Monday, 25 June 2018

On the Role of Statistics in Miscarriages of Justice


I have been invited by Jon Robins (the Justice Gap) to speak today at the third meeting of the All-Party Parliamentary Group on Miscarriages of Justice, hosted by Barry Sheerman MP, in the House of Commons. Jon Robins will be talking about his outstanding new book "Guilty Until Proven Innocent: The Crisis in Our Justice System" at the event. The book includes a description of the Ben Geen case for which I provided a report to the Criminal Cases Review Commission in 2015 showing that the sequence of 'unusual events' at the Horton General Hospital (where Ben Geen worked as a nurse) was not especially unusual.

My short talk today focuses on the role of statistics in miscarriages of justice. A transcript of the talk can be found here.

Norman Fenton

See also

Friday, 22 June 2018

Bias in AI Algorithms


This is an update of a posting originally made on  18 Jan 2018 (see below for the update)

On 17 Jan 2018 multiple news sources (e.g. see here, here, and here) ran a story about a new research paper ‎ that claims to expose both the inaccuracies and racial bias in COMPAS - one of the most common algorithms used for parole and sentencing decisions to predict recidivism (i.e. whether or not a defendant will re-offend).

The research paper was written by the world famous computer scientist Hany Farid (along with a student Julia Dressel).

But the real story here is that the paper’s accusation of racial bias (specifically that the algorithm is biased against black people) is based on a fundamental misunderstanding of causation and statistics. The algorithm is no more ‘biased’ against black people than it is biased against white single parents, ‎ old people, people living in Beattyville Kentucky, or women called ‘Amber’. In fact, as we show in this brief article, if you choose any factor that correlates with poverty you will inevitably replicate the statistical ‘bias’ claimed in the paper. And if you accept the validity of the claims in the paper then you must also accept, for example, that a charity which uses poverty as a factor to identify and help homeless people is being racist because it is biased against white people (and also, interestingly, Indian Americans).

The fact that the article was published and that none of the media running the story realise that they are pushing fake news is what is most important here. Depressingly, many similar research studies involving the same kind of misinterpretation of statistics result in popular media articles that push a false narrative of one kind or another.

22 June 2018 Update: It turns out that now Microsoft is "developing a tool to help engineers catch bias in algorithms" This article also cites the case of the COMPAS software:
 "...., which uses machine learning to predict whether a defendant will commit future crimes, was found to judge black defendants more harshly than white defendants." 
Interestingly, this latest news article about Microsoft does NOT refer to the 2018 Dressel and Fardi article but, rather, to an earlier 2016 article by Larson et al: https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm From a quick inspection it does seem to be a more comprehensive study than the flawed Dressel and Farid article. But my quick impression is that the same fundamental misunderstandings statistics/causality are there. Given the great degree of interest in AI/bias, and given also that we were unaware of the 2016 study, we plan to do an update to our unpublished paper.

Our article (5 pages): Fenton, N.E., & Neil, M. (2018). "Criminally Incompetent Academic Misinterpretation of Criminal Data - and how the Media Pushed the Fake News" http://dx.doi.org/10.13140/RG.2.2.32052.55680  Also available here.

The research paper: Dressel, J. & Farid, H. The accuracy, fairness, and limits of predicting recidivism. Sci. Adv. 4, eaao5580 (2018). 

Thanks to Scott McLachlan for the tip off on this story.

See some previous articles on poor use of statistics:

Wednesday, 20 June 2018

New project: Bayesian Artificial Intelligence for Decision Making under Uncertainty


Anthony Constantinou - a lecturer based in the Risk and Information Management Group at Queen Mary University of London - has been awarded a prestigious 3-year EPSRC Fellowship Grant £475,818 in partnership with Agena Ltd to develop open-source software that will enable end-users to quickly and efficiently generate Bayesian Decision Networks (BDNs) for optimal real-world decision-making. BDNs are Bayesian Networks augmented with additional functionality and knowledge-based assumptions to represent decisions and associated utilities that a decision maker would like to optimize. BDNs are suitable for modelling real-world situations where we seek to discover the optimal decision path to maximise utilities of interest and minimise undesirable risk.

A full description of the project can be found here. The EPSRC announcement is here.
 
Links

Thursday, 24 May 2018

The limitations of machine learning


Readers of this and our other blogs will be aware that we have long been sceptical of the idea that 'big data' - coupled with clever machine learning algorithms - will be able to achieve improved decision-making and risk assessment as claimed (see links below). We believe that a smart data approach that combines expert judgment (including understanding of underlying causal mechanisms) with relevant data is required and that Bayesian Networks (BNs) provide an ideal formalism for doing this effectively.

