The European Union’s Approach to Artificial Intelligence and the Challenge of Financial Systemic Risk

Author:

Keller Anat,Martins Pereira Clara,Pires Martinho Lucas

Abstract

AbstractThis piece examines the EU’s ‘Proposal for a Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence’ (‘AI Act’) with a view to determining the extent to which it addresses the systemic risk created by AI FinTech. Ultimately, it is argued that the notion of ‘high risk’ at the centre of the AI Act leaves out financial systemic risk. This exclusion can neither be justified by reasons of technology neutrality, nor by reasons of proportionality: neither is AI-driven financial systemic risk already covered by existing (or proposed) macroprudential frameworks and tools, nor can its omission from the AI Act be justified by the prioritisation of other types of risk. Moving forward, it is suggested that the EU’s AI Act would have benefited from a broader definition of ‘high risk’. It is also hoped that EU policy makers will soon begin to strengthen existing macroprudential toolkits to address the financial systemic risk created by AI.

Publisher

Springer International Publishing

Reference81 articles.

1. Aggarwal N (2021) The norms of algorithmic credit scoring. Camb Law J 80:42–73

2. Alcantara C, Schaul K, De Vynck G, Albergotti R (2021) How big tech got so big: hundreds of acquisitions. The Washington Post, April 21. https://www.washingtonpost.com/technology/interactive/2021/amazon-apple-facebook-google-acquisitions/. Accessed 15 Mar 2022

3. Armour J, Awrey D, Davies P, Enriques L, Gordon JN, Mayer C, Payne J (2016) Principles of financial regulation, 1st edn. Oxford University Press, Oxford

4. Arner DW, Buckley R, Zetzsche D (2019) The rise of global technology risk. In: Arner DW, Avgouleas E, Busch D, Schwarcz SL (eds) Systemic risk in the financial sector: ten years after the great crash. McGill-Queen’s University Press, Montreal, pp 69–82

5. Bank of England (2020) The impact of COVID on machine learning and data science in UK banking, quarterly bulletin 2020 Q4. https://www.bankofengland.co.uk/quarterly-bulletin/2020/2020-q4/the-impact-of-covid-on-machine-learning-and-data-science-in-uk-banking. Accessed 15 Mar 2022

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