Category: Big Data

The gender shades audit

by Jo Brodie, Queen Mary University of London

Face recognition technology is used widely, such as at passport controls and by police forces. What if it isn’t as good at recognising faces as it has been claimed to be? Joy Buolamwini and Timnit Gebru tested three different commercial systems and found that they were much more likely to wrongly classify darker skinned female faces compared to lighter or darker skinned male faces. The systems were not reliable.

Different skin tone cosmetics
Image by Stefan Schweihofer from Pixabay

Face recognition systems are trained to detect, classify and even recognise faces based on a bank of photographs of people. Joy and Timnit examined two banks of images used to train the systems and found that around 80 percent of the photos used were of people with lighter coloured skin. If the photographs aren’t fairly balanced in terms of having a range of people of different gender and ethnicity then the resulting technologies will inherit that bias too. The systems examined were being trained to recognise light skinned people.

The pilot parliaments benchmark

Joy and Timnit decided to create their own set of images and wanted to ensure that these covered a wide range of skin tones and had an equal mix of men and women (‘gender parity’). They did this using photographs of members of parliaments around the world which are known to have a reasonably equal mix of men and women. They selected parliaments both from countries with mainly darker skinned people (Rwanda, Senegal and South Africa) and from countries with mainly lighter skinned people (Iceland, Finland and Sweden).

They labelled all the photos according to gender (they had to make some assumptions based on name and appearance if pronouns weren’t available) and used a special scale called the Fitzpatrick scale to classify skin tones (see Different Shades below). The result was a set of photographs labelled as dark male, dark female, light male, light female, with a roughly equal mix across all four categories: this time, 53 per cent of the people were light skinned (male and female).

Testing times

Joy and Timnit tested the three commercial face recognition systems against their new database of photographs (a fair test of a wide range of faces that a recognition system might come across) and this is where they found that the systems were less able to correctly identify particular groups of people. The systems were very good at spotting lighter skinned men, and darker skinned men, but were less able to correctly identify darker skinned women, and women overall. The tools, trained on sets of data that had a bias built into them, inherited those biases and this affected how well they worked.

As a result of Joy and Timnit’s research there is now much more recognition of the problem, and what this might mean for the ways in which face recognition technology is used. There is some good news, though. The three companies made changes to improve their systems and several US cities have already banned the use of this technology in criminal investigations, with more likely to follow. People worldwide are more aware of the limitations of face recognition programs and the harms to which they may be (perhaps unintentionally) put, with calls for better regulation.

Different Shades
The Fitzpatrick skin tone scale is used by skin specialists to classify how someone’s skin responds to ultraviolet light. There are six points on the scale with 1 being the lightest skin and 6 being the darkest. People whose skin tone has a lower Fitzpatrick score are more likely to burn in the sun and are at greater risk of skin cancer. People with higher scores have darker skin which is less likely to burn and have a lower risk of skin cancer. A variation of the Fitzpatrick scale, with five points, is used to create the skin tone emojis that you’ll find on most messaging apps in addition to the ‘default’ yellow.

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EPSRC supports this blog through research grant EP/W033615/1. 

Mary Clem: getting it right

by Paul Curzon, Queen Mary University of London

Mary Clem was a pioneer of dependable computing long before the first computers existed. She was a computer herself, but became more like a programmer.

A tick on a target of red concentric zeros
Image by Paul Curzon

Back before there were computers there were human computers: people who did the calculations that machines now do. Victorian inventor, Charles Babbage, worked as one. It was the inspiration for him to try to build a steam-powered computer. Often, however, it was women who worked as human computers especially in the first half of the 20th century. One was Mary Clem in the 1930s. She worked for Iowa State University’s statistical lab. Despite having no mathematical training and finding maths difficult at school, she found the work fascinating and rose to become the Chief Statistical Clerk. Along the way she devised a simple way to make sure her team didn’t make mistakes.

