I had the chance to take part within the Inaugural AI Industrial Lifecycle and Procurement Summit 2024 hosted by Curshaw. This was a really attention-grabbing ‘unconference’ the place individuals provided to guide periods on subjects 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, on condition that the ‘large promise’ of AI is that it’s going to scale back friction (eg by way of automation, and/or delegation of ‘non-value-added’ duties). Why would I wish 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The primary clarification I used to be thus requested for was whether or not this was about ‘good friction’ (versus outdated unhealthy ‘purple tape’ sort of friction), which after all it was (?!), and the second, what do I imply by friction.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 deliver again to the desk the ‘non-AI’ possibility and several other ‘cease challenge’ or ‘deal breaker’ trumps to push again in opposition to the tidal wave of unavoidability that appears to dominate all discussions on public sector digitalisation. My most popular resolution is to take action by way of a system of permissioning or licencing administered by an impartial authority—however I’m conscious and prepared to concede that there is no such thing as a political will for it. I thus began eager about second-best approaches to slowing public sector AI procurement. That is how I obtained to the concept of friction.
By creating friction, I imply the necessity for a structured decision-making course of that enables for collective deliberation inside and across the adopting establishment, and which is supported by rigorous affect assessments that tease out second and third order implications from AI adoption, in addition to completely interrogating first order points round knowledge high quality and governance, technological governance and organisational functionality, specifically round threat administration and mitigation. That is complementary—however hopefully goes past—rising frameworks to find out organisational ‘threat urge for food’ for AI procurement, equivalent to that developed by the AI Procurement Lab and the Centre for Inclusive Change.
The conversations the concentrate on ‘good friction’ moved in several 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), equivalent to (in no explicit order of significance or potential):
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the potential for ‘AI minimisation’ or ‘non-AI equivalence’ to check the necessity for (particular) AI options—in the event you can sufficiently approximate, or replicate, the identical purposeful end result with out AI, or with a less complicated kind of AI, why not do it that means?;
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the necessity for a structured catalogue of options (and parts of options) which might be already accessible (generally in open entry, the place there may be numerous duplication) to tell such concerns;
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the significance of asking whether or not procuring AI is pushed by concerns equivalent to availability of funding (is that this funded if achieved with AI however not funded, or onerous to fund on the similar degree, if achieved in different methods?), which may clearly skew decision-making—the significance of contemplating the consequences of ‘digital industrial coverage’ on decision-making;
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the facility (and relevance) of the deceptively easy query ‘is there an interdisciplinary workforce to be devoted to this, and completely to this’?;
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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 ‘stylish phrases’ (equivalent to ‘agile’ or ‘accountable’) could or could not result in the award of the contract to the best-placed and best-fitting (tech) supplier;
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the chance to adapt civic monitoring or social witnessing mechanisms utilized in different contexts, equivalent to massive infrastructure initiatives, to be embedded in contract efficiency and auditing phases;
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the significance of understanding displacement results and whether or not deploying an answer (AI or automation, or comparable) to cope with a bottleneck will merely displace the problem to a different (new) bottleneck someplace alongside the method;
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the significance of understanding the broader organisational modifications required to seize the hoped for (productiveness) positive aspects arising from the tech deployment;
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the significance of rigorously contemplating and resourcing the a lot wanted engagement of the ‘clever particular person’ that should examine 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;
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relatedly, the necessity to guarantee organisational engagement and alignment at each degree and each step of the AI (pre)procurement course of (on which I might advocate studying this latest piece by Kawakami and colleagues);
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the necessity to assess the impacts of modifications in scale, complexity, and error publicity;
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the necessity to create satisfactory circuit-breakers all through the method.
Actually heaps to mirror on and attempt to embed in future analysis and outreach efforts. Due to all those that participated within the dialog, and to these enthusiastic about becoming a member of it. A structured means to take action is thru this LinkedIn group.
