Humans design and control machines, and the algorithms that let those machines learn, whether that is automating the recruitment process or providing financial advice. As with any member of staff, coders, programmers and managers can be subject to the myriad of human biases, and the outputs of machines may likely reflect those biases. It follows that the regulatory reforms over recent years were developed to overcome the very ‘human’ problems embodied in people-centric workplaces – be they cultural failings and lack of diversity of thought; poorly aligned incentives, responsibilities and remuneration; or short-termism and other biases – remain equally relevant to an AI/ML-centric workplace. In fact, it may even become harder and take longer to identify root causes of problems, and hence attribute accountability to individuals. For example, how would you know which issues are a function of poor design – the manufacturer’s fault if you have bought an ‘off the shelf’ technology product – or poor implementation – which could demonstrate incompetence or a lack of clear understanding from the firm’s management. In the context of decisions made by machines which themselves learn and change over time, how do you define what it means for the humans in the firm to act with “reasonable steps” and “due skill, care and diligence”? In a more automated, fast-moving world of AI/ML, boards – not just regulators – will need to consider and be on top of these issues. Firms will need to consider how to allocate individual responsibilities, including under the Senior Managers Regime. Machines lack morals.
In 2018, an FT survey of banks around the world provided evidence of a cautious approach being taken by firms.5 And according to a McKinsey survey of financial and non-financial firms, most barriers to the more rapid adoption of AI were internal to the firms themselves: poor data accessibility; lack of suitable IMF (2019) ‘Global Financial Stability Report April 2019’ Carney, M., (2019) ‘AI and the Global Economy’ 4 IIF (2018) ‘Machine Learning in Anti-Money Laundering’ 5 FT (2018) ‘AI in banking reality behind the hype’ 2 3  3  All speeches are available online at www.bankofengland.co.uk/publications/Pages/speeches/default.aspx  3  technology infrastructure; and a lack of trust in the insights of AI, for example.6 Despite the plethora of anecdotal evidence on the adoption of AI/ML, there is little structured evidence about UK financial services. To gather more evidence, the Bank of England and the FCA sent a survey in March to more than 200 firms, including the most significant banks, building societies, insurance companies and financial market infrastructure firms in the UK. This is the first systematic survey of AI/ML adoption in financial services. The survey is focused on building our understanding of key themes. First, the extent to which firms have adopted, or are intending to adopt, AI/ML within their businesses and what they regard as the most promising use cases. Second, the extent to which firms have clearly articulated strategies towards the adoption of AI/ML.