Devices that work for everyone #BlackHistoryMonth ^JB

A pulse oximeter on the finger of a Black person's hand

by Jo Brodie, Queen Mary University of London

In 2009 Desi Cryer, who is Black, shared a light-hearted video with a serious message. He’d bought a new computer with a face tracking camera… which didn’t track his face, at all. It did track his White colleague Wanda’s face though. In the video (below) he asked her to go in front of the camera and move from side to side and the camera obediently tracked her face – wherever she moved the camera followed. When Desi moved back in front of the camera it stopped again. He wondered if the computer might be racist…

The computer recognises Desi’s colleague Wanda, but not him

Another video (below), this time from 2017, showed a dark-skinned man failing to get a soap to dispenser to give him some soap. Nothing happened when he put his hand underneath the sensor but as soon as his lighter-skinned friend put his hand under it – out popped some soap! The only way the first man could get any soap dispensed was to put a white tissue on his hand first. He wondered if the soap dispenser might be racist…

The soap dispenser only dispenses soap if it ‘see’s a white hand

What’s going on?

Probably no-one set out to maliciously design a racist device but designers might need to check that their products work with a range of different people before putting them on the market. This can save the company embarrassment as well as creating something that more people want to buy. 

Sensors working overtime

Both devices use a sensor that is activated (or in these cases isn’t) by a signal. Soap dispensers shine a beam of light which bounces off a hand placed below it and some of that light is reflected back. Paler skin reflects more light (and so triggers the sensor) than darker skin. Next to the light is a sensor which responds to the reflected light – but if the device was only tested on White people then the sensor wasn’t adjusted for the full range of skin tones and so won’t respond appropriately. Similarly cameras have historically been designed for White skin tones meaning darker tones are not picked up as well.

In the days when film was developed the technicians would use what was called a ‘Shirley’ card (a photograph of a White woman with brown hair) to colour-correct the photographs. The colour balancing meant darker-skinned tones didn’t come out as well, however the problem was only really addressed because chocolate manufacturers and furniture companies complained that the different chocolates and dark brown wood products weren’t showing up correctly!

The Racial Bias Built Into Photography (25 April 2019) The New York Times

Things can be improved!

It’s a good idea, when designing something that will be used by lots of different people, to make sure that it will work correctly with everyone. Having a diverse design team and, importantly, making sure that everyone feels empowered to contribute is a good way to start. Another is to test the design with different target audiences early in the design process so that changes can be made before it’s too late. How a company responds to feedback when they’ve made an oversight is also important. In the case of the computer company they acknowledged the problem and went to work to improve the camera’s sensitivity. 

A problem with pulse oximeters

A pulse oximeter on the finger of a Black person's hand
Pulse oximeter image by Mufid Majnun from Pixabay
The oximeter is shown on the index finger of a Black person’s right hand.

During the coronavirus pandemic many people bought a ‘pulse oximeter’, a device which clips painlessly onto a finger and measures how much oxygen is circulating in your blood (and your pulse). If the oxygen reading became too low people were advised to go to hospital. Oximeters shine red and infrared light from the top clip through the finger and the light is absorbed diferently depending on how much oxygen is present in the blood. A sensor on the lower clip measures how much light has got through but the reading can be affected by skin colour (and coloured nail polish). People were concerned that pulse oximeters would overestimate the oxygen reading for someone with darker skin (that is, tell them they had more oxygen than they actually had) and that the devices might not detect a drop in oxygen quickly enough to warn them.

In response the UK Government announced in August 2022 that it would investigate this bias in a range of medical devices to ensure that future devices work effectively for everyone.

Further reading

See also Is your healthcare algorithm racist? (from issue 27 of the CS4FN magazine).


See more in ‘Celebrating Diversity in Computing

We have free posters to download and some information about the different people who’ve helped make modern computing what it is today.

Screenshot showing the vibrant blue posters on the left and the muted sepia-toned posters on the right

Or click here: Celebrating diversity in computing


This blog is funded through EPSRC grant EP/W033615/1.