The start of stats

Big Data, the idea of processing lots of data to turn that data into useful information, is all the rage now, but its origins lie at the start of the 20th century, driven by human computers using early calculating machines. The 1920s marked the birth of statistics as a practical mathematical science. A key idea was that of calculating whether there were correlations between different data sets such as rainfall and crop growth, or holding agricultural fairs and improved farm output. Correlation is the the first step to working out what causes what. it allows scientists to make progress in working out how the world works, and that can then be turned into improved profits by business, or into positive change by governments. It became big business between the wars, with lots of work for statistical labs.

Calculations and cards

Originally, in and before the 19th century, human computers did all the calculations by hand. Then simple calculating machines were invented, so could be used by the human computers to do the basic calculations needed. In 1890 Herman Hollerith invented his Tabulator machine (his company later became computing powerhouse, IBM). The Tabulator machine was originally just a counting machine created for the US census, though later versions could do arithmetic too. The human computers started to use them in their work. The tabulator worked using punch cards, cards that held data in patterns of holes punched in to them. A card representing a person in the census might have a hole punched in one place if they were male, and in a different place if they were female. Then you could count the total number of any property of a person by counting the appropriate holes.

Mary was being more than a computer,
and becoming more like a programmer

Mary’s job ultimately didn’t just involve doing calculations but also involved preparing punch cards for input into the machines (so representing data as different holes on a card). She also had to develop the formulae needed for doing calculations about different tasks. Essentially she was creating simple algorithms for the human computers using the machines to follow, including preparing their input. Her work was therefore moving closer to that of a computer operator and then programmer’s job.

Zero check

She was also responsible for checking calculations to make sure mistakes were not being made in the calculations. If the calculations were wrong the results were worse than useless. Human computers could easily make mistakes in calculations, but even with machines doing calculations it was also possible for the formulae to be wrong or mistakes to be made preparing the punch cards. Today we call this kind of checking of the correctness of programs verification and validation. Since accuracy mattered, this part of he job also mattered. Even today professional programming teams spend far more time checking their code and testing it than writing it.

Mary took the role of checking for mistakes very seriously, and like any modern computational thinker, started to work out better ways of doing it that was more likely to catch mistakes. She was a pioneer in the area of dependable computing. What she came up with was what she called the Zero Check. She realised that the best way to check for mistakes was to do more calculations. For the calculations she was responsible for, she noticed that it was possible to devise an extra calculation, whereby if the other answers (the ones actually needed) have been correctly calculated then the answer to this new calculation is 0. This meant, instead of checking lots of individual calculations with different answers (which is slow and in itself error prone), she could just do this extra calculation. Then, if the answer was not zero she had found a mistake.

A trivial version of this general idea when you are doing a single calculation is to just do it a second time, but in a different way. Rather than checking manually if answers are the same, though, if you have a computer it can subtract the two answers. If there are no mistakes, the answer to this extra check calculation should be 0. All you have to do is to look for zero answers to the extra subtractions. If you are checking lots of answers then, spotting zeros amongst non-zeros is easier for a human than looking for two numbers being the same.

Defensive Programming

This idea of doing extra calculations to help detect errors is a part of defensive programming. Programmers add in extra checking code or “assertions” to their programs to check that values calculated at different points in the program meet expected properties automatically. If they don’t then the program itself can do something about it (issue a warning, or apply a recovery procedure, for example).

A similar idea is also used now to catch errors whenever data is sent over networks. An extra calculation is done on the 1s and 0s being sent and the answer is added on to the end of the message. When the data is received a similar calculation is performed with the answer indicating if the data has been corrupted in transmission. 

A pioneering human computer

Mary Clem was a pioneer as a human computer, realising there could be more to the job than just doing computations. She realised that what mattered was that those computations were correct. Charles Babbages answer to the problem was to try to build a computing machine. Mary’s was to think about how to validate the computation done (whether by a human or a machine).

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EPSRC supports this blog through research grant EP/W033615/1. 

