Machine Learning Meets Public Policy: What to Expect and How to Cope

Ed Felten

This is the video of a conference talk by Ed Felten, which is fascinating for a number of reason. He has been thinking hard about technology and the policy consequences of technology for a very long time, and doing so with deep technical expertise (on the explicability of algorithms, to take just one example).

But he also has been at the heart of the intersection of technology and public policy – a one man One Team Government – including a couple of years in the Obama White House. This talk is primarily about how machine learning lands in a public policy context and is immediately addressed to an audience at a big AI conference, whose perspective can be assumed to be technical.

Given that, the starting point is to underline a critical difference in perspective. At least in principle, science and engineering are about a search for truth. Democracy is not just not a search for truth, it is not really a search for anything. And that difference is simultaneously obvious, a strength and a source of deep confusion and misunderstanding

Democracy is not a search for truth; it is an algorithm for resolving disagreements

But this talk is interesting not just to an audience of technologist having the world of public policy explained by one of their own who has ventured into a strange and distant land. Given the importance of AI and machine learning – and indeed technology change more generally – to almost every aspect of policy, it is jut as important for policy makers and players in the democratic process to understand how their world is perceived. And from that perspective, this is a fascinating account of a strange world by a participant-observer who has retained his distance and brings a distinct professional perspective.

Toolkit Navigator

OECD Observatory of Public Sector Innovation

This rather mundane title is the gateway to a rich set of resources – a compendium of tools for public sector innovation and transformation, as the site’s subtitle has it. It’s a library organised by topics and actions, as well as supporting connections between people working on public sector innovation round the world. It’s very richness has the potential to be a bit overwhelming, so it’s well worth starting with a very clear blog post by Angela Hanson which introduces the approach OECD has taken.

Attempting to teach parliamentary procedure to machines

Michael Smethurst

There’s no getting away from the fact that parliamentary procedure is pretty arcane and that modelling that procedure adds a still more arcane overlay. But this is a beautifully reflective post which wears deep expertise very lightly to share thinking which is relevant well beyond the immediate parliamentary context.

Two points which should resonate far beyond the Palace of Westminster are worth pulling out. One is that parliamentary processes may have some extreme characteristics, but they also have some characteristics which people involved with other kinds of information flows will instantly recognise. It may or may not be possible to express definitively how the system should work; for different reasons it may or may not be possible to capture in detail how it does work, particularly if that is in some circumstances indeterminate. But taking an almost anthropological approach to understanding systems is both an art form and an investment which needs to be made.

The second is that for all the power of starting with user needs, that is necessarily limited if some kinds of needs come into being only as a result of building a system which satisfies them. In a nice nod to George Box, the post ends with a bold claim for the art of system modelling:

The models are only ever maps, but if they’re good enough to be useful they can be useful in ways the map designers never considered. No amount of requirements gathering or user research will ever compensate for omitting the work on modelling, because user needs are emergent from use and emergent from materials.

Too many projects, too much change

Naomi Stanford

Prioritisation is hard. One reason why it’s hard is that starting new things is always more attractive than stopping old ones. There are all sorts of reasons for that – many nicely set out in this post – which include the ease with which we overlook the opportunity cost: if we start this new thing, what do we no longer have the capacity or attention span to do? That of course is a problem for the organisation as a whole, not for the proponents of the new shiny thing, so it all too easily becomes one which is brushed aside, because there isn’t anybody whose job is to address it.

There is a closely related problem, pithily described, it appears, by Kurt Vonnegut:

Another flaw in the human character is that everybody wants to build and nobody wants to do maintenance.

That can have consequences from the irritatingly inefficient to the utterly terrifying, but all contributing to the wider problem, that the more change there is going on, the more likely it is that the changes will collide with each other unproductively, and the more it becomes important to understand and manage the dependencies and interactions between projects, as much as to understand and manage each of the contributing initiatives.