iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/s43681-022-00238-5
Designed to cooperate: a Kant-inspired ethic of machine-to-machine cooperation | AI and Ethics Skip to main content

Advertisement

Log in

Designed to cooperate: a Kant-inspired ethic of machine-to-machine cooperation

  • Commentary
  • Published:
AI and Ethics Aims and scope Submit manuscript

Abstract

We envision an increasing presence of devices with agency and autonomous machines in public spaces (e.g., automated vehicles, urban robots and drones) beyond the confines of constrained environments such as a factory floor or research labs. Hence, AI and robotic systems of the future will need to interact with one another, not only in cyber space but also in physical space, and need to behave appropriately in their interactions with one another. This commentary highlights an ethic of machine-to-machine cooperation and machine pro-sociality, and argues that machines capable of autonomous sensing, decision making and action, such as automated vehicles and urban robots, owned and used by different self-interested parties, and having their own agendas (or interests of their owners) should be designed and built to be cooperative in their behaviours, especially if they share public spaces. That is, by design, the machine should first cooperate, and then only consider alternatives if there are problems. It is argued that being cooperative is not only important for their improved functioning, especially, when they use shared resources (e.g., parking spaces, public roads, curbside space and walkways), but also as a favourable requirement analogous to how humans cooperating with other humans can be advantageous and often viewed favourably. The usefulness of such machine-to-machine cooperation are illustrated via examples including cooperative crowdsourcing, cooperative traffic routing and parking as well as futuristic scenarios involving urban robots for delivery and shopping. It is argued that just as privacy-by-design and security-by-design are important considerations, to yield systems that fulfill ethical requirements, cooperative-by-design should also be an imperative for autonomous systems that are separately owned but co-inhabit the same spaces and use common resources. If a machine using shared public spaces is not cooperative, as one might expect, then it is not only anti-social but not behaving ethically. It is also proposed that certification for urban robots that operate in public could be explored.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. From: https://www.oxfordlearnersdictionaries.com/definition/american_english/cooperation.

  2. https://www.bbc.com/news/technology-42265048, though there seems more freedom for deployment in more recent times: https://www.aitrends.com/robotics/last-mile-delivery-robots-making-a-comeback-after-initial-bans/.

  3. https://www.etsi.org/technologies/automotive-intelligent-transport

  4. http://www.sae.org/servlets/pressRoom?OBJECT_TYPE=PressReleases &PAGE=showRelease &RELEASE_ID=3343.

  5. https://techcrunch.com/2020/08/16/autox-launches-its-robotaxi-service-in-shanghai-competing-with-didis-pilot-program/.

  6. E.g., https://www.straitstimes.com/singapore/transport/robot-traffic-cop-spotted-at-changi-airport,https://mothership.sg/2020/05/police-matar-robots/.

  7. https://www.knightscope.com.

  8. E.g., see https://www.dezeen.com/2017/12/13/k5-knightscope-security-robot-sfspca-san-francisco-bullied-off-street/.

  9. For example, see  [8], and https://unece.org/DAM/trans/doc/2019/wp29grva/GRVA-02-09e.pdf.

  10. See https://www.ansys.com/applications/autonomous-vehicle-validation and https://www.dlr.de/content/en/articles/news/2021/02/20210430_the-set-level-project.html.

  11. A large database of scenarios and situations for automated driving systems testing is being considered: https://www.safetypool.ai.

References

  1. Alfeo, A.L., Ferrer, E. C., Carrillo, Y. L., Grignard, A., Pastor, L. A., Sleeper, D. T., Cimino, M. G. C. A., Lepri, B., Vaglini, G., Larson, K., Dorigo M., Pentland, A.: Urban swarms: A new approach for autonomous waste management. In 2019 International Conference on Robotics and Automation (ICRA), pages 4233–4240, (2019)

  2. Aparow, Vimal Rau, Choudary, Apratim, Kulandaivelu, Giridharan, Webster, Thomas, Dauwels, Justin, de Boer, Niels: A comprehensive simulation platform for testing autonomous vehicles in 3d virtual environment. In 2019 IEEE 5th International Conference on Mechatronics System and Robots (ICMSR), pages 115–119, (2019)

  3. Ben Messaoud, Rim: Towards efficient mobile crowdsensing assignment and uploading schemes. Theses, Université Paris-Est (2017)

    Google Scholar 

  4. Capponi, A., Fiandrino, C., Kantarci, B., Foschini, L., Kliazovich, D., Bouvry, P.: A survey on mobile crowdsensing systems: Challenges, solutions, and opportunities. IEEE Communications Surveys Tutorials 21(3), 2419–2465 (2019)

