Machine Morality

  • Living reference work entry
  • First Online:
Encyclopedia of Heroism Studies

Synonyms

Algorithmic harm; Artificial heroism; Computational ethics; Ethical AI; Virtuous machines, computational morality

Definition

Machine morality focuses on ethical aspects of artificial intelligence (AI) in terms of ways in which algorithms, machines, and systems can be developed and deployed adhering to moral values and, depending on the degree of autonomy, exhibit some form of moral competence.

Introduction

Algorithms are all around us; yet, their pervasiveness and functionality are often opaque. In our everyday lives, algorithms – coalesced into artificial intelligence (AI) systems – shape the way we do our work, interact with others, and, in many respects, shape our view of reality. Unfortunately, these technologies are not all benevolent. In many cases, they are at least minimally exploitative, but they can also be directly harmful to the individuals and groups that use them.

Humans are generally exposed to AI when it is embedded in an everyday tool such as a phone or...

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

Access this chapter

Institutional subscriptions

References

  • Abadi, M., A. Chu, I.J. Goodfellow, H.B. McMahan, I. Mironov, K. Talwar, and L. Zhang. 2016. Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, Vienna, Austria, October 24–28, 2016, ed. E.R. Weippl, S. Katzenbeisser, C. Kruegel, A.C. Myers, and S. Halevi, 308–318. New York, NY, USA, ACM.

    Google Scholar 

  • Allison, S.T., G.R. Goethals, and R.M. Kramer. 2017. Setting the scene: The rise and coalescence of heroism science. In Handbook of heroism and heroic leadership, ed. S.T. Allison, G.R. Goethals, and R.M. Kramer. New York: Routledge.

    Google Scholar 

  • Anthony, D.L., T. Henderson, and D. Kotz. 2007. Privacy in location-aware computing environments. IEEE Pervasive Computing 6 (4): 64–72. https://doi.org/10.1109/MPRV.2007.83.

    Article  Google Scholar 

  • Ateniese, G., L.V. Mancini, A. Spognardi, A. Villani, D. Vitali, and G. Felici. 2015. Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers. International Journal of Security and Networks 10 (3): 137–150. https://doi.org/10.1504/IJSN.2015.071829.

    Article  Google Scholar 

  • Bender, E.M., T. Gebru, A. McMillan-Major, and S. Shmitchell. 2021. On the dangers of stochastic parrots: Can language models be too big? In FAccT ‘21: 2021 ACM conference on fairness, accountability, and transparency, virtual event/Toronto, Canada, March 3–10, 2021, ed. M.C. Elish, W. Isaac, and R.S. Zemel, 610–623. ACM.

    Chapter  Google Scholar 

  • Birhane, A. 2021. Algorithmic injustice: A relational ethics approach. Patterns 2 (2): 100205. https://doi.org/10.1016/j.patter.2021.100205.

  • Birhane, A., P. Kalluri, D. Card, W. Agnew, R. Dotan, and M. Bao. 2022. The values encoded in machine learning research. In 2022 ACM conference on fairness, accountability, and transparency, FAccT’22, New York, NY, USA, 173–184. Association for Computing Machinery.

    Chapter  Google Scholar 

  • Bode, L., and B. Epstein. 2015. Campaign Klout: Measuring online influence during the 2012 election. Journal of Information Technology & Politics 12 (2): 133–148. https://doi.org/10.1080/19331681.2014.994157.

    Article  Google Scholar 

  • Cameron, M.A., R. Power, B. Robinson, and J. Yin. 2012. Emergency situation awareness from twitter for crisis management. In Proceedings of the 21st international conference on World Wide Web, WWW’12 companion, New York, NY, USA, 695–698. Association for Computing Machinery.

    Google Scholar 

  • Carlini, N., F. Tramer, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T.B. Brown, D. Song, U. Erlingsson, A. Oprea, and C. Raffel. 2021. Extracting training data from large language models. In 30th USENIX security symposium, USENIX security 2021, August 11–13, 2021, ed. M. Bailey and R. Greenstadt, 2633–2650. Berkeley, CA, USA, USENIX Association.

    Google Scholar 

  • Chandrasekaran, V., C. Gao, B. Tang, K. Fawaz, S. Jha, and S. Banerjee. 2021. Face-off: Adversarial face obfuscation. Proceedings on Privacy Enhancing Technologies 2: 369–390.

