Creating (optimistic) friction in AI procurement — Tips on how to Crack a Nut – Tech Cyber Internet

I had the chance to take part within the Inaugural AI Business Lifecycle and Procurement Summit 2024 hosted by Curshaw. This was a really attention-grabbing ‘unconference’ the place individuals supplied to steer periods on matters they needed to speak about. I led a session on ‘Creating friction in AI procurement’.

This was clearly a counterintuitive mind-set about AI and procurement, provided that the ‘large promise’ of AI is that it’s going to cut back friction (eg by means of automation, and/or delegation of ‘non-value-added’ duties). Why would I need to create friction on this context?

The primary clarification I used to be thus requested for was whether or not this was about ‘good friction’ (versus outdated unhealthy ‘pink tape’ form of friction), which in fact it was (?!), and the second, what do I imply by friction.

My latest analysis on AI procurement (eg right here and right here for the book-long remedy) has led me to conclude that we have to decelerate the method of public sector AI adoption and to create mechanisms that carry again to the desk the ‘non-AI’ possibility and several other ‘cease challenge’ or ‘deal breaker’ trumps to push again towards the tidal wave of unavoidability that appears to dominate all discussions on public sector digitalisation. My most well-liked resolution is to take action by means of a system of permissioning or licencing administered by an unbiased authority—however I’m conscious and prepared to concede that there isn’t any political will for it. I thus began occupied with second-best approaches to slowing public sector AI procurement. That is how I acquired to the thought of friction.

By creating friction, I imply the necessity for a structured decision-making course of that permits for collective deliberation inside and across the adopting establishment, and which is supported by rigorous influence assessments that tease out second and third order implications from AI adoption, in addition to totally interrogating first order points round information high quality and governance, technological governance and organisational functionality, particularly round danger administration and mitigation. That is complementary—however hopefully goes past—rising frameworks to find out organisational ‘danger urge for food’ for AI procurement, reminiscent of that developed by the AI Procurement Lab and the Centre for Inclusive Change.

The conversations the give attention to ‘good friction’ moved in numerous instructions, however there are some takeaways and concepts that caught with me (or I managed to jot down in my notes whereas chatting to others), reminiscent of (in no specific order of significance or potential):

• the potential for ‘AI minimisation’ or ‘non-AI equivalence’ to check the necessity for (particular) AI options—when you can sufficiently approximate, or replicate, the identical practical final result with out AI, or with a less complicated kind of AI, why not do it that approach?;

• the necessity for a structured catalogue of options (and elements of options) which might be already accessible (typically in open entry, the place there may be a lot of duplication) to tell such issues;

• the significance of asking whether or not procuring AI is pushed by issues reminiscent of availability of funding (is that this funded if finished with AI however not funded, or laborious to fund on the identical stage, if finished in different methods?), which might clearly skew decision-making—the significance of contemplating the consequences of ‘digital industrial coverage’ on decision-making;

• the facility (and relevance) of the deceptively easy query ‘is there an interdisciplinary staff to be devoted to this, and solely to this’?;

• the significance of information and understanding of the tech and its implications from the start, and of experience within the translation of technical and governance necessities into procurement necessities, to keep away from ‘video games of probability’ whereby using ‘fashionable phrases’ (reminiscent of ‘agile’ or ‘accountable’) might or might not result in the award of the contract to the best-placed and best-fitting (tech) supplier;

• the likelihood to adapt civic monitoring or social witnessing mechanisms utilized in different contexts, reminiscent of giant infrastructure tasks, to be embedded in contract efficiency and auditing phases;

• the significance of understanding displacement results and whether or not deploying an answer (AI or automation, or comparable) to take care of a bottleneck will merely displace the problem to a different (new) bottleneck someplace alongside the method;

• the significance of understanding the broader organisational adjustments required to seize the hoped for (productiveness) positive aspects arising from the tech deployment;

• the significance of fastidiously contemplating and resourcing the a lot wanted engagement of the ‘clever particular person’ that should verify the design and outputs of the AI, together with frontline staff and people on the receiving finish of the related choices or processes and the affected communities—the significance of making significant and efficient deliberative engagement mechanisms;

• relatedly, the necessity to guarantee organisational engagement and alignment at each stage and each step of the AI (pre)procurement course of (on which I’d advocate studying this latest piece by Kawakami and colleagues);

• the necessity to assess the impacts of adjustments in scale, complexity, and error publicity;

• the necessity to create satisfactory circuit-breakers all through the method.

Definitely heaps to mirror on and attempt to embed in future analysis and outreach efforts. Because of all those that participated within the dialog, and to these thinking about becoming a member of it. A structured approach to take action is thru this LinkedIn group.