1 month, 7 days ago

From Dodgy Data to Dodgy Policy – Mrs May’s Immigration Targets

The TotalData™ value chain is about the flow from raw data to business decisions (including evidence-based policy decisions).

In this post, I want to talk about an interesting example of a flawed data-driven policy. The UK Prime Minister, Theresa May, is determined to reduce the number of international students visiting the UK. This conflicts with the advice she is getting from nearly everyone, including her own ministers.

As @Skapinker explains in the Financial Times, there are a number of mis-steps in this case.

  • Distorted data collection. Mrs May’s policy is supported by raw data indicating the number of students that return to their country of origin. These are estimated measurements, based on daytime and evening surveys taken at UK airports. Therefore students travelling on late-night flights to such countries as China, Nigeria, Hong Kong, Saudi Arabia and Singapore are systematically excluded from the data.
  • Disputed data definition. Most British people do not regard international students as immigrants. But as May stubbornly repeated to a parliamentary committee in December 2016, she insists on using an international definition of migration, which includes any students that stay for more than 12 months.
  • Conflating measurement with target. Mrs May told the committee that “the target figures are calculated from the overall migration figures, and students are in the overall migration figures because it is an international definition of migration”. But as Yvette Cooper pointed out “The figures are different from the target. … You choose what to target.”
  • Refusal to correct baseline. Sometimes the easiest way to achieve a goal is to move the goalposts. Some people are quick to use this tactic, while others instinctively resist change. Mrs May is in the latter camp, and appears to regard any adjustment of the baseline as backsliding and morally suspect.

If you work with enterprise data, you may recognize these anti-patterns.


David Runciman, Do your homework (London Review of Books Vol. 39 No. 6, 16 March 2017)

Michael Skapinker, Theresa May’s clampdown on international students is a mystery (Financial Times, 15 March 2017)

International students and the net migration target: Should students be taken out? (Migration Observatory, 25 Jun 2015)

Oral evidence: The Prime Minister (House of Commons HC 833, 20 December 2016) 


TotalData™ is a trademark of Reply Ltd. All rights reserved

3 months, 22 days ago

The Unexpected Happens

When Complex Event Processing (CEP) emerged around ten years ago, one of the early applications was real-time risk management. In the financial sector, there was growing recognition for the need for real-time visibility – continuous calibration of positions – in order to keep pace with the emerging importance of algorithmic trading. This is now relatively well-established in banking and trading sectors; Chemitiganti argues that the insurance industry now faces similar requirements.

In 2008, Chris Martins, then Marketing Director for CEP firm Apama, suggested considering CEP as a prospective “dog whisperer” that can help manage the risk of the technology “dog” biting its master.

But “dog bites master” works in both directions. In the case of Eliot Spitzer, the dog that bit its master was the anti money-laundering software that he had used against others.

And in the case of algorithmic trading, it seems we can no longer be sure who is master – whether black swan events are the inevitable and emergent result of excessive complexity, or whether hostile agents are engaged in a black swan breeding programme.  One of the first CEP insiders to raise this concern was John Bates, first as CTO at Apama and subsequently with Software AG. (He now works for a subsidiary of SAP.)

from Dark Pools by Scott Patterson

And in 2015, Bates wrote that “high-speed trading algorithms are an alluring target for cyber thieves”.

So if technology is capable of both generating unexpected events and amplifying hostile attacks, are we being naive to imagine we use the same technology to protect ourselves?

Perhaps, but I believe there are some productive lines of development, as I’ve discussed previously on this blog and elsewhere.

1. Organizational intelligence – not relying either on human intelligence alone or on artificial intelligence alone, but looking for establishing sociotechnical systems that allow people and algorithms to collaborate effectively.

2. Algorithmic biodiversity – maintaining multiple algorithms, developed by different teams using different datasets, in order to detect additional weak signals and generate “second opinions”.


John Bates, Algorithmic Terrorism (Apama, 4 August 2010). To Catch an Algo Thief (Huffington Post, 26 Feb 2015)

John Borland, The Technology That Toppled Eliot Spitzer (MIT Technology Review, 19 March 2008) via Adam Shostack, Algorithms for the War on the Unexpected (19 March 2008)

Vamsi Chemitiganti, Why the Insurance Industry Needs to Learn from Banking’s Risk Management Nightmares.. (10 September 2016)

Theo Hildyard, Pillar #6 of Market Surveillance 2.0: Known and unknown threats (Trading Mesh, 2 April 2015)

Neil Johnson et al, Financial black swans driven by ultrafast machine ecology (arXiv:1202.1448 [physics.soc-ph], 7 Feb 2012)

Chris Martins, CEP and Real-Time Risk – “The Dog Whisperer” (Apama, 21 March 2008)

