Bias, Fairness & Ethics

Publications

1. Z. Zhou and M. Brandao, “Noise and Environmental Justice in Drone Fleet Delivery Paths: A Simulation-Based Audit and Algorithm for Fairer Impact Distribution,” in 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023. [Abstract] [Code] [PDF]

   Despite the growing interest in the use of drone fleets for delivery of food and parcels, the negative impact of such technology is still poorly understood. In this paper we investigate the impact of such fleets in terms of noise pollution and environmental justice. We use simulation with real population data to analyze the spatial distribution of noise, and find that: 1) noise increases rapidly with fleet size; and 2) drone fleets can produce noise hotspots that extend far beyond warehouses or charging stations, at levels that lead to annoyance and interference of human activities. This, we will show, leads to concerns of fairness of noise distribution. We then propose an algorithm that successfully balances the spatial distribution of noise across the city, and discuss the limitations of such purely technical approaches. We complement the work with a discussion of environmental justice, showing how careless UAV fleet development and regulation can lead to reinforcing well-being deficiencies of poor and marginalized communities.

2. M. E. Akintunde, M. Brandao, G. Jahangirova, H. Menendez, M. R. Mousavi, and J. Zhang, “On Testing Ethical Autonomous Decision-Making,” in Springer LNCS Festschrift dedicated to Jan Peleska’s 65th Birthday, 2023.
3. M. Brandao, M. Mansouri, and M. Magnusson, “Editorial: Responsible Robotics,” Frontiers in Robotics and AI, vol. 9, Jun. 2022. [DOI]
4. M. Brandao, “Normative roboticists: the visions and values of technical robotics papers,” in IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2021, pp. 671–677. [Abstract] [DOI] [PDF]

   Visions have an important role in guiding and legitimizing technical research, as well as contributing to expectations of the general public towards technologies. In this paper we analyze technical robotics papers published between 1998 and 2019 to identify themes, trends and issues with the visions and values promoted by robotics research. In particular, we identify the themes of robotics visions and implicitly normative visions; and we quantify the relative presence of a variety of values and applications within technical papers. We conclude with a discussion of the language of robotics visions, marginalized visions and values, and possible paths forward for the robotics community to better align practice with societal interest. We also discuss implications and future work suggestions for Responsible Robotics and HRI research.

5. M. Brandao, “Socially Fair Coverage: The Fairness Problem in Coverage Planning and a New Anytime-Fair Method,” in 2021 IEEE International Conference on Advanced Robotics and its Social Impacts (ARSO), 2021, pp. 227–233. [Abstract] [DOI] [PDF]

   In this paper we investigate and characterize social fairness in the context of coverage path planning. Inspired by recent work on the fairness of goal-directed planning, and work characterizing the disparate impact of various AI algorithms, here we simulate the deployment of coverage robots to anticipate issues of fairness. We show that classical coverage algorithms, especially those that try to minimize average waiting times, will have biases related to the spatial segregation of social groups. We discuss implications in the context of disaster response, and provide a new coverage planning algorithm that minimizes cumulative unfairness at all points in time. We show that our algorithm is 200 times faster to compute than existing evolutionary algorithms - while obtaining overall-faster coverage and a fair response in terms of waiting-time and coverage-pace differences across multiple social groups.

6. J. Grzelak and M. Brandao, “The Dangers of Drowsiness Detection: Differential Performance, Downstream Impact, and Misuses,” in AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2021. [Abstract] [DOI] [PDF]

   Drowsiness and fatigue are important factors in driving safety and work performance. This has motivated academic research into detecting drowsiness, and sparked interest in the deployment of related products in the insurance and work-productivity sectors. In this paper we elaborate on the potential dangers of using such algorithms. We first report on an audit of performance bias across subject gender and ethnicity, identifying which groups would be disparately harmed by the deployment of a state-of-the-art drowsiness detection algorithm. We discuss some of the sources of the bias, such as the lack of robustness of facial analysis algorithms to face occlusions, facial hair, or skin tone. We then identify potential downstream harms of this performance bias, as well as potential misuses of drowsiness detection technology - focusing on driving safety and experience, insurance cream-skimming and coverage-avoidance, worker surveillance, and job precarity.