The Mummy in an AI world: Jane Webb’s future

by Paul Curzon, Queen Mary University of London

The sarcophagus of a mummy
Image by albertr from Pixabay

Inspired by Mary Shelley’s Frankenstein, 17-year old Victorian orphan, Jane Webb secured her future by writing the first ever Mummy story. The 22nd century world in which her novel was set is perhaps the most amazing thing about the three volume book though.

On the death of her father, Jane realised she needed to find a way to support herself and did so by publishing her novel “The Mummy!” in 1827. In contrast to their modern version as stars of horror films, Webb’s Mummy, a reanimation of Cheops, was actually there to help those doing good and punish those that were evil. Napoleon had, through the start of the century, invaded Egypt, taking with him scholars intent on understanding the Ancient Egyptian society. Europe was fascinated with Ancient Egypt and awash with Egyptian artefacts and stories around them. In London, the Egyptian Hall had been built in Piccadilly in 1812 to display Egyptian artefacts and in 1821 it displayed a replica of the tomb of Seti I. The Rosetta Stone that led to the decipherment of hieroglyphics was cracked in 1822. The time was therefore ripe for someone to come up with the idea of a Mummy story.

The novel was not, however, set in Victorian times but in a 22nd century future that she imagined, and that future was perhaps more amazing than the idea of a mummy coming to life. Her version of the future was full of technological inventions supporting humanity, as well as social predictions, many of which have come to fruition such as space travel and the idea that women might wear trousers as the height of fashion (making her a feminist hero). The machines she described in the book led to her meeting her future husband, John Loudon. As a writer about farming and gardening he was so impressed by the idea of a mechanical milking machine included in the book, that he asked to meet her. They married soon after (and she became Jane Loudon).

The skilled artificial intelligences she wrote into her future society are perhaps the most amazing of her ideas in that she was the first person to really envision in fiction a world where AIs and robots were embedded in society just doing good as standard. To put this into context of other predictions, Ada Lovelace wrote her notes suggesting machines of the future would be able to compose music 20 years later.

Jane Webb’s future was also full of cunning computational contraptions: there were steam-powered robot surgeons, foreseeing the modern robots that are able to do operations (and with their steady hands are better at, for example, eye surgery than a human). She also described Artificial Intelligences replacing lawyers. Her machines were fed their legal brief, giving them instructions about the case, through tubes. Whilst robots may not yet have fully replaced barristers and judges, artificial intelligence programs are already used, for example, to decide the length of sentences of those convicted in some places, and many see it now only being a matter of time before lawyers are spending their time working with Artificial Intelligence programs as standard. Jane’s world also includes a version of the Internet, at a time before electric telegraph existed and when telegraph messages were sent by semaphore between networks of towers.

The book ultimately secured her future as required, and whilst we do not yet have any real reanimated mummy’s wandering around doing good deeds, Jane Webb did envision lots of useful inventions, many that are now a reality, and certainly had pretty good ideas about how future computer technology would pan out in society…despite computers, never mind artificial intelligences, still being well over a century away.


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EPSRC supported this article through research grants (EP/K040251/2 and EP/K040251/2 held by Professor Ursula Martin as well as grant EP/W033615/1). 

Full metal jacket: the fashion of Iron Man

by Peter W McOwan and Paul Curzon, Queen Mary University of London

Spoiler Alert

Industrialist Tony Stark always dresses for the occasion, even when that particular occasion happens to be a fight with the powers of evil. His clothes are driven by computer science: the ultimate in wearable computing.

In the Iron Man comic and movie franchise Anthony Edward Stark, Tony to his friends, becomes his crime fighting alter ego by donning his high tech suit. The character was created by Marvel comic legend Stan Lee and first hit the pages in 1963. The back story tells how industrial armaments engineer and international playboy Stark is kidnapped and forced to work to develop new forms of weapons, but instead manages to escape by building a flying armoured suit.

Though the escape is successful Stark suffers a major heart injury during the kidnap ordeal, becoming dependant on technology to keep him alive. The experience forces him to reconsider his life, and the crime avenging Iron Man is born. Lee’s ‘businessman superhero’ has proved extremely popular and in recent years the Iron Man movies, starring Robert Downey Jr, have been box office hits. But as Tony himself would be the first to admit, there is more than a little computer science supporting Iron Man’s superhero standing.

