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Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

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  • Samuele Lo Piano

    (University of Reading
    Universitat Oberta de Catalunya)

Abstract

Decision-making on numerous aspects of our daily lives is being outsourced to machine-learning (ML) algorithms and artificial intelligence (AI), motivated by speed and efficiency in the decision process. ML approaches—one of the typologies of algorithms underpinning artificial intelligence—are typically developed as black boxes. The implication is that ML code scripts are rarely scrutinised; interpretability is usually sacrificed in favour of usability and effectiveness. Room for improvement in practices associated with programme development have also been flagged along other dimensions, including inter alia fairness, accuracy, accountability, and transparency. In this contribution, the production of guidelines and dedicated documents around these themes is discussed. The following applications of AI-driven decision-making are outlined: (a) risk assessment in the criminal justice system, and (b) autonomous vehicles, highlighting points of friction across ethical principles. Possible ways forward towards the implementation of governance on AI are finally examined.

Suggested Citation

  • Samuele Lo Piano, 2020. "Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-7, December.
    • Handle: RePEc:pal:palcom:v:7:y:2020:i:1:d:10.1057_s41599-020-0501-9
    DOI: 10.1057/s41599-020-0501-9
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    Cited by:

    1. Raza, Syed Arshad, 2021. "Managing ethical requirements elicitation of complex socio-technical systems with critical systems thinking: A case of course-timetabling project," Technology in Society, Elsevier, vol. 66(C).
    2. Meir Russ, 2021. "Knowledge Management for Sustainable Development in the Era of Continuously Accelerating Technological Revolutions: A Framework and Models," Sustainability, MDPI, vol. 13(6), pages 1-32, March.
    3. Ola Michalec & Cian O’Donovan & Mehdi Sobhani, 2021. "What is robotics made of? The interdisciplinary politics of robotics research," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-15, December.
    4. Andrea Saltelli & Monica Fiore, 2020. "From sociology of quantification to ethics of quantification," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-8, December.
    5. Annye Braca & Pierpaolo Dondio, 2023. "Developing persuasive systems for marketing: the interplay of persuasion techniques, customer traits and persuasive message design," Italian Journal of Marketing, Springer, vol. 2023(3), pages 369-412, September.
    6. Stephen C. Slota & Kenneth R. Fleischmann & Sherri Greenberg & Nitin Verma & Brenna Cummings & Lan Li & Chris Shenefiel, 2023. "Locating the work of artificial intelligence ethics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(3), pages 311-322, March.
    7. Lachlan O'Neill & Simon D Angus & Satya Borgohain & Nader Chmait & David Dowe, 2021. "Creating Powerful and Interpretable Models with Regression Networks," SoDa Laboratories Working Paper Series 2021-09, Monash University, SoDa Laboratories.
    8. Federico Fioravanti & Iyad Rahwan & Fernando Tohmé, 2022. "Properties of Aggregation Operators Relevant for Ethical Decision Making in Artificial Intelligence," Working Papers 177, Red Nacional de Investigadores en Economía (RedNIE).
    9. Athanasios Polyportis & Nikolaos Pahos, 2024. "Navigating the perils of artificial intelligence: a focused review on ChatGPT and responsible research and innovation," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    10. Koefer, Franziska & Lemken, Ivo & Pauls, Jan, 2023. "Fairness in algorithmic decision systems: A microfinance perspective," EIF Working Paper Series 2023/88, European Investment Fund (EIF).
    11. Federico Fioravanti & Iyad Rahwan & Fernando Abel Tohm'e, 2022. "Classes of Aggregation Rules for Ethical Decision Making in Automated Systems," Papers 2206.05160, arXiv.org, revised Jun 2023.

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