Abstract
Artificial Intelligence (AI) is already having a major impact on society. As a result, many organizations have launched a wide range of initiatives to establish ethical principles for the adoption of socially beneficial AI. Unfortunately, the sheer volume of proposed principles threatens to overwhelm and confuse. How might this problem of ‘principle proliferation’ be solved? In this paper, we report the results of a fine-grained analysis of several of the highest-profile sets of ethical principles for AI. We assess whether these principles converge upon a set of agreed-upon principles, or diverge, with significant disagreement over what constitutes ‘ethical AI.’ Our analysis finds a high degree of overlap among the sets of principles we analyze. We then identify an overarching framework consisting of five core principles for ethical AI. Four of them are core principles commonly used in bioethics: beneficence, non-maleficence, autonomy, and justice. On the basis of our comparative analysis, we argue that a new principle is needed in addition: explicability, understood as incorporating both the epistemological sense of intelligibility (as an answer to the question ‘how does it work?’) and in the ethical sense of accountability (as an answer to the question: ‘who is responsible for the way it works?’). In the ensuing discussion, we note the limitations and assess the implications of this ethical framework for future efforts to create laws, rules, technical standards, and best practices for ethical AI in a wide range of contexts.
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Notes
- 1.
These are not the only problems, see (Floridi 2019b).
- 2.
The Montreal Declaration is currently open for comments as part of a redrafting exercise. The principles we refer to here are those which were publicly announced as of May 1, 2018.
- 3.
The third version of Ethically Aligned Design will be released in 2019 following wider public consultation.
- 4.
A similar evaluation of AI ethics guidelines has recently been undertaken by Hagendorff (2019), which adopts different criteria of inclusion and assessment. Note that the evaluation includes in its sample the set of principles we describe here.
- 5.
Of the six documents, the Asilomar Principles offer the largest number of principles with arguably the broadest scope. The 23 principles are organised under three headings, “research issues”, “ethics and values”, and “longer-term issues”. We have omitted consideration of the five “research issues” here as they are related specifically to the practicalities of AI development in the narrower context of academia and industry. Similarly, the Partnership’s eight Tenets consist of both intra-organisational objectives and wider principles for the development and use of AI. We include only the wider principles (the first, sixth, and seventh tenets).
- 6.
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Disclosure
Floridi chaired the AI4People project and Cowls was the rapporteur. Floridi is also a member of the European Commission’s High-Level Expert Group on Artificial Intelligence (HLEGAI).
Funding
Floridi’s work was supported by (i) Privacy and Trust Stream—Social lead of the PETRAS Internet of Things research hub—PETRAS is funded by the UK Engineering and Physical Sciences Research Council (EPSRC), grant agreement no. EP/N023013/1; (ii) Facebook; and (iii) Google. Cowls is the recipient of a Doctoral Studentship from the Alan Turing Institute.
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Floridi, L., Cowls, J. (2021). A Unified Framework of Five Principles for AI in Society. In: Floridi, L. (eds) Ethics, Governance, and Policies in Artificial Intelligence. Philosophical Studies Series, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-030-81907-1_2
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