    Article  Google Scholar 

  • Christian, J. 2023, Jan. CNET’s AI journalist appears to have committed extensive plagiarism.

    Google Scholar 

  • Doull, K.E., C. Chalmers, P. Fergus, S. Longmore, A.K. Piel, and S.A. Wich. 2021. An evaluation of the factors affecting ‘poacher’ detection with drones and the efficacy of machine-learning for detection. Sensors 21 (12): 4074. https://doi.org/10.3390/s21124074.

    Article  PubMed  PubMed Central  Google Scholar 

  • Dwork, C., F. McSherry, K. Nissim, and A.D. Smith. 2016. Calibrating noise to sensitivity in private data analysis. Journal of Privacy and Confidentiality 7 (3): 17–51. https://doi.org/10.29012/jpc.v7i3.405.

    Article  Google Scholar 

  • Emmery, C., Ákos Kádár, and G. Chrupała. 2021. Adversarial stylometry in the wild: Transferable lexical substitution attacks on author profiling. In Proceedings of the 16th conference of the European chapter of the Association for computational linguistics: Main Volume, EACL 2021, Online, April 19–23, 2021, ed. P. Merlo, J. Tiedemann, and R. Tsarfaty, 2388–2402. Cedarville, OH, USA, Association for Computational Linguistics.

    Google Scholar 

  • Evans, S.W. 2022. When all research is dual use. Issues in Science and Technology 38 (3): 84–87.

    Google Scholar 

  • Gebru, T., J. Morgenstern, B. Vecchione, J.W. Vaughan, H.M. Wallach, Hal Daumé III, and K. Crawford. 2021. Datasheets for datasets. Communications of the ACM 64 (12): 86–92. https://doi.org/10.1145/3458723.

    Article  Google Scholar 

  • Jayawickreme, E., and P. Di Stefano. 2012. How can we study heroism? Integrating persons, situations and communities. Political Psychology 33 (1): 165–178.

    Article  Google Scholar 

  • Jobin, A., M. Ienca, and E. Vayena. 2019. The global landscape of AI ethics guidelines. Nature Machine Intelligence 1 (9): 389–399. https://doi.org/10.1038/s42256-019-0088-2.

    Article  Google Scholar 

  • Jurowetzki, R., D.S. Hain, J. Mateos-Garcia, and K. Stathoulopoulos. 2021. The privatization of AI research(-ers): Causes and potential consequences – From university-industry interaction to public research brain-drain? CoRR abs/2102.01648: 1–36. ar**v:2102.01648.

    Google Scholar 

  • Klincewicz, M. 2015. Autonomous weapons systems, the frame problem and computer security. Journal of Military Ethics 14 (2): 162–176. https://doi.org/10.1080/15027570.2015.1069013.

    Article  Google Scholar 

  • Koppel, M., N. Akiva, E. Alshech, and K. Bar. 2009. Automatically classifying documents by ideological and organizational affiliation. In IEEE international conference on intelligence and security informatics, ISI 2009, Dallas, Texas, USA, June 8–11, 2009, Proceedings, Stanford, 176–178. CA, USA, IEEE.

    Google Scholar 

  • Liang, Y., Z. Cai, J. Yu, Q. Han, and Y. Li. 2018. Deep learning based inference of private information using embedded sensors in smart devices. IEEE Network 32 (4): 8–14. https://doi.org/10.1109/MNET.2018.1700349.

    Article  Google Scholar 

  • Manzoor, S.I., J. Singla, and Nikita. 2019. Fake news detection using machine learning approaches: A systematic review. In 2019 3rd international conference on trends in electronics and informatics (ICOEI), 230–234.

    Chapter  Google Scholar 

  • Mitchell, M., S. Wu, A. Zaldivar, P. Barnes, L. Vasserman, B. Hutchinson, E. Spitzer, I.D. Raji, and T. Gebru. 2019. Model cards for model reporting. In Proceedings of the conference on fairness, accountability, and transparency, FAT* 2019, Atlanta, GA, USA, January 29–31, 2019, ed. Danah Boyd and J.H. Morgenstern, New York, NY, USA 220–229. ACM.

