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. if i tell you to shoplift, then i am committing an unethical act - and so are you, if you follow my instruction. “ i was only following orders ” is not a legitimate defence. there is, if you like, a double - lock on unethical instructions within a wholly human environment - on the part of the instructor and the instructed. this is one reason why firms and regulators are so determined to promote ‘ good ’ cultures, including, for example, ‘ speak up ’ cultures, and robust whistle - blowing. but there is no such double - lock for ai / ml. you cannot tell a machine to “ do the right thing ” without somehow first telling it what “ right ” is - nor can a machine be a whistle - blower of its own learning algorithm. in a world of machines, the burden of correct corporate and ethical behaviour is shifted further in the direction of the board, but also potentially further towards more junior, technical staff. in the round this could mean less weight being placed on the judgements of front - office middle management. all speeches are available online at www. bankofengland. co. uk / publications / pages / speeches / default. aspx there have been some initial steps to promote the ethical use of big data and ai / ml in financial services. notably, for example, in singapore, 7 and – more broadly – within the eu. 8 in the uk, the centre for data ethics and innovation is looking at maximising the benefits of ai, 9 and many consider them leaders in this field. principles - based expectations have focused on areas such as fairness, ethics, accountability and transparency. nevertheless, promoting the right outcomes, even if framed as principle - based expectations, will require appropriate, up - to - date systems and controls across the three lines of defence to ensure an appropriate control environment throughout the firm. further thought is needed. the third challenge posed by greater use of ai / ml to boards is around change. as the rate of introduction of ai / ml in financial services looks

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. third, the extent of barriers - regulatory or otherwise - to adoption and what techniques and tools can enable safe use of this technology. fourth, an assessment of firms ’ perceptions of the risks, to both their own safety and soundness as well as to their conduct towards customers and clients, arising from ai / ml. and fifth, the extent to which the appreciation of these risks has given rise to changes in risk management, governance and compliance frameworks. the full results of the survey will be published by the bank and fca in q3 2019, and are likely to prove insightful. responses were returned to the bank in late april, so some early indicative results are emerging. overall, the mood around ai implementation amongst firms regulated by the bank of england is strategic but cautious. four fifths of the firms surveyed returned a response ; many reported that they are currently in the process of building the infrastructure necessary for larger scale ai deployment, and 80 per cent reported using ml applications in some form. the median firm reported deploying six distinct such applications currently, and expected three further applications to go live over the next year, with ten more over the following three years. consistent with the mckinsey survey, barriers to ai deployment currently seem to be mostly internal to firms, rather than stemming from regulation. some of the main reasons include : ( i ) legacy systems and unsuitable it infrastructure ; ( ii ) lack of access to sufficient data ; and ( iii ) challenges integrating ml into existing business processes. large established firms seem to be most advanced in deployment. there is some reliance on external providers at various levels, ranging from providing infrastructure, the programming environment, up to specific solutions. approaches to testing and explaining ai are being developed and, perhaps unsurprisingly, there is some heterogeneity in techniques and tools. firms said that ml applications are embedded in their existing risk mckinsey ( 2018 ) ‘ adoption of ai advances, but foundational barriers remain ’ all speeches are available online at www. bankofengland. co. uk / publications / pages / speeches / default. aspx frameworks. but many say that new approaches to model validation ( which include ai explainability techniques ) are needed in the future. of the firms regulated by the bank of england that responded to the