How to eliminate organizational debt

The idea of ‘technical debt’ has been around for a long time. It’s all the things you should have done to write clean code and clear documentation, to have tested everything in combination with everything else – but never quite got round to doing. The thing you built may well work, but at some point somebody is going to have to clear up the mess – so you have a debt until the time and cost of doing that have been met. There’s a clear parallel with organisations: the way they do things has all too often got disconnected from what the organisation wants and needs to get done.. So there’s a constant drag on delivery until the organisation can get itself better aligned to its current needs. That’s organisational debt – and it isn’t cheap or easy to pay it off.

Two bonus Dilbert cartoons included which make the point all too clearly.

Aaron Dignan – The Ready

Policy v5.127: Could government make services like Dyson makes vacuum cleaners?

A good summary of the people and methods bringing design thinking into government, with mini case studies of where it’s starting to make a difference. The provocative question in the title never quite gets answered, but there is a bit of a flavour that while there’s been a lot of progress in some areas, the vacuum cleaner of government itself hasn’t moved far beyond version 1.0.

John Lapping – Pieria

In praise of cash

Does paying for things by card (and phone and watch and…) represent liberation from the need to carry coins around and enable faster, simpler transactions? Or is it a dangerous slide towards the privatisation of money and the advent of universal financial surveillance? And, most importantly it seems, can you get a coke from a machine when you want one?

Brett Scott – Aeon

Machine intelligence makes human morals more important

Zeynep Tufekci is a computer programmer turned sociologist, whose book Twitter and Tear Gas is coming out in a couple of months. This TED talk is the video parallel of Cathy O’Neil’s book, Weapons of Math Destruction. The core point is the same – that machines we don’t understand, trained on imperfect data, are as likely to be amplifying human biases as to be emobodying objectivity.

Zeynep Tufekci – TED

Do the hard work to help people learn

Most advice about presentations (and powerpoint) assumes you are standing between a large audience and a big screen, recounting a single narrative with a beginning middle and end. This post is about when you are having a conversation with a small group, when it’s faciliation as much as presentation.

Lots of good advice, including most critically, when powerpoint is just the wrong medium. Now where’s the overhead projector and a chinagraph pencil when you really need them…?

Matt Edgar

Should economists be more concerned about artificial intelligence?

There is both growing concern among economists about the potential speed and extent of the disruption caused by automation and also a temptation to draw conclusions from previous industrial revolutions, when apparently similar concerns about apparently similar risks proved unfounded. The not very illuminating conclusion is that it would be a mistake to dismiss the risks too lightly.

Mauricio Armellini and Tim Pike – Bank of England

Weapons of Math Destruction

A polemic against the misuse of big data models by a reformed hedge fund quant – the book’s subtitle, ‘how big data increases inequality and threatens democracy’, is a pretty good indicator of what is to come. Using examples from policing to insurance and teacher evaluation, she shows that the underlying models often encode and reinforce prejudices, rather than being the embodiment of objectivity often claimed for them. It’s very US focused, both in its examples and in its style (a half way decent copy editor could easily make it a third shorter), but it’s a good, simple and readable introduction to some important issues.

Cathy O’Neil – Weapons of Math Destruction

A view from the other side: perspectives on an emergent design culture in Whitehall

An academic case study of the first year of the Cabinet Office Policy Lab, reflecting on how civil servants see design thinking and the emergence of a design culture for policy. That leads to some interesting reflections on the traditonal model (and culture) of policy making, the power of words (particularly when elegantly assembled), and the difficulty of introducing what may appear to be frivolity to the policy making process.

Jocelyn Bailey and Peter Lloyd – Uscreates/University of Brighton

The Automation Argument for a basic income. Does it add up?

A dissection of the ‘automation argument’ for a basic income – interesting not so much for arguing that automation won’t lead to a life of well-rewarded idleness as for suggesting that a basic income is an inadequate, and ultimately very conservative, approach to the problems automation might bring. Also notable for including a reference to the shoe event horizon.

Katharina Nieswandt – World Economic Forum

The future of not working

Universal basic income – examined not in Scandinavia but in rural Kenya. This is either Silicon Valley on a very long distance guilt trip or a radical approach to extreme poverty. Are there implications for rich countries?

And despite the title, this isn’t really about not working at all – all the stories are about people being liberated to work smarter once given the margin which allows them to make the change.

Annie Lowrey – New York Times