Clippy returns!

Helpful suggestions aren’t helpful if they block what you are doing. You would think Microsoft would have learned that lesson with Clippy.

For those who don’t remember Clippy, it was an early AI agent incorporated into Office products.  If you were in Word and started to type “Dear Sam”, Clippy would pop up and say “it looks like you are writing a letter” and offered potentially helpful suggestions.  The problem was that Clippy was a modal dialog, that is, while it was showing you couldn’t type.  So of you were in the middle of typing “Dear Sam, Thank you for your letter …”, everything after the point Clippy appeared would be lost.  This violates a critical rule of appropriate intelligence, while Clippy did “good things when it was right”, it did not avoid doing “bad things when it wasn’t” 🙁

Not surprisingly, Clippy was withdrawn many years ago.

However, now in Outlook (web version) shades of Clippy return.  If you make a typo or spelling mistake, it is marked with an underline like this.

This is a trivial typo a semi-colon instead of an apostrophe in “can’t”.  So I go to correct it by clicking just after the semi-colon and then type delete followed by apostrophe.  However, the text does not change!  This is because the spelling checker has ‘helpfully’ popped up a dialog box with spelling suggestions …

… but the dialog is modal!  So, what I type is simply thrown away.  In this case it is possible to select the correct spelling, but it only after it has interrupted my flow of editing.  If no suggestion is correct one has to either click somewhere else in the message, or click  the’stop’ icon on the bottom left of the box to make the box go away (with slightly different meanings), and then continue to type what you were trying to type in the first place.

Design takeaway:  Be very cautious when using modal dialog boxes, especially when they may appear unexpectedly.

Borrowed Light

This morning here was a rainbow over the sea; not the one in the photo, the one this morning was too faint to photograph, only dusty colours at the two ends before dissolving into the clouds.

At school we learnt that rainbow were due to the way light from the sun behind is bent as it enters tiny water droplets, bounces within the droplet and then comes back out1.  The angles of entry and exit depend on refraction as the refractive indices of different colours vary slightly, there are different critical angles, hence the arcs of the rainbow.

If the water drops were not there, the sun’s light would strike the land behind the rainbow’s feet or in the upper parts simply fly past into the sky and then outer space.  It was light never destined for us but borrowed from other people and other places.  If there were no rainbows the earth would shine just a little more brightly for astronauts on the moon.

Behind the rainbow is blue sky.  Again we were taught in school how the atmosphere scatters blue light, and this gives the sky its colour. Some of the light that would otherwise fall in other places, instead is diverted to us.  Without this the sky away from the blinding sun would be black and star-spattered like night at noon.

Twilight is a magical time of things half seen and just before sunrise here is the exuberant awakening of the dawn chorus.  In these in-between times, the sun is below the horizon and yet there is still glimmering light.

Our atmosphere is fragile and thin, less than 2% of the radius of the Earth, not so much like the skin of an orange as the coating of wax on its surface, or the tissue-thin layer of dried onion skin.  The sliver of light from the sun that does not hit the earth, but skims the atmosphere is also partially scattered, lightening the sky when the sun is still hidden.  Light that would be lost to the heavens, instead finding us.

We all live in borrowed light.


  1. See Meteorological Office explanation and video.[back]

Politics of Water

Water has been at the heart of Welsh politics for many years as highlighted by an article in the BBC News today1.  However, the impacts of climate change means this is a growing issue across the world.

Man Turns on Water tap

BBC News: Wales ‘missing out on fortune’ over water powers – An ex-minister says he is “aghast” the Welsh government still hasn’t taken control of water policy.

I recall hearing on the radio about the Free Wales Army attacking water pipelines in the 1960s. I was a small child at the time and thought it all sounded very exciting, but I had no idea of the politics behind this.

It was brought home to me when I first paid water rates myself.

As a child, after my dad died, my mum was incredibly good at managing the finances and saving for bills.  Each year the household rate bill would arrive (the UK housing tax for local services).  It would be huge, but as she was on widow’s benefit there was a rebate of 90% of the bill, so the remainder was manageable.  However, this did not include water and sewage.  Once a year the water rates bill would arrive.  It was as big as the standard ‘rates’ bill, but this time there was no rebate, and at that time there was no monthly payment option.  As I said, mum was good at saving towards these big bills, but it was so big, we always knew when it hit the carpet!

Roll on the years and I am paying my own water rates bill for the first time in the early 1980s.  We lived in Bedfordshire, a county of England not known for high rainfall.  My water rates bill was £60 (about £300 today’s prices), but when I talked to my mum, in Cardiff, at the base of the Brecon Beacons with multiple reservoirs, her water bill was £300 (~£1500 today), five times higher.

