How should we regulate artificial intelligence?
Open accessAbstract
Using artificial intelligence (AI) technology to replace human decision-making will inevitably create new risks whose consequences are unforeseeable. This naturally leads to calls for regulation, but I argue that it is too early to attempt a general system of AI regulation. Instead, we should work incrementally within the existing legal and regulatory schemes which allocate responsibility, and therefore liability, to persons. Where AI clearly creates risks which current law and regulation cannot deal with adequately, then new regulation will be needed. But in most cases, the current system can work effectively if the producers of AI technology can provide sufficient transparency in explaining how AI decisions are made. Transparency ex post can often be achieved through retrospective analysis of the technology's operations, and will be sufficient if the main goal is to compensate victims of incorrect decisions. Ex ante transparency is more challenging, and can limit the use of some AI technologies such as neural networks. It should only be demanded by regulation where the AI presents risks to fundamental rights, or where society needs reassuring that the technology can safely be used. Masterly inactivity in regulation is likely to achieve a better long-term solution than a rush to regulate in ignorance. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations'.
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Transparency in goals undergirds other safeguards around the protection of target populations and may help ensure compliance with relevant legislation and precedent.
“Transparency in goals, thus, undergirds other safeguards around the protection of target populations and may help ensure compliance with relevant legislation and precedent (Reed 2018).”
From How to Design AI for Social Good: Seven Essential Factors (2020) · cites this paper as Reed 2018
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How to Design AI for Social Good: Seven Essential Factors
Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-020-00213-5 · Cited by 430 · see citing works
“Transparency in goals, thus, undergirds other safeguards around the protection of target populations and may help ensure compliance with relevant legislation and precedent (Reed 2018).”
BackgroundArtificial Intelligence Regulation: A framework for governance
de Almeida, P. G. R., dos Santos, C. D., & Farias, J. S. (2021) · Ethics and Information Technology · doi:10.1007/s10676-021-09593-z · Cited by 395 · see citing works
Yigitcanlar, T., & Cugurullo, F. (2020) · Sustainability · doi:10.3390/su12208548 · Cited by 337 · see citing works
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The growing ubiquity of algorithms in society: implications, impacts and innovations
Olhede, S. C., & Wolfe, P. J. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2017.0364 · Cited by 120 · see citing works
Designing AI for Social Good: Seven Essential Factors
Cowls, J., King, T., Taddeo, M., & Floridi, L. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3388669 · Cited by 83 · see citing works
- Authors
- Reed, C.
- Affiliations
- Queen Mary University of London (GB)
- Corresponding author
- Reed, Chris
- ORCID
- Reed, Chris
- Venue
- Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
- Publisher
- The Royal Society
- Published
- 2018-08-06
- Volume
- 376
- Issue
- 2128
- Pages
- 20170360
- ISSN
- 1364-503X (ISSN-L)
- 1471-2962
- PMC
- PMC6107539
- OpenAlex
- W2887319786
- Cited by
- 114
- References
- 2
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 21 · 2023: 30 · 2022: 14 · 2021: 14 · 2020: 8 · 2019: 6 · 2018: 2
- FWCI
- 14.47
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Queen Mary University of London
- University of Cambridge
- Topics
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
- Keywords
- AI regulation, algorithmic decision-making, liability, transparency, artificial neural networks, fundamental rights, legal responsibility
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