5 What role do socio-structural factors and team diversity play in shaping the fairness of robot learning outcomes?

Key findings

• Bias in robot learning arises from multiple sources—societal, historical, measurement, and representation—across all stages of the learning pipeline.
• The proposed four-level taxonomy enables systematic identification of bias at data, model, implementation, and socio-structural levels.
• Fairness-aware learning methods, including constrained optimization and online evaluation, can effectively reduce discriminatory behavior in robots.
• Inclusive development teams and diverse training data significantly reduce representation bias and improve fairness in robot behavior.
• Early detection and penalization of undesirable behaviors during training, such as disrespectful language, can be achieved through specialized learning frameworks.
• Continuous online evaluation and feedback loops enable robots to adapt ethically to novel and dynamic social contexts, enhancing long-term fairness and safety.