    Google Scholar 

  • MohammedKhan, H., M. Balvert, C. Guven, and E. Postma. 2021. Predicting human body dimensions from single images: A first step in automatic malnutrition detection. In Proceedings of the 1st international conference on AI for people: Towards sustainable AI, CAIP 2021, 20–24 November 2021, Tilburg, NB, NL, Bologna, Italy. EAI.

    Google Scholar 

  • Paullada, A., I.D. Raji, E.M. Bender, E. Denton, and A. Hanna. 2021. Data and its (dis)contents: A survey of dataset development and use in machine learning research. Patterns 2 (11): 100336. https://doi.org/10.1016/j.patter.2021.100336.

    Article  PubMed  PubMed Central  Google Scholar 

  • Pauwels, E. 2020. Artificial intelligence and data capture technologies in violence and conflict prevention: Opportunities and challenges for the international community, Technical report. Global Center on Cooperative Security.

    Google Scholar 

  • Perrigo, B. 2023, Jan. OpenAI used Kenyan workers on less than $2 per hour: Exclusive.

    Google Scholar 

  • Raji, I.D., A. Smart, R.N. White, M. Mitchell, T. Gebru, B. Hutchinson, J. SmithLoud, D. Theron, and P. Barnes. 2020. Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 conference on fairness, accountability, and transparency, FAT* ‘20, 33–44. New York, NY, USA, Association for Computing Machinery.

    Google Scholar 

  • Raval, N., A. Machanavajjhala, and J. Pan. 2019. Olympus: Sensor privacy through utility aware obfuscation. Proceedings on Privacy Enhancing Technologies 2019 (1): 5–25. https://doi.org/10.2478/popets-2019-0002.

    Article  Google Scholar 

  • Scheutz, M., and T. Arnold. 2016. Feats without heroes: Norms, means, and ideal robotic action. Frontiers in Robotics and AI 3: 1–8. https://doi.org/10.3389/frobt.2016.00032.

    Article  Google Scholar 

  • Schler, J., M. Koppel, S. Argamon, and J.W. Pennebaker. 2006. Effects of age and gender on blogging. In Computational approaches to analyzing weblogs, papers from the 2006 AAAI Spring symposium, Technical report SS-06-03, Stanford, California, USA, March 27–29, 2006, Stanford, CA, USA, 199–205. AAAI.

    Google Scholar 

  • Schwartz, B. 1990. The creation and destruction of value. American Psychologist 45 (1): 7.

    Article  Google Scholar 

  • Taddeo, M., and L. Floridi. 2021. Regulate artificial intelligence to avert cyber arms race, 283–287. Cham: Springer International Publishing.

    Google Scholar 

  • Wachter, S., and B. Mittelstadt. 2018. A right to reasonable inferences: Re-thinking data protection law in the age of Big Data and AI. Columbia Business Law Review 2019: 494–620.

    Google Scholar 

  • Wallach, W., and C. Allen. 2008. Moral machines: Teaching robots right from wrong. Oxford University Press. New York, NY, USA

    Google Scholar 

  • Whittaker, M. 2021. The steep cost of capture. Interactions 28 (6): 50–55. New York, NY, USA, https://doi.org/10.1145/3488666.

  • Whittaker, M., M. Alper, C.L. Bennett, S. Hendren, L. Kaziunas, M. Mills, M.R. Morris, J. Rankin, E. Rogers, M. Salas, et al. 2019. Disability, bias, and AI, New York, NY, USA, 1–32. AI Now Institute.

    Google Scholar 

  • Wiltshire, T.J. 2015. A prospective framework for the design of ideal artificial moral agents: Insights from the science of heroism in humans. Minds and Machines 25 (1): 57–71.

    Article  Google Scholar 

  • Yao, Z., Y. Lum, A. Johnston, L.M. Mejia-Mendoza, X. Zhou, Y. Wen, A. AspuruGuzik, E.H. Sargent, and Z.W. Seh. 2022. Machine learning for a sustainable energy future, 1–14. London, ENG, UK, Springer Nature.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chris Emmery .

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Emmery, C., Wiltshire, T.J. (2023). Machine Morality. In: Encyclopedia of Heroism Studies. Springer, Cham. https://doi.org/10.1007/978-3-031-17125-3_317-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-17125-3_317-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17125-3

  • Online ISBN: 978-3-031-17125-3

  • eBook Packages: Springer Reference Behavioral Science and PsychologyReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

Publish with us

Policies and ethics

Navigation