Suits you

The Iron Man suit is an example of a powered exoskeleton. The technology surrounding the wearer amplifies the movement of the body, a little like a wearable robot. This area of research is often called ‘human performance augmentation’ and there are a number of organisations interested in it, including universities and, unsurprisingly, defence companies like Stark Industries. Their researchers are building real exoskeletons which have powers uncannily like those of the Iron Man suit.

To make the exoskeleton work the technology needs to be able to accurately read the exact movements of the wearer, then have the robot components duplicate them almost instantly. Creating this fluid mechanical shadow means the exoskeleton needs to contain massive computing power, able to read the forces being applied and convert them into signals to control the robot servo motors without any delay. Slow computing would cause mechanical drag for the wearer, who would feel like they were wading through treacle. Not a good idea when you’re trying to save the world.

Pump it up

Humans move by using their muscles in what are called antagonistic pairs. There are always two muscles on either side of the joint that pull the limb in different directions. For example, in your upper arm there are the muscles called the biceps and the triceps. Contracting the biceps muscle bends your elbow up, and contracting your triceps straightens your elbow back. It’s a clever way to control biological movement using just a single type of shortening muscle tissue rather than needing one kind that shortens and another that lengthens.

In an exoskeleton, the robot actuators (the things that do the moving) take the place of the muscles, and we can build these to move however we want, but as the robot’s movements need to shadow the person’s movements inside, the computer needs to understand how humans move. As the human bends their elbow to lift up an object, sensors in the exoskeleton measure the forces applied, and the onboard computer calculates how to move the exoskeleton to minimise the resulting strain on the person’s hand. In strength amplifying exoskeletons the actuators are high pressure hydraulic pistons, meaning that the human operators can lift considerable weight. The hydraulics support the load, the humans movements provide the control.

I knew you were going to do that

It is important that the human user doesn’t need to expend any effort in moving the exoskeleton; people get tired very easily if they have to counteract even a small but continual force. To allow this to happen the computer system must ensure that all the sensors read zero force whenever possible. That way the robot does the work and the human is just moving inside the frame. The sensors can take thousands of readings per second from all over the exoskeleton: arms, legs, back and so on.

This information is used to predict what the user is trying to do. For example, when you are lifting a weight the computer begins by calculating where all the various exoskeleton ‘muscles’ need to be to mirror your movements. Then the robot arm is instructed to grab the weight before the user exerts any significant force, so you get no strain but a lot of gain.

Flight suit?

Exoskeleton systems exist already. Soldiers can march further with heavy packs by having an exoskeleton provide some extra mechanical support that mimics their movements. There are also medical applications that help paralysed patients walk again. Sadly, current exoskeletons still don’t have the ability to let you run faster or do other complex activities like fly.

Flying is another area where the real trick is in the computer programming. Iron Man’s suit is covered in smart ‘control surfaces’ that move under computer control to allow him to manoeuvre at speed. Tony Stark controls his suit through a heads-up display and voice control in his helmet, technology that at least we do have today. Could we have fully functional Iron Man suits in the future? It’s probably just a matter of time, technology and computer science (and visionary multi-millionaire industrialists too).


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This blog is funded through EPSRC grant EP/W033615/1.

Swallow a slug-bot to catch a …

by Paul Curzon, Queen Mary University of London

Imagine swallowing a slug (hint not only a yucky thought but also not a good idea as it could kill you)…now imagine swallowing a slug-bot … also yucky but in the future it might save your life.

When people accidentally swallow solid objects that won’t pass through their digestive system, or are toxic, it can be a big problem. Once an object passes beyond your stomach it becomes hard to get at.

That is where the slug shaped robot comes in. The idea of scientists at the Chinese University of Hong Kong is that a robot like a slug could crawl down your throat to retrieve whatever you had swallowed.

If you think of robots as solid, hard things then that would be the last thing you might want to swallow (aside from an actual slug), and certainly not to catch the previous solid thing you swallowed. You may be right. However, that is where the soft slug-shaped robot comes in.