The reason for this was that the water companies were semi-autonomous.  Wales is full of mountains and consequently expensive to pipe water around the country, hence the high bills.  However, Wales also has lots of water, but as this was piped across the border, there was no commensurate flow of cash back.

Happily this disparity no longer seems to be the case, I assume due to different subsidies to the water companies, but certainly highlights why the control of water is a political issue.

Separating the Waters

In the early months of 2020 the news in the UK was dominated by flooding; Covid-19 was still a distant and uncertain problem compared to the images of homes, shops, and whole communities inundated, often with filthy water contaminated by effluent forced out of drains and sewers. In insurance terms, flood is one of the natural disasters commonly referred to as “Acts of God”. Water seems to be the ultimate blessing or curse of God: the “rain falls on the just and the unjust” (Matt. 5:45) a liberal outpouring for all.

At the same time in the east of Australia bushfire raged, following an unprecedented heat wave. A little further west, in the Murray Darling Basin, a report highlighted that gigalitres of water were being wasted as large volumes of water were directed through the constricted river to almond groves near the sea, by-passing farms ravaged by drought on the way. Farmers looked on helpless as water flowed past by their parched land2.

Almond fields near Mildura. There are fears the Murray-Darling water management regime may not be able to handle the boom in the water-intensive crop. Photograph: Mike Bowers/The Guardian

Guardian 25 May 2019. Tough nut to crack: the almond boom and its drain on the Murray-Darling – Demand for the thirsty crop has created a gold rush but irrigators and growers fear there might not be enough water

As the coronavirus lockdown restricted movement across the world, it highlighted the plight of 15 million US citizens who each year have their water supply cut off for non-payment of water bills. In the UK water companies can sue and eventually bailiffs seize goods, but they cannot, by law, turn off the water supply. Water is deemed essential for human life and dignity. Not so in the US.

In normal times this is bad enough, but at least family members can use toilets and wash in their workplace or public buildings. However during lock down, confined in one’s home, there was no such recourse; bottled water could be brought in, but without a water supply faeces had to be collected and thrown out with the rubbish. One BBC report told the harrowing story of a woman with a family of eleven, who refused to let helpers drop off water at her home for shame of the smell.

How does water, the universal gift of God, become a commodity and privilege?

Water is not mentioned explicitly in the Universal Declaration of Human Rights (UDHR), but  Article 25 guarantees the right to:

“a standard of living adequate for the health and well-being of himself and of his family, including food, clothing, housing and medical care”

In addition, Article 22’s right to “social security” and the “economic, social and cultural rights indispensable for his dignity and the free development of his personality” seems highly pertinent.

Although the order of the 30 articles in the UDHR is not a priority list, it is perhaps telling that Article 25 comes well behind Article 17 which guarantees the right to private property.

Water and War

For many years there have been warnings of the impact of climate change on water supplies and the potential for conflict.  Sometimes this is largely within states as in the case of Australia and the Colorado River in the US (although the latter also impacts North Mexico).  However others cross national borders, such as the long-running disputes on the Nile, including between Egypt and Ethiopia about the construction of the Grand Ethiopian Renaissance Dam.  There have always been water wars, but the likelihood and severity are expected to rise.

It was significant that one of the first actions of the Russian invasion into Ukraine was to reopen the North Crimean Canal, which had been dammed by the Ukrainian government in 2014 cutting off the majority of the fresh water supply to the two and half million people of the Crimean peninsula.  The crisis was largely silent for many years, perhaps in part because Russia does not like to admit weakness, but back in 2020 there were warnings from open source news sites of the ever growing human, ecological and geopolitical crisis. and by 2021 this was picked by Bloomberg and the FT, the latter describing it as a ‘water war‘.  Although there are many interlinked reasons for the conflict in Ukraine, it may be we are already seeing the first major modern water war.

North Crimean Canal. Connects the Denpr at the Kakhovka reservoir with the east of Crimea.

Wikipedia: North Crimean Canal (image: Berihert, CC BY-SA 3.0)


  1. Thanks to Alan Sandry for pointing out the BBC article.[back]
  2. See 9News “Struggling Aussie farmers enraged by incredible water wastage” and full Australia Institute report “Southern discomfort: water losses in the southern Murray Darling Basin“.[back]

Another year – running and walking, changing roles and new books

Yesterday I completed the Tiree Ultramarathon, I think my sixth since they began in 2014. As always a wonderful day and a little easier than last year. This is always a high spot in the year for me, and also corresponds to the academic year change, so a good point to reflect on the year past and year ahead.  Lots of things including changing job role, books published and in preparation, conferences coming to Wales … and another short walk …

Tiree Ultra and Tech Wave

Next week there will be a Tiree Tech Wave, the first since Covid struck. Really exciting to be doing this again, with a big group coming from Edinburgh University, who are particularly interested in co-design with communities.