Turing award winner Judea Pearl, who was the pioneer of BNs, has just published a new book "The Book of Why: The New Science of Cause and Effect", which delivers essentially this same message. And there is a great interview with Pearl in the Atlantic Magazine about the book and his current views. The article includes the following:
As he sees it, the state of the art in artificial intelligence today is merely a souped-up version of what machines could already do a generation ago: find hidden regularities in a large set of data. “All the impressive achievements of deep learning amount to just curve fitting,” he said recently.
Read it all.

The interview also refers to the article "Human-Level Intelligence or Animal-Like Abilities?" by Adnan Darwiche. This is an outstanding paper (8 pages) that explains in more detail why we do not need to be over impressed by deep learning.

Links
Norman gets his hands on Pearl's new book
p.s. The second edition of our book:  Fenton, N.E. and M. Neil, "Risk Assessment and Decision Analysis with Bayesian Networks" (with foreword by Judea Pearl) will be available August 2018. See this space.

Friday, 4 May 2018

Anthony Constantinou's football prediction system wins second spot in international competition


Anthony Constantinou
QMUL lecturer Dr Anthony Constantinou of the RIM research group has come second in an international competition to produce the most accurate football prediction system. Moreover, the winners (whose predictive accuracy was only very marginally better) actually based their model on the previously published pi-ratings system of Constantinou and Fenton.





Anthony's model Dolores was developed for the International Machine Learning for Soccer Competition hosted by the Machine Learning journal.

All participants were provided with the results of matches from 52 different leagues around the world - with some missing data as part of the challenge. They had to produce a single model before the end of March 2017 that would be tested on its accuracy of predicting 206 future match outcomes from 26 different leagues, played from March 31 to April 9 in 2017.

Dolores was ranked 2nd with a predictive accuracy almost the same as the top ranked system (there was less than 1% error rate difference between the two; the error rate was nearly 120% lower than the participants ranked lowest among those that passed the basic criteria).

Dolores is  designed to predict football match outcomes in one country by observing football matches in multiple other countries.It is based on a) dynamic ratings and b) Hybrid Bayesian Networks.

Unlike past academic literature which tends to focus on a single league or tournament, Dolores provides empirical proof that a model can make a good prediction for a match outcome between teams 𝑥 and 𝑦 even when the prediction is derived from historical match data that neither 𝑥 nor 𝑦 participated in. This implies that we can still predict, for example, the outcome of English Premier League matches, based on training data from Japan, New Zealand, Mexico, South Africa, Russia, and other countries in addition to data from the English Premier league.

The Machine Learning journal has published the descriptions of the highest ranked systems in its latest issue published online today. The full reference for Anthony's paper is:

Constantinou, A. (2018). Dolores: A model that predicts football match outcomes from all over the world. Machine Learning, 1-27, DOI: https://doi.org/10.1007/s10994-018-5703-7

The full published version can be viewed (for free) at https://rdcu.be/Nntp. An open access pre-publication version (pdf format) is available for download here.

This work was partly supported by the European Research Council (ERC), research project ERC-2013-AdG339182-BAYES_KNOWLEDGE
The DOLORES Hybrid Bayesian Network was built and run using the AgenaRisk software.

The full reference for the pi-ratings model (used by the competition's winning team) is:
Constantinou, A. C. & Fenton, N. E. (2013). Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries. Journal of Quantitative Analysis in Sports. Vol. 9, Iss. 1, 37–50. DOI: http://dx.doi.org/10.1515/jqas-2012-0036
Open access version here.
See also:

Tuesday, 17 April 2018

Explaining Bayesian Networks through a football management problem


Today's Significance Magazine (the magazine of the Royal Statistical Society and the American Statistical Association) has published an article by Anthony Constantinou and Norman Fenton that explains, through the use of an example from football management, the kind of assumptions required to build useful Bayesian networks (BNs) for complex decision-making. The article highlights the need to fuse data with expert knowledge, and describes the challenges in doing so. It also explains why, for fully optimised decision-making, extended versions of BNs, called Bayesian decision networks, are required.

The published pdf (open source) is also available here and here.

Full article details:
Constantinou, A., Fenton, N.E, "Things to know about Bayesian networks", Significance, 15(2), 19-23 April 2018, https://doi.org/10.1111/j.1740-9713.2018.0