It is easy to make or buy slime-like “silly” putty. Add iron filings to slime putty and you can make it stretch and sway and even move around with magnets yourself. You can buy such magnetic slime at science museums…it is fun to play with though you definitely shouldn’t swallow it.

The scientists have taken that general idea though and using special materials created a similar highly controllable bot that can be moved around using a magnet-based control system. It is made of a special material that is magnetic and slime-like but coated in silicon dioxide to stop it being poisonous.

They have shown that they can control it to squeeze through narrow gaps and encircle small objects, carrying them away with it…essentially what would be needed to recover objects that have been swallowed.

It needs a lot more work to make sure it is safe to really be swallowed. Also to be a real autonomous robot it would need to have sensors included somehow, and be connected to some sort of intelligent system to automatically control its behaviour. However, with more research that all may become possible.

So in the future if you don’t fancy swallowing a slug-bot, you’d better be far more careful about what else you swallow first. Of course, if it turns out slug like robots can break down, so get stuck themselves, you may then be in a position of needing to swallow a bird-bot to catch the slug-bot. How absurd …


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The cs4fn blog is funded by EPSRC, through grant EP/W033615/1.

Back (page) to health

Improvements in technology and decision making are transforming the way we look after our health. Here are some more interesting ideas to keep people alive and well.

Woman wearing VR headset looking at the sky.
Image by Pexels from Pixabay

The future is in your poo

You’ve heard of telling a person’s future from reading their tea leaves. Scientists believe an effective way of seeing a town’s future may be in the poo. By looking for infection in the waste at sewerage works it’s possible to get fast and accurate local knowledge of where infection rates are high and where low to feed into decision making tools.

Health advice: Stay in the toilet, Stay safe. Help the NHS.

Virtually breaking quarantine

The game, World of Warcraft, a multi-user dungeon game, helped virologists understand how people might behave in pandemics. The game’s developers released a plague that could be passed between avatars. The game’s contaminated area was quarantined. Rather than dying out, the virus escaped – because people broke into the quarantined areas to gawk, then left taking the virus with them.

Health advice: Your avatar should obey quarantine rules too!

The missing bullet holes

To stay healthy in a war, avoid being hit by a bullet. In World War II, many aircraft returned badly damaged. Abraham Wald studied them to decide where better armour was needed. There were more bullet holes in the fuselage than the engines. Where would you add the armour? Abraham added it where there were no bullet holes. He reasoned that the lack of holes in places like engines on returning planes meant that being hit there brought the plane down. Being hit elsewhere did not kill the pilots as those planes made it home!

Health advice: Dodge bullets by making good decisions …

Cybersick of virtual reality

The AI can detect puke-inducing movement and automatically correct the image.

A problem with virtual reality is that wearing a headset can be so immersive that it makes some people actually sick. This happens if you move about when watching a 3D video that was shot from a single place. Artificial intelligence software has come to the rescue, detecting puke-inducing movement and automatically correcting the image.

Health advice: If no bucket, always keep an AI handy.

Shining light on cancers

Cancer treatments like chemotherapy and radiotherapy make patients ill. Some drugs make cancer sensitive to light, allowing tumours to be killed by painlessly shining light on them instead. Sadly, that’s not easy when cancers are inside the body. A new Japanese solution is an LED chip, based on the technology used by contactless payment cards to provide power from a distance. Surgeons place it under the skin and leave it there. They glue it in place using a sticky protein from the feet of mussels. It shines low-intensity green light on the cancer, shrinking it.

Health advice: Stick a chip to your tumour

Smart sometimes means no gadgets

Being smart about health doesn’t have to be high-tech or even involve drugs. Exercise, for example, can be as effective helping with depression as taking medicine. Being out in nature can help too, so sometimes it’s worth leaving the gadgets behind and just going for a walk to enjoy the beauty of nature.

Health advice: Walk weekly in the woods

Paul Curzon, Queen Mary University of London, Spring 2021

Download Issue 27 of the cs4fn magazine on Smart Health here.

This post and issue 27 of the cs4fn magazine have been funded by EPSRC as part of the PAMBAYESIAN project.

Gadgets based on works of fiction

Why might a computer scientist need to write fiction? To make sure she creates an app that people actually need.