Aside: I nearly wrote “the first post-Covid Tiree Tech Wave”, but I am very aware that for many the impact of Covid is not past: those with long Covid, immunocompromised people who are in almost as much risk now as at the peak of the pandemic, and patients in hospital where Covid adds considerably to mortality.

Albrecht Schmidt from Ludwig-Maximilians-Universität München was here again for the Ultra. He’s been several times after first coming the year of 40 mile an hour winds and rain all day … he is built of stern stuff.  Happily, yesterday was a little more mixed, wind and driving rain in the morning and glorious sunshine from noon onwards … a typical Tiree day 😊

We have hatched a plan to have Tiree Tech Wave next year immediately after the Ultra. There are a number of people in the CHI research community interested in technology related to outdoors, exercise and well-being, so hoping to have that as a theme and perhaps attract a few of the CHI folk to the Ultra too.

Changing roles

My job roles have changed over the summer.

I’ve further reduced my hours as Director of the Computational Foundry to 50%. University reorganisation at Swansea over the last couple of years has created a School of Mathematics and Computer Science, which means that some of my activities helping to foster research collaboration between CS and Maths falls more within the School role. So, this seemed a good point to scale back and focus more on cross-University digital themes.

However, I will not be idle! I’ve also started a new PT role as Professorial Fellow at Cardiff Metropolitan University. I have been a visiting professor at the Cardiff School of Art and Design for nearly 10 years, so this is partly building on many of the existing contacts I have there. However, my new role is cross-university, seeking to encourage and grow research across all subject areas. I’ve always struggled to fit within traditional disciplinary boundaries, so very much looking forward to this.

Books and writing

This summer has also seen the publication of “TouchIT: Understanding Design in a Physical-Digital World“. Steve, Devina, Jo and I first conceived this when we were working together on the DePTH project, which ran from 2007 to 2009 as part of the AHRC/EPSRC funded Designing for the 21st Century Initiative. The first parts were written in 2008 and 2009 during my sabbatical year when I first moved to Tiree and Steve was our first visitor. But then busyness of life took over until another spurt in 2017 and then much finishing off and updating. However now it is at long last in print!

Hopefully not so long in the process, three more books are due to be published in this coming year, all around an AI theme. The first is a second edition of the “Introduction to Artificial Intelligence” textbook that Janet Finlay and I wrote way back in 1996. This has stayed in print and even been translated into Japanese. For many years the fundamentals of AI only changed slowly – the long ‘AI winter’. However, over recent years things have changed rapidly, not least driven by massive increases in computational capacity and availability of data; so it seemed like a suitable time to revisit this. Janet’s world is now all about dogs, so I’ve taken up the baton. Writing the new chapters has been easy. The editing making this flow as a single volume has been far more challenging, but after a focused writing week in August, it feels as though I’ve broken the back of it.

In addition, there are two smaller volumes in preparation as part of the Routledge and CRC AI for Everything series. One is with Clara Crivellaro on “AI for Social Justice“, the other a sole-authored “AI for Human–Computer Interaction”.

All of these were promised in 2020 early in the first Covid lockdown, when I was (rather guiltily) finding the time tremendously productive. However, when the patterns of meetings started to return to normal (albeit via Zoom), things slowed down somewhat … but now I think (hope!) all on track 😊

Welcoming you to Wales

In 2023 I’m chairing and co-chairing two conferences in Swansea. In June, ACM Engineering Interactive Computer Systems (EICS 2023) and in September the European Conference on Cognitive Ergonomics (web site to come, but here is ECCE 2022). We also plan to have a Techwave Cymru in March. So I’m looking forward to seeing lots of people in Wales.

As part of the preparation to EICS I’m planning to do a series of regular blog posts on more technical aspects of user interface development … watch this space …

Alan’s on the road again

Nearly ten years ago, in 2013, I walked around Wales, a personal journey and research expedition. I always assumed I would do ‘something else’, but time and life took over. Now, the tenth anniversary is upon me and it feels time do something to mark it.

I’ve always meant to edit the day-by-day blogs into a book, but that certainly won’t happen next year. I will do some work on the dataset of biodata, GPS, text and images that has been used in a few projects and is still a unique data set, including, I believe, still the largest single ECG trace in the public domain.

However, I will do ‘something else’.

