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Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities

Author

Listed:
  • Hazel Si Min Lim

    (Lee Kuan Yew School of Public Policy, National University of Singapore, 469B Bukit Timah Road, Li Ka Shing Building, Singapore 259771, Singapore)

  • Araz Taeihagh

    (Lee Kuan Yew School of Public Policy, National University of Singapore, 469B Bukit Timah Road, Li Ka Shing Building, Singapore 259771, Singapore)

Abstract

Autonomous Vehicles (AVs) are increasingly embraced around the world to advance smart mobility and more broadly, smart, and sustainable cities. Algorithms form the basis of decision-making in AVs, allowing them to perform driving tasks autonomously, efficiently, and more safely than human drivers and offering various economic, social, and environmental benefits. However, algorithmic decision-making in AVs can also introduce new issues that create new safety risks and perpetuate discrimination. We identify bias, ethics, and perverse incentives as key ethical issues in the AV algorithms’ decision-making that can create new safety risks and discriminatory outcomes. Technical issues in the AVs’ perception, decision-making and control algorithms, limitations of existing AV testing and verification methods, and cybersecurity vulnerabilities can also undermine the performance of the AV system. This article investigates the ethical and technical concerns surrounding algorithmic decision-making in AVs by exploring how driving decisions can perpetuate discrimination and create new safety risks for the public. We discuss steps taken to address these issues, highlight the existing research gaps and the need to mitigate these issues through the design of AV’s algorithms and of policies and regulations to fully realise AVs’ benefits for smart and sustainable cities.

Suggested Citation

  • Hazel Si Min Lim & Araz Taeihagh, 2019. "Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities," Sustainability, MDPI, vol. 11(20), pages 1-28, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:20:p:5791-:d:278030
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    Cited by:

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    2. Shadi Shayan & Ki Pyung Kim & Tony Ma & Tan Hai Dang Nguyen, 2020. "The First Two Decades of Smart City Research from a Risk Perspective," Sustainability, MDPI, vol. 12(21), pages 1-20, November.
    3. Konstantina Anastasiadou, 2021. "Sustainable Mobility Driven Prioritization of New Vehicle Technologies, Based on a New Decision-Aiding Methodology," Sustainability, MDPI, vol. 13(9), pages 1-27, April.
    4. Araz Taeihagh, 2021. "Governance of artificial intelligence [Application of artificial intelligence for development of intelligent transport system in smart cities]," Policy and Society, Darryl S. Jarvis and M. Ramesh, vol. 40(2), pages 137-157.
    5. Junyi Wu & Shari Shang, 2020. "Managing Uncertainty in AI-Enabled Decision Making and Achieving Sustainability," Sustainability, MDPI, vol. 12(21), pages 1-17, October.
    6. Fabiana Di Porto & Marialuisa Zuppetta, 2021. "Co-regulating algorithmic disclosure for digital platforms [Theorizing regulatory intermediaries]," Policy and Society, Darryl S. Jarvis and M. Ramesh, vol. 40(2), pages 272-293.
    7. Julien Mercille, 2021. "Inclusive Smart Cities: Beyond Voluntary Corporate Data Sharing," Sustainability, MDPI, vol. 13(15), pages 1-13, July.
    8. Feipeng Wang & Diana Filipa Araújo & Yan-Fu Li, 2023. "Reliability assessment of autonomous vehicles based on the safety control structure," Journal of Risk and Reliability, , vol. 237(2), pages 389-404, April.
    9. Inga Ulnicane & William Knight & Tonii Leach & Bernd Carsten Stahl & Winter-Gladys Wanjiku, 2021. "Framing governance for a contested emerging technology:insights from AI policy [The next space race is Artificial Intelligence]," Policy and Society, Darryl S. Jarvis and M. Ramesh, vol. 40(2), pages 158-177.
    10. Tania Leiman, 2021. "Law and tech collide: foreseeability, reasonableness and advanced driver assistance systems [Advanced Driver Assistance Systems (ADAS): A consideration of driver perceptions on training, usage & im," Policy and Society, Darryl S. Jarvis and M. Ramesh, vol. 40(2), pages 250-271.

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