Portrait images of lots of people used as personas.

Writing fiction doesn’t sound like the sort of skill a computer scientist might need. However, it’s part of my job at the moment. Working with expert rheumatologists Amy MacBrayne and Fran Humby, I am helping a design team understand what life with rheumatoid arthritis is like, so they can design software that is actually needed and so will be used and useful.

A big problem with developing software is that programmers tend to design things for themselves. However, programmers are not like the users of their software. They have different backgrounds and needs and they have been trained to think differently. Worse, they know the system they are developing inside out, unlike its users. An important first step in a project is to do background research to understand your users. If designing an app for people with rheumatoid arthritis, you need to know a lot about the lives of such people. To design a successful product, you particularly need to understand their unfulfilled goals. What do they want to be able to do that is currently hard or impossible?

What do you do with the research? Alan Cooper’s idea of ‘Personas’ are a powerful next step – and this is where writing fiction comes in. Based on research, you write descriptions of lots of fictional characters (personas), each representing groups of people with similar goals. They have names, photos and realistic lives. You also write scenarios about their lives that help understand their goals. Next, you merge and narrow these personas down, dropping some, creating new ones, altering others. Your aim is to eventually end up with just one, called a primary persona. The idea is that if you design for the primary persona, you will create something that meets the goals of the groups represented by the other personas it replaced.

The primary persona (let’s call her Samira) is then used throughout the design process as the person being designed for. If wondering whether some new feature or way of doing things is a good idea, the designers would ask themselves, “Would Samira actually want this? Would she be able to use it?” If they can think of her as a real person, it is much easier to make decisions than if thinking of some non-existent abstract “user” who becomes whatever each team member wants them to be. It helps stop ‘feature bloat’ where designers add in every great idea for a new feature they have but end up with a product so complex no one can, or wants to, use it.

As part of the Queen Mary PAMBAYESIAN project we have been talking to rheumatoid arthritis patients and their doctors to understand their needs and goals. I’ve then created a cast of detailed personas to represent the results. These can act as an initial set of personas to help future designers designing apps to support those with the disease.

If you thought creative writing wasn’t important to a computer scientist, think again. A good persona needs to be as powerfully written and as believable as a character in a good novel. So, you should practice writing fiction as well as writing programs.

Read some of our personas about living with rheumatoid arthritis here.

– Paul Curzon, Queen Mary University of London, Spring 2021

See the related Teaching London Computing Activity

Find out more about goal-directed design and personas from its creator in Alan Cooper’s wonderful book “The inmates are running the Asylum” (the inmates are computer scientists!)

Download Issue 27 of the cs4fn magazine on Smart Health here.

This post and issue 27 of the cs4fn magazine have been funded by EPSRC as part of the PAMBAYESIAN project.

How do you solve a problem like arthritis?

Some diseases can’t be cured. Doctors and nurses just try to control the disease to stop them ruining people’s lives. Perhaps smartphone apps can pull off the trick of giving patients better care while giving clinicians more time to spend with the patients who most need them? A Venn diagram is at the centre of the Queen Mary team’s prototype.

A Venn diagram of low participation, low empowerment and low independence with images linked to each - people eating in a resterount, a person holding out arms at the top of a peak and two people walking.

What is rheumatoid arthritis?

Normally your immune system does a good job of fighting infection and keeping you healthy. But, if you have an autoimmune disease, it can also attack your healthy cells, causing inflammation and damage. Rheumatoid arthritis is like this: a painful condition that mostly affects hands, knees and feet as the person’s immune system attacks their joints, making them swell painfully. It affects around 400,000 people in the UK and is more common in women than men.

People with the disease alternate between periods when it is under control and they have few symptoms, and with days or weeks of painful ‘flares’ where it is very, very bad. During these flares it especially affects a person’s ability to live a normal life. It can be hard to move around comfortably, do exercise – plus it interferes with their ability to work. It can also leave them totally reliant on family and friends just to do everyday things like dress or eat, never mind go out. This can lead to depression and puts a strain on friendships.

