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Mediating artificial intelligence developments through negative and positive incentives

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  • The Anh Han
  • Luís Moniz Pereira
  • Tom Lenaerts
  • Francisco C Santos

Abstract

The field of Artificial Intelligence (AI) is going through a period of great expectations, introducing a certain level of anxiety in research, business and also policy. This anxiety is further energised by an AI race narrative that makes people believe they might be missing out. Whether real or not, a belief in this narrative may be detrimental as some stake-holders will feel obliged to cut corners on safety precautions, or ignore societal consequences just to “win”. Starting from a baseline model that describes a broad class of technology races where winners draw a significant benefit compared to others (such as AI advances, patent race, pharmaceutical technologies), we investigate here how positive (rewards) and negative (punishments) incentives may beneficially influence the outcomes. We uncover conditions in which punishment is either capable of reducing the development speed of unsafe participants or has the capacity to reduce innovation through over-regulation. Alternatively, we show that, in several scenarios, rewarding those that follow safety measures may increase the development speed while ensuring safe choices. Moreover, in the latter regimes, rewards do not suffer from the issue of over-regulation as is the case for punishment. Overall, our findings provide valuable insights into the nature and kinds of regulatory actions most suitable to improve safety compliance in the contexts of both smooth and sudden technological shifts.

Suggested Citation

  • The Anh Han & Luís Moniz Pereira & Tom Lenaerts & Francisco C Santos, 2021. "Mediating artificial intelligence developments through negative and positive incentives," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-21, January.
    • Handle: RePEc:plo:pone00:0244592
    DOI: 10.1371/journal.pone.0244592
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    References listed on IDEAS

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    1. Mariarosaria Taddeo & Luciano Floridi, 2018. "Regulate artificial intelligence to avert cyber arms race," Nature, Nature, vol. 556(7701), pages 296-298, April.
    2. Todd Cherry & David McEvoy, 2013. "Enforcing Compliance with Environmental Agreements in the Absence of Strong Institutions: An Experimental Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 54(1), pages 63-77, January.
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    Cited by:

    1. Egils Ginters & Jagadeesh Chakkaravarthy Revathy, 2021. "Hidden and Latent Factors’ Influence on Digital Technology Sustainability Development," Mathematics, MDPI, vol. 9(21), pages 1-22, November.
    2. Wang, Xianjia & Ding, Rui & Zhao, Jinhua & Chen, Wenman & Gu, Cuiling, 2022. "Competition of punishment and reward among inequity-averse individuals in spatial public goods games," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    3. Han, The Anh & Lenaerts, Tom & Santos, Francisco C. & Pereira, Luís Moniz, 2022. "Voluntary safety commitments provide an escape from over-regulation in AI development," Technology in Society, Elsevier, vol. 68(C).
    4. Sayed Fayaz Ahmad & Muhammad Mansoor Alam & Mohd. Khairil Rahmat & Muhammad Khalil Shahid & Mahnaz Aslam & Nur Agus Salim & Mohammed Hasan Ali Al-Abyadh, 2023. "Leading Edge or Bleeding Edge: Designing a Framework for the Adoption of AI Technology in an Educational Organization," Sustainability, MDPI, vol. 15(8), pages 1-20, April.
    5. He, Jialu & Wang, Jianwei & Yu, Fengyuan & Chen, Wei & Li, Bofan, 2022. "The slow but persistent self-improvement boosts group cooperation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    6. Liu, Linjie & Chen, Xiaojie, 2022. "Effects of interconnections among corruption, institutional punishment, and economic factors on the evolution of cooperation," Applied Mathematics and Computation, Elsevier, vol. 425(C).

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