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Applying a principle of explicability to AI research in Africa: should we do it?

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Abstract

Developing and implementing artificial intelligence (AI) systems in an ethical manner faces several challenges specific to the kind of technology at hand, including ensuring that decision-making systems making use of machine learning are just, fair, and intelligible, and are aligned with our human values. Given that values vary across cultures, an additional ethical challenge is to ensure that these AI systems are not developed according to some unquestioned but questionable assumption of universal norms but are in fact compatible with the societies in which they operate. This is particularly pertinent for AI research and implementation across Africa, a ground where AI systems are and will be used but also a place with a history of imposition of outside values. In this paper, we thus critically examine one proposal for ensuring that decision-making systems are just, fair, and intelligible—that we adopt a principle of explicability to generate specific recommendations—to assess whether the principle should be adopted in an African research context. We argue that a principle of explicability not only can contribute to responsible and thoughtful development of AI that is sensitive to African interests and values, but can also advance tackling some of the computational challenges in machine learning research. In this way, the motivation for ensuring that a machine learning-based system is just, fair, and intelligible is not only to meet ethical requirements, but also to make effective progress in the field itself.

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Notes

  1. Africa is, of course, a vast continent with many different cultures and peoples. While we talk of ‘Africa’ in this paper for ease of reference, we do not deny that within Africa there is great complexity.

  2. The guidelines and frameworks surveyed are: The Asilomar AI Principles (2017), the Montreal Declaration for Responsible AI (2017), the General Principles in the IEEE Global Initiative’s ‘Ethically Aligned Design’ (2017), the Ethical Principles in the ‘Statement on Artificial Intelligence, Robotics and “Autonomous” Systems’ of the European Group on Ethics in Science and New Technologies (2018), the principles of the ‘AI in the UK: Ready, willing and able?’ report of the UK House of Lords Artificial Intelligence Committee (2018), and the Tenets of the Partnership on AI (2018).

  3. This question is an ethical question. There are, of course, questions about legal accountability and responsibility but we do not attend to them in this paper.

  4. For a sample of seminal philosophical work highlighting community within different cultural contexts, see Mbiti (1990) (Kenya, although with a systematic review of other cultures), Gyekye (1987) (Akan, Ghana), Gbadegesin (1991) (Yoruba, Nigeria) and Ramose (2005) (South Africa).

  5. This is a tradition that draws on a diverse range of theorists from across the continent, such as Senghor (1988), Mbembe (2001), Wa’Thionga (1986) and Biko (2002).

  6. https://moralmachineresults.scalablecoop.org/.

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Acknowledgements

We would like to thank participants at the Third CAIR Symposium on AI Research and Society, held at the University of Johannesburg in March 2019, for feedback and discussion on an earlier version of the paper. We would also like to thank the two reviewers and editors for this journal for their comments.

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Authors and Affiliations

  1. Department of Philosophy, University of the Witwatersrand, Johannesburg, South Africa

    Mary Carman

  2. School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa

    Benjamin Rosman

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  1. Mary Carman
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Correspondence to Mary Carman.

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Benjamin Rosman is one of the founders and organisers of the Deep Learning Indaba that we mention as an example of the growth of machine learning across Africa.

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Carman, M., Rosman, B. Applying a principle of explicability to AI research in Africa: should we do it?. Ethics Inf Technol 23, 107–117 (2021). https://doi.org/10.1007/s10676-020-09534-2

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