Treating the disease

Treatment, which can include tablets, injections, physiotherapy and sometimes surgery, slows the disease, keeping it under control for long periods. Sufferers are also given advice on lifestyle changes. This all reduces the risk of joint damage and helps people live their life more fully.

At appointments, doctors collect information to help them see how the disease is progressing. A Disease Activity Score (DAS) calculator lets them combine measurements for pain, how tender or swollen their patient’s joints are and how many joints are affected. Regular blood tests keep track of the amount of inflammation and how the body is reacting to drugs. This helps them decide if they need to adjust the medication.

If it is caught early, modern medicine reduces the worst effects of the disease, helped by keeping a close eye on the Disease Activity Score as treatments may need to be repeatedly adjusted to control flares. This requires regular hospital visits which uses up scarce healthcare resources and is very time-consuming for patients. It is hampered because hospital appointments may only happen twice a year due to the number of patients. Everyone wants to give more personalised care, but hospitals just can’t afford to provide it.

Supporting doctors

So, what do you do when there just aren’t enough doctors to see everyone as regularly as needed to maintain their patients’ wellbeing? One solution is to use remote monitoring with an app on a patient’s smartphone, so involving patients more directly in their own care. They can use such apps to regularly record their own disease activity measurements, sharing the information with their doctor to save visiting the hospital.

A smart app

This is an improvement, but the measurements still require expert monitoring and can take more of the doctor’s time. However, if smartphones can actually be made to be, well, smart, then they could help give advice between hospital visits and alert the hospital team, when needed, so they can step in. This might involve, for example, loading the app with background knowledge about rheumatoid arthritis, expert knowledge from lots of doctors, and creating an artificial intelligence to use this information effectively for each patient.

Hospital specialists and computer scientists at Queen Mary are developing such a prototype based on Bayesian networks as the artificial intelligence core. Bayesian networks are based on reasoning about the causes of things and how likely different things are to be the cause of something being observed. Building the prototype involves finding out if patients and clinicians find such tools useful and acceptable (some people might find clinic visits reassuring, while some may be keener to avoid taking the time off work, for example).

Smart and patient centred

This still focusses on a clinician’s view of treatment using drugs though. With a smartphone app we can perhaps do better and take the person’s life into account – but how? The first step is to understand patient goals. Patients would need to be willing to share lots of information about themselves so that the software can learn as much as possible about them. Eventually, this might be done using sensors that automatically detect information: how much pain they are in, how stiff their joints are, how much they move around, how long it takes them to get out of a chair, how much sleep they get, how often they meet others, if and when they take their medicine, and so on. Rather than just focussing on medical treatment it can then focus advice ‘holistically’ on the whole person.

The Queen Mary team’s approach is centred around three different things: helping people with physical independence so they can move around and look after themselves; empowering them to manage their condition and general well-being themselves; and participation in the sense of helping them socialise, keep friendships and maintain family bonds.

The Bayesian network processes the information about patients and computes their predicted levels of independence, empowerment and participation, working out how good or bad things are for them at the moment. This places them in one of seven positions in a Venn diagram of the three dimensions over which areas need most attention. It then gives appropriate advice, aiming to keep all three dimensions in balance, monitoring what happens, but also alerting the hospital when necessary.

So, for example, if the Bayesian network judges independence low, participation high and empowerment low, the patient is in the Venn diagram intersection of low empowerment and low independence. Advice in the following weeks, based on this area of the Venn diagram, would focus on things like coping with pain and stiffness, getting better sleep, as well as how to manage the disease in general.

By personalising advice and focusing on the whole person, it is hoped patients will get more appropriate care as soon as they need it, but doctors’ time will also be freed up to focus on the patients who most need their help.

– Jo Brodie, Hamit Soyel and Paul Curzon, Queen Mary University of London, Spring 2021

Download Issue 27 of the cs4fn magazine on Smart Health here.

This post and issue 27 of the cs4fn magazine have been funded by EPSRC as part of the PAMBAYESIAN project.

So, so tired…

Fatigue is a problem that people with a variety of long-term diseases can also suffer from.

