Fairness and Bias in Robot Learning

• URL: http://arxiv.org/abs/2207.03444v2
• Date: Sun, 29 Oct 2023 18:12:25 GMT
• Title: Fairness and Bias in Robot Learning
• Authors: Laura Londo\~no, Juana Valeria Hurtado, Nora Hertz, Philipp Kellmeyer, Silja Voeneky, Abhinav Valada
• Abstract summary: 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. We present early advances in the field by covering different fairness definitions, ethical and legal considerations, and methods for fair robot learning.
• Score: 7.517692820105885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics [53.33976793493801]
  We organized the Robot Air Hockey Challenge at the NeurIPS 2023 conference. We focus on practical challenges in robotics, such as the sim-to-real gap, low-level control issues, safety problems, real-time requirements, and the limited availability of real-world data. Results show that solutions combining learning-based approaches with prior knowledge outperform those relying solely on data when real-world deployment is challenging.
  arXiv  Detail & Related papers  (2024-11-08T17:20:47Z)
• On the Effect of Robot Errors on Human Teaching Dynamics [1.7249361224827533]
  We investigate how the presence and severity of robot errors affect three dimensions of human teaching dynamics. Results show that people tend to spend more time teaching robots with errors. Our findings offer valuable insights for designing effective interfaces for interactive learning.
  arXiv  Detail & Related papers  (2024-09-15T19:02:34Z)
• A Survey of Embodied Learning for Object-Centric Robotic Manipulation [27.569063968870868]
  Embodied learning for object-centric robotic manipulation is a rapidly developing and challenging area in AI. Unlike data-driven machine learning methods, embodied learning focuses on robot learning through physical interaction with the environment.
  arXiv  Detail & Related papers  (2024-08-21T11:32:09Z)
• Aligning Robot and Human Representations [50.070982136315784]
  We argue that current representation learning approaches in robotics should be studied from the perspective of how well they accomplish the objective of representation alignment. We mathematically define the problem, identify its key desiderata, and situate current methods within this formalism.
  arXiv  Detail & Related papers  (2023-02-03T18:59:55Z)
• Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning [121.9708998627352]
  Recent work has shown that, in practical robot learning applications, the effects of adversarial training do not pose a fair trade-off. This work revisits the robustness-accuracy trade-off in robot learning by analyzing if recent advances in robust training methods and theory can make adversarial training suitable for real-world robot applications.
  arXiv  Detail & Related papers  (2022-04-15T08:12:15Z)
• From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence [113.06484656032978]
  Article argues that embodied intelligence is a key driver for the advancement of machine learning technology. We highlight challenges and opportunities specific to embodied intelligence. We propose research directions which may significantly advance the state-of-the-art in robot learning.
  arXiv  Detail & Related papers  (2021-10-28T16:04:01Z)
• Auditing Robot Learning for Safety and Compliance during Deployment [4.742825811314168]
  We study how best to audit robot learning algorithms for checking their compatibility with humans. We believe that this is a challenging problem that will require efforts from the entire robot learning community.
  arXiv  Detail & Related papers  (2021-10-12T02:40:11Z)
• Lifelong Robotic Reinforcement Learning by Retaining Experiences [61.79346922421323]
  Many multi-task reinforcement learning efforts assume the robot can collect data from all tasks at all times. In this work, we study a practical sequential multi-task RL problem motivated by the practical constraints of physical robotic systems. We derive an approach that effectively leverages the data and policies learned for previous tasks to cumulatively grow the robot's skill-set.
  arXiv  Detail & Related papers  (2021-09-19T18:00:51Z)
• From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation [3.3511723893430476]
  We argue that social navigation models can replicate, promote, and amplify societal unfairness such as discrimination and segregation. Our proposed framework consists of two components: textitlearning which incorporates social context into the learning process to account for safety and comfort, and textitrelearning to detect and correct potentially harmful outcomes before the onset.
  arXiv  Detail & Related papers  (2021-01-07T17:42:35Z)
• Hierarchical Affordance Discovery using Intrinsic Motivation [69.9674326582747]
  We propose an algorithm using intrinsic motivation to guide the learning of affordances for a mobile robot. This algorithm is capable to autonomously discover, learn and adapt interrelated affordances without pre-programmed actions. Once learned, these affordances may be used by the algorithm to plan sequences of actions in order to perform tasks of various difficulties.
  arXiv  Detail & Related papers  (2020-09-23T07:18:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.