Black in Data

by Paul Curzon, Queen Mary University of London

Careers do not have to be decided on from day one. You can end up in a good place in a roundabout way. That is what happened to Sadiqah Musa, and now she is helping make the paths easier for others to follow.

Lightbulb in a black circle surrounded by circles of colour representing data

Image based on ones by Gerd Altmann from Pixabay

Sadiqah went to university at QMUL expecting to become an environmental scientist. Her first job was as a geophysicist analysing seismic data. It was a job she thought she loved and would do forever. Unfortunately, she wasn’t happy, not least about the lack of job security. It was all about data though which was a part she did still enjoy, and the computer science job of Data Analyst was now a sought-after role. She retrained and started on a whole new exciting career. She currently works at the Guardian Newspapers where she met Devina Nembhard … who was the first Black woman she had ever worked with throughout her career.

Together they decided that was just wrong, but also set out to change it. They created “Black in Data” to support people of colour in the industry, mentoring them, training them in the computer science skills they might be short of: like programming and databases; helping them thrive. More than that they also confront industry to try and take down the barriers that block diversity in the first place.

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EPSRC supports this blog through research grant EP/W033615/1. 

Florence Nightingale: rebel with a cause

Kerosene lamp
Image by Agnieszka from Pixabay modified by CS4FN

Florence Nightingale, the most famous female Victorian after Queen Victoria, is known for her commitment to nursing, especially in the Crimean War. She rebelled against convention to become a nurse at a time when nursing was seen as a lowly job, not suitable for ‘ladies’. She broke convention in another less well-known, but much more significant way too. She was a mathematician – the first woman to be elected a member of the Royal Statistical Society. She also pioneered the use of pictures to present the statistical data that she collected about causes of war deaths and issues of sanitation and health. What she did was an early version of the current Big Data revolution in computer science.

Soldiers were dying in vast numbers in the field hospital she worked in, not directly from their original wounds but from the poor conditions. But how do you persuade people of something that (at least then) is so unintuitive? Even she originally got the cause of the deaths wrong, thinking they were due to poor nutrition, rather than the hospital conditions as her statistics later showed. Politicians, the people with power to take action, were incapable of understanding statistical reports full of numbers then (and probably now). She needed a way to present the information so that the facts would jump out to anyone. Only then could she turn her numbers into life-saving action. Her solution was to use pictures, often presenting her statistics as books of pie charts and circular histograms.

Florence Nightingale Rose Chart, Public domain, via Wikimedia Commons

Whilst she didn’t invent them, Florence Nightingale certainly was responsible for demonstrating how effective they could be in promoting change, and so subsequently popularising their use. She undoubtedly saved more lives with her statistics than from her solitary rounds at night by lamplight.

She had collected data on the reason each person died but to present the data in ways that were convincing she also had to act as a human computer doing computation on the basic data. For each month based on the raw data, she computed annual rate of mortality per 1,000. Then to present it in a circular histogram, where the area represents deaths she calculated the appropriate radius for each segment, allowing the charts to then be drawn.

Florence Nightingale by Augustus Egg. Public domain, via Wikimedia Commons

Big Data is now a big thing. It is the idea that if you collect lots of data about something (which computers now make easy) then you (and computers themselves) can look for patterns and so gain knowledge and, for people, ultimately wisdom from it. Florence Nightingale certainly did that. Data visualisation is now an important area of computer science. As computers allow us to collect and store ever more data, it becomes harder and harder for people to make any sense of it all – to pick out the important nuggets of information that matter. Raw numbers are little use if you can’t actually turn them into knowledge, or better still wisdom. Machine Learning programs can number crunch the data and make decisions from it, but its hard to know where the decisions came from. That often matters if we are to be persuaded. For humans the right kind of picture for the right kind of data can do just that as Florence Nightingale showed.

‘The Lady of the Lamp’: more than a nurse, but also a remarkable statistician and pioneer of a field of computer science…a Lady who made a difference by rebelling with a cause.

– Paul Curzon, Queen Mary University of London

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EPSRC supports this blog through research grant EP/W033615/1.