7. M. Brandao, “Fair navigation planning: a humanitarian robot use case,” in KDD 2020 Workshop on Humanitarian Mapping, 2020. [Abstract] [arXiv] [PDF]

   In this paper we investigate potential issues of fairness related to the motion of mobile robots. We focus on the particular use case of humanitarian mapping and disaster response. We start by showing that there is a fairness dimension to robot navigation, and use a walkthrough example to bring out design choices and issues that arise during the development of a fair system. We discuss indirect discrimination, fairness-efficiency trade-offs, the existence of counter-productive fairness definitions, privacy and other issues. Finally, we conclude with a discussion of the potential of our methodology as a concrete responsible innovation tool for eliciting ethical issues in the design of autonomous systems.

8. M. Brandao, “Discrimination issues in usage-based insurance for traditional and autonomous vehicles,” in Culturally Sustainable Robotics—Proceedings of Robophilosophy 2020, 2020, vol. 335, pp. 395–406. [Abstract] [DOI] [PDF]

   Vehicle insurance companies have started to offer usage-based policies which track users to estimate premiums. In this paper we argue that usage-based vehicle insurance can lead to indirect discrimination of sensitive personal characteristics of users, have a negative impact in multiple personal freedoms, and contribute to reinforcing existing socio-economic inequalities. We argue that there is an incentive for autonomous vehicles (AVs) to use similar insurance policies, and anticipate new sources of indirect and structural discrimination. We conclude by analyzing the advantages and disadvantages of alternative insurance policies for AVs: no-fault compensation schemes, technical explainability and fairness, and national funds.

9. M. Brandao, M. Jirotka, H. Webb, and P. Luff, “Fair navigation planning: a resource for characterizing and designing fairness in mobile robots,” Artificial Intelligence (AIJ), vol. 282, 2020. [Abstract] [DOI] [PDF]

   In recent years, the development and deployment of autonomous systems such as mobile robots have been increasingly common. Investigating and implementing ethical considerations such as fairness in autonomous systems is an important problem that is receiving increased attention, both because of recent findings of their potential undesired impacts and a related surge in ethical principles and guidelines. In this paper we take a new approach to considering fairness in the design of autonomous systems: we examine fairness by obtaining formal definitions, applying them to a system, and simulating system deployment in order to anticipate challenges. We undertake this analysis in the context of the particular technical problem of robot navigation. We start by showing that there is a fairness dimension to robot navigation, and we then collect and translate several formal definitions of distributive justice into the navigation planning domain. We use a walkthrough example of a rescue robot to bring out design choices and issues that arise during the development of a fair system. We discuss indirect discrimination, fairness-efficiency trade-offs, the existence of counter-productive fairness definitions, privacy and other issues. Finally, we elaborate on important aspects of a research agenda and reflect on the adequacy of our methodology in this paper as a general approach to responsible innovation in autonomous systems.

10. M. Brandao, “Age and gender bias in pedestrian detection algorithms,” in Workshop on Fairness Accountability Transparency and Ethics in Computer Vision, CVPR, 2019. [Abstract] [Dataset] [arXiv] [PDF]

    In this paper we evaluate the age and gender bias in state-of-the-art pedestrian detection algorithms. These algorithms are used by mobile robots such as autonomous vehicles for locomotion planning and control. Therefore, performance disparities could lead to disparate impact in the form of biased crash outcomes. Our analysis is based on the INRIA Person Dataset extended with child, adult, male and female labels. We show that all of the 24 top-performing methods of the Caltech Pedestrian Detection Benchmark have higher miss rates on children. The difference is significant and we analyse how it varies with the classifier, features and training data used by the methods. Algorithms were also gender-biased on average but the performance differences were not significant. We discuss the source of the bias, the ethical implications, possible technical solutions and barriers to "solving" the issue.

11. M. Brandao, “Moral Autonomy and Equality of Opportunity for Algorithms in Autonomous Vehicles,” in Envisioning Robots in Society: Power, Politics, and Public Space—Proceedings of Robophilosophy 2018, 2018, vol. 311, pp. 302–310. [Abstract] [DOI] [PDF]

    This paper addresses two issues with the development of ethical algorithms for autonomous vehicles. One is that of uncertainty in the choice of ethical theories and utility functions. Using notions of moral diversity, normative uncertainty, and autonomy, we argue that each vehicle user should be allowed to choose the ethical views by which the vehicle should act. We then deal with the issue of indirect discrimination in ethical algorithms. Here we argue that equality of opportunity is a helpful concept, which could be applied as an algorithm constraint to avoid discrimination on protected characteristics.