Public sector AI transparency standard: UK Government seeks to lead by example

Springer Science and Business Media LLC - Tập 2 - Trang 1-9 - 2022
Nigel Kingsman1, Emre Kazim1, Ali Chaudhry2, Airlie Hilliard2, Adriano Koshiyama2, Roseline Polle2, Giles Pavey3, Umar Mohammed2
1UCL, Holistic AI, London, UK
2Holistic AI, London, UK
3Unilever, London, UK

Tóm tắt

In releasing the Algorithmic Transparency Standard, the UK government has reiterated its commitment to greater algorithmic transparency in the public sector. The Standard signals that the UK government is both pushing forward with the AI standards agenda and ensuring that those standards benefit from empirical practitioner-led experience, enabling coherent, widespread adoption. The two-tier approach of the Algorithmic Transparency Standard encourages transparency inclusivity across distinct audiences, facilitating trust across algorithm stakeholders. Moreover, it can be understood that implementation of the Standard within the UK’s public sector will inform standards more widely, influencing best practice in the private sector. This article provides a summary and commentary of the text.

Tài liệu tham khảo

European Commission. Proposal for a regulation of the European Parliament and of the council: Laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. 2021. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021PC0206. Accessed 07 Dec 2021. Central Digital and Data Office. Algorithmic transparency standard. 2021. https://www.gov.uk/government/collections/algorithmic-transparency-standard. Accessed 07 Dec 2021. Polle R, Kazim E, Carvalho G, Koshiyama A, Inness C, Knight A, Gorski C, Barber D, Lomas E, Yilmaz E, Thompson G. Towards AI standards: thought-leadership in AI legal, ethical and safety specifications through experimentation. SSRN Electron J. 2021. https://doi.org/10.2139/ssrn.3935987. Bandy J. Problematic machine behavior: a systematic literature review of algorithm audits. Proc ACM Hum-Comput Interact. 2021; 5: 1–34. Doi: https://doi.org/10.1145/3449148. Pagallo U, Aurucci P, Casanovas P, Chatila R, Chazerand P, Dignum V, et al. AI4People—on good AI governance: 14 priority actions, a S.M.A.R.T. model of governance, and a regulatory toolbox/ Social Science Research Network. 2019. https://papers.ssrn.com/abstract=3486508 Department for Digital, Culture, Media & Sport. UK national data strategy. 2021. https://www.gov.uk/government/publications/uk-national-data-strategy. Accessed 07 Dec 2021. Office for Artificial Intelligence. National AI strategy. 2021. https://www.gov.uk/government/publications/national-ai-strategy. Accessed 07 Dec 2021. Open Data Institute. Getting data right: perspectives on the UK National Data Strategy 2020. 2020. http://theodi.org/wp-content/uploads/2021/01/Getting-data-right_-perspectives-on-the-UK-National-Data-Strategy-2020-1.pdf. Accessed 06 Jan 2022. Kazim E, Almeida D, Kingsman N, Kerrigan C, Koshiyama A, Lomas E, Hilliard A. Innovation and opportunity: review of the UK’s National AI Strategy. Discov Artif Intell. 2021;1:14. https://doi.org/10.1007/s44163-021-00014-0. Zapisetskaya B, De Silva S. National AI strategy: step change for the AI economy in the UK. 2021. https://www.cms-lawnow.com/ealerts/2021/10/national-ai-strategy-step-change-for-the-ai-economy-in-the-uk. Accessed 24 Jan 2022. Yaros O, Prinsley M A, Vanryckeghem V, Randall R, Hajda O, and Hepworth E. UK Government publishes National Artificial Intelligence Strategy. 2021. https://www.mayerbrown.com/en/perspectives-events/publications/2021/10/uk-government-publishes-national-artificial-intelligence-strategy. Accessed 24 Jan 2022. Markey E. J. Algorithmic justice and online platform transparency act. 2021. https://www.congress.gov/bill/117th-congress/senate-bill/1896/text. Accessed 06 Jan 2022. Brown S. Data accountability and transparency act. 2020. https://www.banking.senate.gov/imo/media/doc/Brown%20-%20DATA%202020%20Discussion%20Draft.pdf. Accessed 06 Jan 2022 Cyberspace Administration of China. Provisions on the administration of algorithm recommendations for internet information services. 