LLMs can increase demand on public systems by removing the friction that previously limited access. One potential result of this is new forms of unconsidered rationing that recreate that friction. Instead, we should move away from zero sum systems and aim for technical and policy approaches that turn unscalable private benefits into efficient collective ones. 

Many kinds of citizen-driven interactions with the public sector are rationed through friction: fewer people engage in them than might do otherwise, because they feel the process is time-consuming or requires expertise. We can see examples of this in planning objections, correspondence with elected representatives, FOI requests and consultation responses. LLM technologies can lower the time or expertise required and also prompt people to engage in the processes in the first place: “Would you like me to draft a complaint about this?”

Systematic impacts

This reduced friction may be good for individuals, but the resulting increase in engagement can overwhelm the system itself. In response, it may slow down, collapse, or adopt new means of rationing or prioritising access. We can see indications of this across different kinds of interactions: journalist Martin Rosenbaum has identified an upward trend across public sector complaints organisations, and concerns are being raised across sectors about AI’s contribution to growth in volumes. 

So how should organisations that handle public submissions respond in an informed way? Here’s an approach to thinking about the problem. We can divide these interactions into three types:

• Private benefit – when an interaction has a benefit almost exclusively to the requester, either competitively (eg a grant or job application), or non-competitively (eg an application for a state benefit).
• Collective benefit – when an interaction has a benefit to the requester, and also to wider society (eg a public FOI request, reporting a pothole). 
• Zero sum interaction – when an interaction success for one person is a failure for another (eg planning). 

Private benefits

Some public services fall clearly in the first category: they are unavoidably a collection of private interactions. For these, there might be improved efficiencies to be found in delivery at scale but, particularly for non-competitive benefits, these are also likely to eventually run into decisions either about increasing provision (assuming a higher level of claims from those entitled going forward), or new forms of rationing.

As stands, AI inputs can both improve the efficiency of systems through sharper, more complete initial submissions, but can also make more verbose and complex submissions that cite non-existent law. To prioritise the former over the latter, systems can explore triage approaches that enforce or encourage the qualities that make input valuable: clarity, accuracy and concision. 

When running into real limits, it is important to be clear about the criteria you want to ration on, and that they are in line with the overall purpose of the system, rather than implicitly prioritising those with greater resources.  In their FOI complaints system, the ICO is using public benefit as a criteria for prioritisation. The British Academy uses partial randomisation above a scoring cutoff to ration randomly rather than requiring additional work (on both sides) to further differentiate.

Collective benefits

A bigger win is, where possible, to transform private benefits into collective benefits.  In these cases, reduced friction is self-regulating because spillover benefits from an individual’s case help reduce demand from others : the private benefit person B is looking for has already been provided by person A’s interaction. 

One of the key ways mySociety’s services help people is to harness the self-interest of individual users for collective benefits. Every public request made on WhatDoTheyKnow also adds to the pool of public knowledge accessible on the internet, reducing the need for duplicate requests (with a similar logic to reducing duplicate reports on FixMyStreet). This means we can effectively lower the bar to access while improving overall efficiency of the system. 

We come to this from a technology lens, but the same principles apply from an institutional-design approach. For instance, if MPs’ casework or complaints are  increasing, you want to shift towards more systematic rather than individual benefits from casework. This looks like support for better collective learning, and an improved ombudsman to support collective rather than individual fixes. This kind of approach works best where good statistics are collected at a system level to help identify what collective changes are needed: tracking the overall level of demand, level of demand to different parts of the system and nature of the demand, ie what are people asking for.

Zero sum systems

The biggest shift needed is in reforming zero-sum systems, where there is currently an incentive for both sides to escalate the volume. Reduced friction here just raises costs for all concerned rather than giving increased benefits to anyone. Individual use of AI to create submissions is individually enabling in these cases, but not collectively. So, in the words of the 1980s classic film War Games, “the only winning move is not to play”. The real innovation is in solutions that open up new, and more effective, ways of working out what everyone can live with, rather than recreating rationing through new means. For instance, rather than adversarial AI planning objection generators, we could aim for a collaborative planning system that through improved communication and coordination lowers costs and removes incentives to volumes of engagement. 

Red flags for zero sum interactions are when volume is implicitly being used as a proxy for strength of feeling, or popularity of a particular viewpoint, because its value as a signal is going to become increasingly degraded as AI use increases.   

Systems work better when the benefits are collective rather than atomised

Mass adoption of AI removes one set of bottlenecks, but this can create capacity challenges for public systems. Previous waves of civic technology have built on reduced costs of storing and sharing information to build systems that help share the benefits of people’s work and lower the barriers to entry.

The current wave of AI chatbots cut against this, encouraging atomised approaches, rather than collective ones. We need to explore technical and policy approaches that help systems better achieve their purpose, without giving up on the idea of lowering barriers to entry. We can do this both by exploring how the technological features of AI tools can be bent towards collective gains, and moving away from systems that incentivise these approaches. 

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Image: Engin Akyurt

If you were one of the 100+ people who joined us for today’s webinar, you’ll already know it was hugely informative and timely.

We packed three fascinating speakers into the course of one hour-long session on using FOI to understand AI-based decision making by public authorities. Each brought so many insights that, even if you were there, you may wish to watch it all over again.

Fortunately, you can! We’ve uploaded the video to YouTube, and you can also access Morgan’s slides on Google Slides, here and Jake’s as a PDF, here (Jake actually wasn’t able to display his slides, so this gives you the chance to view them alongside his presentation, should you wish).

Morgan Currie of the University of Edinburgh kicked things off with a look at her research ‘Algorithmic Accountability in the UK’, and especially how opaque the Department of Work and Pensions (DWP)’s use of automation for fraud detection has been, over the years.

Morgan explains the techniques used to gain more scrutiny of these decision-making and risk assessment processes, with much of the research based on analysing FOI requests made by others on WhatDoTheyKnow, which of course are public for everyone to see.

Secondly, in a pre-recorded session, Gabriel Geiger from Lighthouse Reports gave an overview of their Suspicion Machines Investigation which delves into the use of AI across different European welfare systems. Shockingly, but sadly not surprisingly, the investigation found code that was predicting which recipients of benefits are most likely to be committing fraud, with an inbuilt bias against minoritised people, women and parents — multiplied for anyone who falls into more than one of those categories.

Gabriel also outlined a useful three-tiered approach to this type of investigation, which others will be able to learn from when instigating similar research projects.

Our third speaker was Jake Hurfurt of Big Brother Watch, who spoke of the decreasing transparency of our public bodies when it comes to AI-based systems, and the root causes of it: a lack of technical expertise among smaller authorities and the contracting of technology from private suppliers. Jake was in equal parts eloquent and fear-inducing about what this means for individuals who want to understand the decisions that have been made about them, and hold authorities accountable — but he also has concrete suggestions as to how the law must be reformed to reflect the times we live in.

The session rounded off with a brief opportunity to ask questions, which you can also watch in the video.

Presented in collaboration with our fellow transparency organisations AccessInfo Europe and Frag Den Staat, this session was an output of the ATI Community of Practice.

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Image: Michael Dziedzic