A man, hands over face, very, very tired.
Image by Małgorzata Tomczak from Pixabay

This isn’t just normal tiredness, but something much, much worse: so bad that it is a struggle to do anything at all, destroying any chance of a normal life. Doctors can often do little to help beyond managing the underlying disease, then hope the fatigue sorts itself out. Sometimes fatigue can stay with the person long, long after. Maha Albarrak, for her PhD, is exploring how computer technology might help people cope. Her first step is to interview those suffering to find out what kind of help they really need. Then she will work closely with volunteers to come up with solutions that solve the problems that matter.

– Paul Curzon, Queen Mary University of London, Spring 2021

Download Issue 27 of the cs4fn magazine on Smart Health here.

This post and issue 27 of the cs4fn magazine have been funded by EPSRC as part of the PAMBAYESIAN project.

Is your healthcare algorithm racist?

Algorithms are taking over decision making, and this is especially so in healthcare. But could the algorithms be making biased decisions? Could their decisions be racist? Yes, and such algorithms are already being used.

A medical operation showing an anaesthetist and the head of the patient
Image by David Mark from Pixabay

There is now big money to be made from healthcare software. One of the biggest areas is in intelligent algorithms that help healthcare workers make decisions. Some even completely take over the decision making. In the US, software is used widely, for example, to predict who will most benefit from interventions. The more you help a patient the more it costs. Some people may just get better without extra help, but for others it means the difference between a disability that might have been avoided or not, or even life and death. How do you tell? It matters as money is limited, so someone has to choose. You need to be able to predict outcomes with or without potential treatments. That is the kind of thing that machine learning technology is generally good at. By looking at the history of lots and lots of past patients, their treatments and what happened, these artificial intelligence programs can spot the patterns in the data and then make predictions about new patients.

This is what current commercial software does. Ziad Obermeyer, from UC Berkeley, decided to investigate how well the systems made those decisions. Working with a team combining academics and clinicians, they looked specifically at the differences between black and white patients in one widely used system. It made decisions about whether to put patients on more expensive treatment programmes. What they found was that the system had a big racial bias in the decisions it made. For patients that were equally ill, it was much more likely to recommend white patients for treatment programmes.

One of the problems with machine learning approaches is it is hard to see why they make the decisions they do. They just look for patterns in data, and who knows what patterns they find to base their decisions on? The team had access to the data of a vast number of patients the algorithms had made recommendations about, the decisions made about them and the outcomes. This meant they could evaluate whether patients were treated fairly.

The data given to the algorithm specifically excluded race, supposedly to stop it making decisions on colour of skin. However, despite not having that information, that was ultimately what it was doing. How?

The team found that its decision-making was based on predicting healthcare costs rather than how ill people actually were. The greater the cost saving of putting a person on a treatment programme, the more likely it was to recommend them. At first sight, this seems reasonable, given the aim is to make best use of a limited budget. The system was totally fair in allocating treatment based on cost. However, when the team looked at how ill people were, black people had to be much sicker before they would be recommended for help. There are lots of reasons more money might be spent on white people, so skewing the system. For example, they may be more likely to seek treatment earlier or more often. Being poor means it can be harder to seek healthcare due to difficulties getting to hospital, difficulties taking time off work, etc. If more black people in the data used to train the system are poor then this will lead to them seeking help less, so less is being spent on them. The system had spotted patterns like this and that was how it was making decisions. Even though it wasn’t told who was black and white, it had learnt to be biased.

There is an easy way to fix the system. Instead of including data about costs and having it use that as the basis of decision making, you can use direct measures of how ill a person is: for example, using the number of different conditions the patient is suffering from and the rule of thumb that the more complications you have, the more you will benefit from treatment. The researchers showed that if the system was trained this way instead, the racial bias disappeared. Access to healthcare became much fairer.

If we are going to allow machines to take healthcare decisions for us based on their predictions, we have to make sure we know how they make those predictions, and make sure they are fair. You should not lose the chance of the help you need just because of your ethnicity, or because you are poor. We must take care not to build racist algorithms. Just because computers aren’t human doesn’t mean they can’t be humane.

– Paul Curzon, Queen Mary University of London, Spring 2021

Download Issue 27 of the cs4fn magazine on Smart Health here.