When walking around the land and ocean boundaries of Wales, I was always aware that while in some sense this ‘encompassed’ the country, it was also the edge, the outside. To be a walker is to be a voyeur, catching glimpses, but never part of what you see.  I started then to think of a different journey, to the heart of Wales, which for me, being born and brought up in Cardiff, is the coal valleys stretching northwards and outwards. The images of coal blackened miners faces and the white crosses on the green hillside after Aberfan are etched into my own conception of Wales.

So, there will be an expedition, or rather as series of expeditions, walking up and down the valleys, meeting communities, businesses, schools and individuals.

Do you know places or people I should meet?

Do you want to join me to show me places you know or to explore new places?

Sampling Bias – a tale of three Covid news stories

If you spend all your time with elephants, you might think that all animals are huge. In any experiment, survey or study, the results we see depend critically on the choice of people or things we consider or measure.

Three recent Covid-19 news stories show the serious (and in one case less serious) impact of sampling bias, potentially creating misleading or invalid results.


  • Story 1 – 99.9% of deaths are unvaccinated – An ONS report in mid-September was widely misinterpreted and led to the mistaken impression that virtually all UK deaths were amongst those who were unvaccinated.  This is not true: whilst vaccination has massively reduced deaths and serious illness, Covid-19 is still a serious illness even for those who are fully jabbed.
  • Story 2 – Lateral flow tests work – They do! False positives are known to be rare (if it says you’ve got it you probably have), but data appears to suggest that false negatives (you get a negative result, but actually have Covid) are much higher.  Researchers at UCL argue that this is due to a form of sampling bias and attempt to work out the true figure … although in the process they slightly overshoot the mark!
  • Story 3 – Leos get their jabs – Analysis of vaccination data in Utah found that those with a Leo star sign were more than twice as likely to be vaccinated than Libras or Scorpios.  While I’d like to believe that Leos are innately more generous of spirit, does your star sign really influence your likelihood of getting a jab?

In the last story we also get a bit of confirmation bias and the  file-drawer effect to add to the sampling bias theme!

Let’s look at each story in more detail.

Story 1 – 99.9% of deaths are unvaccinated

I became aware of the first story when a politician on the radio said that 99.9% of deaths in the UK were of unvaccinated people.  This was said I think partly to encourage vaccination and partly to justify not requiring tougher prevention measures.

The figure surprised me for two reasons:

  1. I was sure I’d seen figures suggesting that there were still a substantial number of ‘breakthrough infections’ and deaths, even though the vaccinations were on average reducing severity.
  2. As a rule of thumb, whenever you hear anything like “99% of people …” or “99.9% of times …”, then 99% of the time (sic) the person just means “a lot”.

Checking online newspapers when I got home I found the story that had broken that morning (13th Sept 2021) based on a report by the Office of National Statistics, “Deaths involving COVID-19 by vaccination status, England: deaths occurring between 2 January and 2 July 2021“.  The first summary finding reads:

In England, between 2 January and 2 July 2021, there were 51,281 deaths involving coronavirus (COVID-19); 640 occurred in people who were fully vaccinated, which includes people who had been infected before they were vaccinated.

Now 640 fully vaccinated deaths out of 51,281 is a small proportion leading to newspaper headlines and reports such as “Fully vaccinated people account for 1.2% of England’s Covid-19 deaths” (Guardian) or “Around 99pc of victims had not had two doses” (Telegraph).

In fact in this case the 99% figure does reflect the approximate value from the data, the politician had simply added an extra point nine for good measure!

So, ignoring a little hyperbole, at first glance it does appear that nearly all deaths are of unvaccinated people, which then suggests that Covid is pretty much a done deal and those who are fully vaccinated need not worry anymore.  What could be wrong with that?

The clue is in the title of the report “between 2 January and 2 July 2021“.  The start of this period includes the second wave of Covid in the UK.  Critically while the first few people who received the Pfizer vaccine around Christmas-time were given a second dose 14 days later, vaccination policy quickly changed to leave several months between first and second vaccine doses. The vast majority of deaths due to Covid during this period happened before mid-February, at which point fewer than half a million people had received second doses.

That is, there were very few deaths amongst the fully vaccinated, in large part because there were very few people doubly vaccinated.  Imagine the equivalent report for January to July 2020, of 50 thousand deaths there would have been none at all of the fully vaccinated.

This is a classic example of sampling bias, the sample during the times of peak infection was heavily biased towards the unvaccinated, making it appear that the ongoing risk for the vaccinated was near zero.

The ONS report does make the full data available.  By the end of the period the number who were fully vaccinated had grown to over 20 million. The second wave had long passed and both the Euros and England’s ‘Freedom Day’ had not yet triggered rises in cases. Looking below, we can see the last five weeks of the data, zooming into the relevant parts of the ONS spreadsheet.