    Article  Google Scholar 

  5. Curry, Oliver Scott: Morality as Cooperation: A Problem-Centred Approach, pages 27–51. Springer International Publishing, Cham, (2016)

  6. Hao, Jianye, Leung, Ho-Fung, Ming, Zhong: Multiagent reinforcement social learning toward coordination in cooperative multiagent systems. ACM Trans. Auton. Adapt. Syst., 9(4), (December 2014)

  7. Jaques, Natasha, Lazaridou, Angeliki, Hughes, Edward, Gülçehre, Çaglar, Ortega, Pedro A., Strouse, DJ, Leibo, Joel Z., de Freitas, Nando: Social influence as intrinsic motivation for multi-agent deep reinforcement learning. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 3040–3049. PMLR (2019)

  8. Legashev, L.V., Letuta, T.V., Polezhaev, P.N., Shukhman, A.E., Ushakov, Yu.A.: Monitoring, certification and verification of autonomous robots and intelligent systems: Technical and legal approaches. Procedia Computer Science 150, 544–551 (2019)

    Article  Google Scholar 

  9. Loke, Seng W.: Cooperative automated vehicles: A review of opportunities and challenges in socially intelligent vehicles beyond networking. IEEE Transactions on Intelligent Vehicles 4(4), 509–518 (2019)

    Article  Google Scholar 

  10. Morishita, Shigeya, Maenaka, Shogo, Nagata, Daichi, Tamai, Morihiko, Yasumoto, Keiichi, Fukukura, Toshinobu, Sato, Keita: Sakurasensor: Quasi-realtime cherry-lined roads detection through participatory video sensing by cars. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp ’15, page 695-705, New York, NY, USA (2015). Association for Computing Machinery

  11. Nagenborg, Michael: Urban robotics and responsible urban innovation. Ethics and Information Technology, (2018)

  12. Ndousse, Kamal K, Eck, Douglas, Levine, Sergey, Jaques, Natasha: Emergent social learning via multi-agent reinforcement learning. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 7991–8004. PMLR, 18–24 (Jul 2021)

  13. Phuttharak, J., Loke, S.W.: A review of mobile crowdsourcing architectures and challenges: Toward crowd-empowered internet-of-things. IEEE Access 7, 304–324 (2019)

    Article  Google Scholar 

  14. Powers, Thomas M.: Prospects for a kantian machine. IEEE Intelligent Systems 21(4), 46–51 (2006)

    Article  Google Scholar 

  15. Roemer, John E.: How We Cooperate: A Theory of Kantian Optimization. Yale University Press (2019)

    Book  Google Scholar 

  16. Salvini, Pericle: Urban robotics: Towards responsible innovations for our cities. Robotics and Autonomous Systems 100, 278–286 (2018)

    Article  Google Scholar 

  17. Schoener, Hans-Peter: The role of simulation in development and testing of autonomous vehicles. 09 (2017)

  18. While, Aidan H, Marvin, Simon, Kovacic, Mateja: Urban robotic experimentation: San Francisco, Tokyo and Dubai. Urban Studies, (2020)

  19. Xiang, Shili, Li, Lu, Lo, Si Min, Li, Xiaoli: People-centric mobile crowdsensing platform for urban design. In Gao Cong, Wen-Chih Peng, Wei Emma Zhang, Chengliang Li, and Aixin Sun, editors, Advanced Data Mining and Applications, pages 569–581, Cham, (2017). Springer International Publishing

  20. Zhang, Chengwei, Li, Xiaohong, Hao, Jianye, Chen, Siqi, Tuyls, Karl, Xue, Wanli, Feng, Zhiyong: Sa-iga: a multiagent reinforcement learning method towards socially optimal outcomes. Autonomous Agents and Multi-Agent Systems 33(4), 403–429 (2019)

    Article  Google Scholar 

  21. Zhang, Jimuyang, Ohn-Bar, Eshed: Learning by watching. CoRR, arXiv:abs/2106.05966, (2021)

Download references

Acknowledgements

The author would like to thank the anonymous reviewers for the many insightful comments which helped improved the commentary.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Seng W. Loke.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Loke, S.W. Designed to cooperate: a Kant-inspired ethic of machine-to-machine cooperation. AI Ethics 3, 991–996 (2023). https://doi.org/10.1007/s43681-022-00238-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s43681-022-00238-5

Keywords

Navigation