Democracy Strategy

Policy for the future

Good policy comes from good policy making. There is plenty of evidence that good policy making is based not just on the rigorous analysis and evidence assessment which is the best of the traditional approach, but also on effective implementation and deep understanding of the needs and behaviour of those who will be affected by the policy. This post argues for a more broadly based approach to policy making, drawing on The Blunders of Our Governments (which remains compelling reading) to make the case.

The problem with this is not – as the author supposes – that it sounds fanciful – it is that it sounds obvious. The problem is not in recognising that policy were better done differently, it is in the doing of it. The post introduces a more substantial paper which has some useful material, but ends up describing barriers to change without offering much about how to overcome them.

Jake Thorold – RSA

Data and AI

NYU Law’s Algorithms and Explanations

This is a neat summary of questions and issues around the explicability of algorithms, in the form of an account of a recent academic conference. The author sums up his own contribution to the debate pithily and slightly alarmingly:

Modern machine learning: We train the wrong models on the wrong data to solve the wrong problems & feed the results into the wrong software

There is a positive conclusion that there is growing recognition of the need to study the social impacts of machine learning – which is clearly essential from a public policy perspective – but with concern expressed that multidisciplinary research in this area lacks a clear home.

Zachary Lipton – Approximately Correct