Notice that the numbers of deaths amongst the fully vaccinated (27, 29, 29, 48, 63) are between one-and-a-half and twice as high as those amongst the unvaccinated (18, 20, 13, 26, 35 ).  Note that this is not because the vaccine is not working; by this point the vaccinated population is around twice as high as the unvaccinated (20 million to 10 million). Also, as vaccines were rolled out first to the most vulnerable, these are not comparing similar populations (more sampling bias!).

The ONS do their best to correct for the latter sampling bias and the column (slightly confusingly) labelled “Rate per 100,000 population“, uses the different demographics to estimate the death rate if everyone were in that vaccination bracket. That is, in the week ending 2nd July (last line of the table) if everyone were unvaccinated one would expect 1.6 deaths per 100,000 whereas if everyone were vaccinated, we would expect 0.2 deaths per 100,000.

It is this (buried and complex) figure which is actually the real headline – vaccination is making a ten-fold improvement.  (This is consonant with more recent data suggesting a ten-fold improvement for most groups and a lower, but still substantial four-fold improvement for the over-80s.)  However, most media picked up the easier to express – but totally misleading – total numbers of deaths figures, leading to the misapprehension amongst some that it is “all over”.

To be fair the ONS report includes the caveat:

Vaccinations were being offered according to priority groups set out by the JCVI, therefore the characteristics of the vaccinated and unvaccinated populations are changing over time, which limits the usefulness of comparing counts between the groups.

However, it is somewhat buried and the executive summary does not emphasise the predictably misleading nature of the headline figures.


  • for Covid – Vaccination does make things a lot better, but the rate of death and serious illness is still significant
  • for statistics – Even if you understand or have corrected for sampling bias or other statistical anomalies, think about how your results may be (mis)interpreted by others

Story 2 – Lateral flow tests work

Lateral flow tests are the quick-and-dirty weapon in the anti-Covid armoury  They can be applied instantly, even at home; in  comparison the ‘gold standard’ PCR test can take several days to return.

The ‘accuracy’ of lateral flow tests can be assessed by comparing with PCR tests.  I’ve put ‘accuracy’ in scare quotes as there are multiple formal measures.

A test can fail in two ways:

  • False Positive – the test says you have Covid, but you haven’t.  – These are believed to be quite rare, partly because the tests are tuned not to give false alarms too often, especially when prevalence is low.
  • False Negative – the test says you don’t have Covid, but you really do. – There is a trade-off in all tests: by calibrating the test not to give too many false alarms, this means that inevitably there will be times when you actually have the disease, but test negative on a lateral flow test.  Data comparing lateral flow with PCR suggests that if you have Covid-19, there is still about a 50:50 chance that the test will be negative.

Note that the main purpose of the lateral flow test is to reduce the transmission of the virus in the population.  If it catches only a fraction of cases this is enough to cut the R number. However, if there were too many false positive results this could lead to large numbers of people needlessly self-isolating and potentially putting additional load on the health service as they verify the Covid status of people who are clear.

So the apparent high chance of false negatives doesn’t actually matter so much except insofar as it may give people a false sense of security.  However, researchers at University College London took another look at the data and argue that the lateral flow tests might actually be better than first thought.

In a paper describing their analysis, they note that a person goes through several stages during the illness; critically, you may test positive on a PCR if:

  1. You actively have the illness and are potentially infectious (called D2 in the paper).
  2. You have recently had the illness and still have a remnant of the virus in your system, but are no longer infectious (called D3 in the paper).

The virus remnants detected during the latter of these (D3) would not trigger a lateral flow test and so people tested with both during this period would appear to be a false negative, but in fact the lateral flow test would accurately predict that they are not infectious. While the PCR test is treated as ‘gold standard’, the crucial issue is whether someone has Covid and is infectious – effectively PCR tests give false positives for a period after the disease has run its course.

The impact of this is that the accuracy of lateral flow tests (in terms of the number of false negatives), may be better than previously estimated, because this second period effectively pollutes the results. There was a systematic sampling bias in the original estimates.

The UCL researchers attempt to correct the bias by using the relative proportion of positive PCR tests in the two stages D2/(D2+D3); they call this ratio π (not sure why).  They use a figure of 0.5 for this (50:50 D2:D3) and use it to estimate that the true positive rate (specificity) for lateral flow tests is about 80%, rather than 40%, and correspondingly the false negative rate only about 20%, rather than 60%.  If this is right, then this is very good news: if you are infectious with Covid-19, then there is an 80% chance that lateral flow will detect it.

The reporting of the paper is actually pretty good (why am I so surprised?), although the BBC report (and I’m sure others) does seem to confuse the different forms of test accuracy.

