Article by Gavin Freeguard: “Our first question is less, ‘to what extent can AI improve public policymaking?’, but ‘what is currently wrong with policymaking?’, and then, ‘is AI able to help?’.
Ask those in and around policymaking about the problems and you’ll get a list likely to include:
- the practice not having changed in decades (or centuries)
- it being an opaque ‘dark art’ with little transparency
- defaulting to easily accessible stakeholders and evidence
- a separation between policy and delivery (and digital and other disciplines), and failure to recognise the need for agility and feedback as opposed to distinct stages
- the challenges in measuring or evaluating the impact of policy interventions and understanding what works, with a lack of awareness, let alone sharing, of case studies elsewhere
- difficulties in sharing data
- the siloed nature of government complicating cross-departmental working
- policy asks often being dictated by politics, with electoral cycles leading to short-termism, ministerial churn changing priorities and personal style, events prompting rushed reactions, or political priorities dictating ‘policy-based evidence making’
- a rush to answers before understanding the problem
- definitional issues about what policy actually is making it hard to get a hold of or develop professional expertise.
If we’re defining ‘policy’ and the problem, we also need to define ‘AI’, or at least acknowledge that we are not only talking about new, shiny generative AI, but a world of other techniques for automating processes and analysing data that have been used in government for years.
So is ‘AI’ able to help? It could support us to make better use of a wider range of data more quickly; but it could privilege that which is easier to measure, strip data of vital context, and embed biases and historical assumptions. It could ‘make decisions more transparent (perhaps through capturing digital records of the process behind them, or by visualising the data that underpins a decision)’; or make them more opaque with ‘black-box’ algorithms, and distract from overcoming the very human cultural problems around greater openness. It could help synthesise submissions or generate ideas to brainstorm; or fail to compensate for deficiencies in underlying government knowledge infrastructure, and generate gibberish. It could be a tempting silver bullet for better policy; or it could paper over the cracks, while underlying technical, organisational and cultural plumbing goes unfixed. It could have real value in some areas, or cause harms in others…(More)”.