This post and issue 27 of the cs4fn magazine have been funded by EPSRC as part of the PAMBAYESIAN project.

Diagnose? Delay delivery? Decisions, decisions. Decisions about diabetes in pregnancy

In the film Minority Report, a team of psychics – who can see into the future – predict who might cause harm, allowing the police to intervene before the harm happens. It is science fiction. But smart technology is able to see into the future. It may be able to warn months in advance when a mother’s body might be about to harm her unborn baby and so allow the harm to be prevented before it even happens.

Baby holding feet with feet in foreground.
Image by Daniel Nebreda from Pixabay

Gestational diabetes (or GDM) is a type of diabetes that appears only during pregnancy. Once the baby is born it usually disappears. Although it doesn’t tend to produce many symptoms it can increase the risk of complications in pregnancy so pregnant women are tested for it to avoid problems. Women who’ve had GDM are also at greater risk of developing Type 2 diabetes later on, joining an estimated 4 million people who have the condition in the UK.

Diabetes happens either when someone’s pancreas is unable to produce enough of a chemical called insulin, or because the body stops responding to the insulin that is produced. We need insulin to help us make use of glucose: a kind of sugar in our food that gives us energy. In Type 1 diabetes (commonly diagnosed in young people) the pancreas pretty much stops producing any insulin. In Type 2 diabetes (more commonly diagnosed in older people) the problem isn’t so much the pancreas (in fact in many cases it produces even more insulin), it’s that the person has become resistant to insulin. The result from either ‘not enough insulin’ or ‘plenty of insulin but can’t use it properly’ is that glucose isn’t able to get into our cells to fuel them. It’s a bit like being unable to open the fuel cap on a car, so the driver can’t fill it with petrol. This means higher levels of glucose circulate in the bloodstream and, unfortunately, high glucose can cause lots of damage to blood vessels.

During a normal pregnancy, women often become a little more insulin-resistant than usual anyway. This is an effect of pregnancy hormones from the placenta. From the point of view of the developing foetus, which is sharing a blood supply with mum, this is mostly good news as the blood arriving in the placenta is full of glucose to help the baby grow. That sounds great but if the woman becomes too insulin-resistant and there’s too much glucose in her blood it can lead to accelerated growth (a very large baby) and increase the risk of complications during pregnancy and at birth. Not great for mum or baby. Doctors regularly monitor the blood glucose levels in a GDM pregnancy to keep both mother and baby in good health. Once taught, anyone can measure their own blood glucose levels using a finger-prick test and people with diabetes do this several times a day.It will save money but also be much more flexible for mothers.

In-depth screening of every pregnant woman, to see if she has, or is at risk of, GDM costs money and is time-consuming, and most pregnant women will not develop GDM anyway. PAMBAYESIAN researchers at Queen Mary have developed a prototype intelligent decision-making tool, both to help doctors decide who needs further investigation, but also to help the women decide when they need additional support from their healthcare team.

The team of computer scientists and maternity experts developed a Bayesian network with information based on expert knowledge about GDM, then trained it on real (anonymised) patient data. They are now evaluating its performance and refining it. There are different decision points throughout a GDM pregnancy. First, does the person have GDM or are they at increased risk (perhaps because of a family history)? If ‘yes’ then the next decision is how best to care for them and whether or not to begin medical treatment or just give diet and lifestyle support. Later on in the pregnancy the woman and her doctor must consider when it’s best for her to deliver her baby, then later she needs ongoing support to prevent her GDM from leading to Type 2 diabetes. Currently in early development work, it’s hoped that if given blood glucose readings, the GDM Bayesian network will ultimately be able to take account of the woman’s risk factors (like age, ethnicity and previous GDM) that increase her risk. It would use that information to predict how likely she is to develop the condition in this pregnancy, and suggest what should happen next.

Systems like this mean that one day your smartphone may be smart enough to help protect you and your unborn baby from future harm.

– Jo Brodie, Queen Mary University of London, Spring 2021

Download Issue 27 of the cs4fn magazine on Smart Health here.

This post and issue 27 of the cs4fn magazine have been funded by EPSRC as part of the PAMBAYESIAN project.