Scott Patterson, Dark Pools – The Rise of A. I. Trading Machines and the Looming Threat to Wall Street (Random House, 2013). See review by David Leinweber, Are Algorithmic Monsters Threatening The Global Financial System? (Forbes, 11 July 2012)

Richard Veryard, Building Organizational Intelligence (LeanPub, 2012)

Related Posts

The Shelf-Life of Algorithms (October 2016)

5 months, 29 days ago

85 Million Faces

It should be pretty obvious why Microsoft wants 85 million faces. According to its privacy policy

Microsoft uses the data we collect to provide you the products we offer, which includes using data to improve and personalize your experiences. We also may use the data to communicate with you, for example, informing you about your account, security updates and product information. And we use data to help show more relevant ads, whether in our own products like MSN and Bing, or in products offered by third parties. (retrieved 25 October 2016)

Facial recognition software is big business, and high quality image data is clearly a valuable asset.

But why would 85 million people go along with this? I guess they thought they were just playing a game, and didn’t think of it in terms of donating their personal data to Microsoft. The bait was to persuade people to find out how old the software thought they were.

The Daily Mail persuaded a number of female celebrities to test the software, and printed the results in today’s paper.

Computer”tell yr age” programme on my face puts me 69 https://t.co/EhEog5LQcN Haha!But why are those judged younger than they are so pleased

— mary beard (@wmarybeard) October 25, 2016

Talking of beards …

. @futureidentity If we ever reach peak data, advertisers will check photos before advertising beard accessories #personalization #TotalData

— Richard Veryard (@richardveryard) April 1, 2016

. @futureidentity So, did you ever buy that right-handed beard brush? #PeakHipster #Sinister https://t.co/kESqmUooNk #CISNOLA cc @mfratto

— Richard Veryard (@richardveryard) June 8, 2016


Kyle Chayka, Face-recognition software: Is this the end of anonymity for all of us? (Independent, 23 April 2014)

Chris Frey, Revealed: how facial recognition has invaded shops – and your privacy (Guardian, 3 March 2016)

Rebecca Ley, Would YOU  dare ask a computer how old you look? Eight brave women try out the terrifyingly simple new internet craze (Daily Mail, 25 October 2016)


TotalData™ is a trademark of Reply Ltd. All rights reserved

8 months, 16 days ago

New White Paper – TotalData™

My latest white paper for @GlueReply has been posted on the Reply website.


It outlines four dimensions of TotalData™ – reach, richness, assurance and agility – and presents a Value Chain from Raw Data to the Data-Fueled Business.

TotalData™: Start making better use of Data (html) (pdf)

(Now I need to write some more detailed stuff, based on a few client projects.)


TotalData™ is a trademark of Reply Ltd. All rights reserved

2 years, 5 months ago

Working for the Machine

#orgintelligence The recent appointment of an algorithm to a Board of Directors raises the spectre of science fiction becoming fact. Although many commentators regarded the appointment as a publicity stunt, there has always been an undercurrent of fear about machine intelligence. Even the BBC (following Betteridge’s Law of Headlines) succumbed to the alarmist headline Could a big data-crunching machine be your boss one day?

There are several useful ways that an algorithm might contribute to the collective intelligence of a Board of Directors. One is to provide an automated judgement on some topic, which can be put into the pot together with a number of human judgements. This is what seems to be planned by the company Deep Knowledge Ventures, whose Board of Directors is faced with a series of important investment decisions. Although each decision is unique, there are some basic similarities in the decision process that may be amenable to automation and machine learning.

Another possible contribution is to evaluate other board members. According to the BBC article, IBM Watson could be programmed to analyse the contributions made by each board member for usefulness and accuracy. There are several ways such a feedback loop could enhance the collective intelligence of the Board.

  • Retrain individuals to improve their contributions in specific contexts.
  • Identify and eliminate individuals whose contribution is weak.
  • Identify and eliminate individuals whose contribution is similar to other members. In other words, promote greater diversity.
  • Enable trial membership of individuals from a wider range of backgrounds, to see whether they can make a valuable contribution.

Organizational Intelligence is about an effective combination of human/social intelligence and machine intelligence. Remember this when people try to develop an either-us-or-them narrative.


#QTWTAIN

Jamie Bartlett, Will Artificial Intelligence put my job at risk? (Spectator 6 June 2014)

Adrian Chen, Can an Algorithm Solve Twitter’s Credibility Problem? (New Yorker 5 May 2014)

John Rentoul, Will Artificial Intelligence put my job at risk? (Independent 6 June 2014)

Richard Veryard, Does Cameron’s Dashboard App Improve the OrgIntelligence of Government? (23 January 2013)

Matthew Wall, Could a big data-crunching machine be your boss one day? (BBC News 9 October 2014)


Other Sources

Algorithm appointed board director (BBC News 16 May 2014)