2022. http://www.cac.gov.cn/2022-01/04/c_1642894606364259.htm. Accessed 24 Jan 2022. China Briefing News. China passes sweeping recommendation algorithm regulations. 2022. https://www.china-briefing.com/news/china-passes-sweeping-recommendation-algorithm-regulations/ Accessed 24 Jan 2022 Cox J, Lewih A, and Halforty I. AI, machine learning & big data laws and regulations. 2021. https://www.globallegalinsights.com/practice-areas/ai-machine-learning-and-big-data-laws-and-regulations/australia. Accessed 24 Jan 2022. Department of Industry, Science, Energy and Resources. Australia’s AI ethics principles. 2021. https://www.industry.gov.au/data-and-publications/australias-artificial-intelligence-ethics-framework/australias-ai-ethics-principles. Accessed 24 Jan 2022. Haataja M, van de Fliert L, and Rautio P. Public AI registers: realising AI transparency and civic participation in government use of AI. 2020. https://algoritmeregister.amsterdam.nl/wp-content/uploads/White-Paper.pdf. Accessed 06 Jan 2022. Government of Canada. Algorithmic Impact Assessment Tool. 2021. https://www.canada.ca/en/government/system/digital-government/digital-government-innovations/responsible-use-ai/algorithmic-impact-assessment.html. Accessed 24 Jan 2022. Koshiyama A, Kazim E, Treleaven P, Rai P, Szpruch, L, Pavey, G, et al. Towards algorithm auditing: a survey on managing legal, ethical and technological risks of AI, ML and associated algorithms. SSRN Electron J. 2021. Doi: https://doi.org/10.2139/ssrn.3778998 Kazim E, Barnett J, Koshiyama A. Automation and fairness: assessing the automation of fairness in cases of reasonable pluralism and considering the blackbox of human judgment. SSRN Electron J. 2020. https://doi.org/10.2139/ssrn.3698404. Bernini M. The opacity of fictional minds: Transparency, interpretive cognition and the exceptionality thesis. In: The Cognitive Humanities, Peter Garratt, Ed. Palgrave Macmillan. p. 35–54. Institute for the Future of Work. Building a systematic framework of accountability for algorithmic decision making. 2021. https://www.ifow.org/publications/policy-briefing-building-a-systematic-framework-of-accountability-for-algorithmic-decision-making. Accessed 07 Dec 2021. Kazim E, Koshiyama A. The interrelation between data and AI ethics in the context of impact assessments. AI Ethics. 2021;1:219–25. https://doi.org/10.1007/s43681-020-00029-w. Agarwal S. Trade-Offs between fairness and privacy in machine learning. IJCAI 2021 Workshop on AI for Social Good. 2021. https://crcs.seas.harvard.edu/publications/trade-offs-between-fairness-and-privacy-machine-learning. Accessed 24 Jan 2022. Kleinberg J, Mullainathan S, Raghavan M. Inherent trade-offs in the fair determination of risk scores. 2016. arXiv:1609.05807. Chouldechova A. Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data. 2017;5:153–63. https://doi.org/10.1089/big.2016.0047. Berk R, Heidari H, Jabbari S, Kearns M, Roth A. Fairness in criminal justice risk assessments: the state of the art. Sociol Methods Res. 2021;50:3–44. https://doi.org/10.1177/0049124118782533. Keeling E. The UK’s National AI Strategy: Setting a 10-year agenda to make the UK a “global AI superpower”. 2021. https://www.allenovery.com/en-gb/global/blogs/digital-hub/the-uks-national-ai-strategy---setting-a-10-year-agenda-to-make-the-uk-a-global-ai-superpower. Accessed 06 Jan 2022. The State Council of The People’s Republic of China. China promotes local AI pilot zones. http://english.www.gov.cn/statecouncil/ministries/202005/09/content_WS5eb66f29c6d0b3f0e9497457.html Accessed 24 Jan 2022. Kazim E, Koshiyama AS. A high-level overview of AI ethics. Patterns. 2021;2: 100314. https://doi.org/10.1016/j.patter.2021.100314. The Law Society. Algorithm use in the criminal justice system report. 2019. https://www.lawsociety.org.uk/topics/research/algorithm-use-in-the-criminal-justice-system-report. Accessed 06 Jan 2022 Almeida D, Shmarko K, Lomas E. The ethics of facial recognition technologies, surveillance, and accountability in an age of artificial intelligence: a comparative analysis of US, EU, and UK regulatory frameworks. AI Ethics. 2021. https://doi.org/10.1007/s43681-021-00077-w.