However, there is a slight caveat here, as this all depends on the D2:D3 ratio.

The UCL researchers use of 0.5 for π is based on published estimates of the period of detectable virus (D2+D3) and infectiousness (D2).  They also correctly note that the effective ratio will depend on whether the disease is growing or decaying in the population (another form of sampling bias similar to the issues in measuring the serial interval for the virus discussed in my ICTAC keynote).  Given that the Liverpool study on which they based their own estimates had been during a time of decay, they note that the results may be even better than they suggest.

However, there is yet another sampling bias at work!  The low specificity figures for lateral flow are always on asymptomatic individuals.  The test is known to be more accurate when the patient is already showing symptoms.  This means that lateral flow tests would only ever be applied in stage D3 if the individual had never been symptomatic during the entire infectious period of the virus (D2).  Early on it was believed that a large proportion of people may have been entirely asymptomatic; this was perhaps wishful thinking as it would have made early herd immunity more likely.  However a systematic review suggested that only between a quarter and a third of cases are never symptomatic, so that the impact of negative lateral flow tests during stage D3 will be a lot smaller than the paper suggests.

In summary there are three kinds of sampling effects at work:

  1. inclusion in prior studies of tests during stage D3 when we would not expect nor need lateral flow tests to give positive results
  2. relative changes in the effective number of people in stages D2 and D3 depending on whether the virus is growing or decaying in the population
  3. asymptomatic testing regimes that make it less likely that stage D3 tests are performed

Earlier work ignored (1) and so may under-estimate lateral flow sensitivity. The UCL work corrects for (1), suggesting a far higher accuracy for lateral flow, and discusses (2), which means it might be even better.  However, it misses (3), so overstates the improvement substantially!


  • for Covid – Lateral flow tests may be more accurate than first believed, but a negative test result does not mean ‘safe’, just less likely to be infected.
  • for statistics – (i) Be aware of time-based sampling issues when populations or other aspects are changing.  (ii) Even when you spot one potential source of sampling bias, do dig deeper; there may be more.

Story 3 – Leos get their jabs

Health department officials in Salt Lake County, Utah decided to look at their data on vaccination take-up.  An unexpected result was that there appeared to be  a substantial difference between citizens with different birth signs. Leos topped the league table with a 70% vaccination rate whilst Scorpios trailed with less than half vaccinated.

Although I’d hate to argue with the obvious implication that Leos are naturally more caring and considerate, maybe the data is not quite so cut and dried.

The first thing I wonder when I see data like this is whether it is simply a random fluke.  By definition the largest element in any data set tends to be a bit extreme, and this is a county, so maybe the numbers involved are quite large.  However, Salt Lake County is the largest county in Utah with around 1.2 million residents according to the US Census; so, even ignoring children or others not eligible, still around 900,000 people.

Looking at the full list of percentages, it looks like the average take-up is between 55% and 60%, with around 75,000 people per star sign (900,000/12).  Using my quick and dirty rule for this kind of data: look at the number of people in the smaller side (30,000 = 40% of 75,000); take its square root (about 170); and as it is near the middle multiply by 1.5 (~250).  This is the sort of variation one might expect to see in the data.  However 250 out of 75,000 people is only about 0.3%, so these variations of +/-10% look far more than a random fluke.

The Guardian article about this digs a little deeper into the data.

The Utah officials knew the birth dates of those who had been vaccinated, but not the overall date-of-birth data for the county as a whole.  If this were not uniform by star sign, then it could introduce a sampling bias.  To counteract this, they used national US population data to estimate the numbers in each star sign in the county and then divided their own vaccination figure by these estimated figures.

That is, they combined two sets of data:

  • their own data on birth dates and vaccination
  • data provided (according to the Guardian article) by University of Texas-Austin on overall US population birth dates

The Guardian suggests that in attempting to counteract sampling bias in the former, the use of the latter may have introduced a new bias. The Guardian uses two pieces of evidence for this.

  1. First an article in the journal Public Health Report that showed that seasonal variation in births varied markedly between states, so that comparing individiual states or counties with national data could be flawed.
  2. Second a blog post by Swint Friday of the College of Business Texas A&M University-Corpus Christi, which includes a table (see below) of overall US star sign prevalence that (in the Guardian’s words) “is a near-exact inverse of the vaccination one“, thus potentially creating the apparent vaccination effect.

Variations in birth rates through the year are often assumed to be in part due to seasonal bedtime activity: hunkering down as the winter draws in vs. short sweaty summer nights; while the Guardian, cites a third source, The Daily Viz, to suggest that “Americans like to procreate around the holiday period“. More seriously, the Public Health Report article also links this to seasonal impact on pre- and post-natal mortality, especially in boys.

Having sorted the data in their own minds, the Guardian reporting shifts to the human interest angle, interviewing the Salt Lake health officials and their reasons for tweeting this in the first place.

But … yes, there is always a but … the Guardian fails to check the various sources in a little more detail.

The Swint Friday blog has figures for Leo at 0.063% of the US population whilst Scorpio tops it at 0.094%, with the rest in between.  Together the figures add up to around 1% … what happened to the other 99% of the population … do they not have a star sign?  Clearly something is wrong, I’m guessing the figures are proportions not percentages, but it does leave me slightly worried about the reliability of the source.

Furthermore, the Public Health Report article (below) shows July-Aug (Leo period) slightly higher rather than lower in terms of birth date frequency, as does more recent US data on births.

from PASAMANICK B, DINITZ S, KNOBLOCH H. Geographic and seasonal variations in births. Public Health Rep. 1959 Apr;74(4):285-8. PMID: 13645872; PMCID: PMC1929236

Also, the ratio between largest and smallest figures in the Swint Friday table is about a half of the smaller figure (~1.5:1), whereas in the figure above it is about an eighth and in the recent data less than a tenth.

The observant reader might also notice the date on the graph above, 1955, and that it only refers to white males and females.  Note that this comes from an article published in 1959, focused on infant mortality and exemplifies the widespread structural racism in the availability of historic health data.  This is itself another form of sampling bias and the reasons for the selection are not described in the paper, perhaps it was just commonly accepted at the time.

Returning to the date, as well as describing state-to-state variation, the paper also surmises that some of this difference may be due to socio-economic factors and that:

The increased access of many persons in our society to the means of reducing the stress associated with semitropical summer climates might make a very real difference in infant and maternal mortality and morbidity.

Indeed, roll on fifty years, and looking at the graph at Daily Viz based on more recent US government birth data produced at Daily Viz, the variation is indeed far smaller now than it was in 1955.

from How Common Is Your Birthday? Pt. 2., the Daily Viz, Matt Stiles, May 18, 2012

As noted the data in Swint Friday’s blog is not consistent with either of these sources, and is clearly intended simply as a light-hearted set of tables of quick facts about the Zodiac. The original data for this comes from Statistics Brain, but this requires a paid account to access, and given the apparent quality of the resulting data, I don’t really want to pay to check! So, the ultimate origins of thsi table remains a mystery, but it appears to be simply wrong.

Given it is “a near-exact inverse” of the Utah star sign data, I’m inclined to believe that this is the source that Utah health officials used, that is data from the Texas A&M University, not Texas University Austin.  So in the end I agree with the Guardian’s overall assessment, even if their reasoning is somewhat flawed.

How is it that the Guardian did not notice these quite marked discrepancies in the data. I think the answer is confirmation bias, they found evidence that agreed with their belief (that Zodiac signs can’t affect vaccination status) and therefore did not look any further.

Finally, we only heard about this because it was odd enough for Utah officials to tweet about it.  How many other things did the Utah officials consider that did not end up interesting?  How many of the other 3000 counties in the USA looked at their star sign data and found nothing.  This is a version of the  file-drawer effect for scientific papers, where only the results that ‘work’ get published.  With so many counties and so many possible things to look at, even a 10,000 to 1 event would happen sometimes, but if only the 10,000 to one event gets reported, it would seem significant and yet be pure chance.


  • for Covid – Get vaccinated whatever your star sign.
  • for statistics – (i) Take especial care when combining data from different sources to correct sampling bias, you might just create a new bias. (ii) Cross check sources for consistency, and if they are not why not? (iii) Beware confirmation bias, when the data agrees with what you believe, still check it!  (iv) Remember that historical data and its availability may reflect other forms of human bias. (v) The file-drawer effect – are you only seeing the selected apparently unusual data?


Universities and Covid – how bad was it and what next?

A record number of students have been heading to universities over the last few weeks.  They will still face Covid-restriction, however, happily the situation will be nothing like last year.

Last year I had my own concerns early on, and in retrospect it is easier to assess just how bad things were.  Combining SAGE’s Sept 2020 estimates of the impact with actual Covid mortality would suggest that during 2020-2021 there was an additional death for every 50-100 university students educated. There are arguments to reduce this figure somewhat; however, it is still clear that society at large paid heavily to enable education to continue.

Happily, this year vaccination has vastly reduced mortality, albeit set against very high case numbers. Although things will be more ‘normal’ this year, as a sector, we are still clearly deeply indebted to the rest of society and need to do all we can to minimise further impact.

The data – how bad was it?

Early in the summer of 2020 I estimated that the potential impact of autumn University return would be to at least double the number of Covid cases unless major action was taken to mitigate the risks.  Based on figures for the first wave and projections for 2020-2021 winter, I put the figure at around 50,000 deaths.

At the time this was derided as heavily pessimistic, but of course within months SAGE modelling estimates came out with far higher figures.  SAGE’s  “Summary of the effectiveness and harms of different non-pharmaceutical interventions, 21 September 2020” estimated that without substantial mitigation, university return in 2020 would lead to an increase in R of between 0.2 and 0.5, which corresponds to not just double, but between eight to sixty times as many cases over the first term.

This was all based on modelling, but the impact was evident in actual case data as Universities returned. This was particularly clear in Scotland as universities returned in mid-September where there was an almost instant doubling of infections in the university age group, which then fed into other cohorts over the succeeding weeks.

As well as more local measures, the Universities Scotland issued guidance for the weekend of 25-27 Sept 2020 asking students to avoid socialising outside their households and avoiding bars and other such venues.

In the rest of the UK the data was a little less clear as university return dates are more staggered, but there was a clear step change at the beginning of October 2020.

In Newcastle the local newspaper analysed national data and found that areas with high student density had Covid rates five times higher than areas with few students.  More anecdotally, we will all remember the images of students’ messages on their windows as halls went into effective lock-in, and the (rapidly removed) fencing around Manchester halls of residence.

This initial surge was due to the combination of simply lots of people coming together and establishing new contact networks, a known Covid risk, and the more obvious effect of start-of-term parties and ‘freshers week’ high spirits.

It is far harder to assess more long-term impacts during the year, as this simply added to the general societal growth.  Modelling can be used to attempt to disentangle these effects, but it is difficult to definitively separate effects of coupled dynamic systems  except during periods of sudden change.  There were noticeable end-of-year spikes in student areas of Leeds reported in June, but that, like the year start, was more about end of term parties, not the general effect of increased contact networks.

Mitigations – it could have been worse

SAGE’s figures, like my own, were for University return without mitigations. and they suggested potential actions to reduce the impact, some of which were headed.

Every university made very strong efforts to reduce spread within teaching environments, whilst still offering levels of in-person activities, but it was, and still is, the social side of student life that was expected to be most problematic.

Anticipating the mixing during Freshers Week, my own University and I know many others, created outdoor bars and activities in order to create spaces that were safer and less likely to lead to cross infection.  This was effective in that the majority of traced ‘superspreader’-style outbreaks seemed to be related to off-campus parties or events.

Students also took matters into their own hands.  For every highly publicised case of wild parties and ignoring of Covid rules, I heard other less highly published accounts of students effectively permanently isolating themselves in their rooms.  I also know of universities where courses that started off in hybrid mode with a mix of in-person and remote activities ended up abandoning the in-person elements as students effectively voted with their feet. I think this was principally the case for universities with a large number of local students, but also some students simply returned home and completed their studies remotely.

But students are young, so not at risk

One of the difficulties when thinking both about universities and schools, is that Covid is not particularly dangerous for those in their teens and twenties.  This is not to say no risk for pupils and students, especially for anyone with other health problems.  There is of course more risk for academics and teachers, and even more other staff such as cleaners, security and catering, who typically have older demographics than teachers and academics, but still the risk for working age adults was always smaller.

The biggest problem was, and still is, the spread into the community as a whole.  The Scottish data for last autumn showed this indeed did happen within weeks.  This is partly due to out-of-house contacts such as buses and shops, and partly due to home visits (for away-from-home students) and local students living at home.

These contacts then seed others and these indirect contacts, contacts of contacts, etc. far exceed the number of initial cases, and furthermore ended up spread over all demographics of society including the most vulnerable.  When the disease is near static (R ~ 0.9–1.1) this leads to around 10 additional cases for each initial case over a 2-3 month window, higher during times of higher growth.  While universities actively published the number of actual student and staff cases, these were the relatively safe tip of a far more deadly iceberg.

Last year, before the vaccine and new variants, these knock-on infections meant that each preventable infection would have a one in ten chance of causing an eventual death (see “More than R – how we underestimate the impact of Covid-19 infection” for the details of this figure).  At our current mid-vaccine stage, but with delta, the figure is about one in fifty – still far higher than any of the common risks we impose upon one another such as car driving, second-hand smoking or general pollution.

What about variants?

While the data suggests that at least half of the cases during the autumn of 2020 were due to university returns, the original Covid variant was overtaken first of all by alpha variant and then by delta variant.  There is thus an argument that only the deaths due to the original variant be counted, that is perhaps 10,000 deaths rather than 40-50,000.

For the delta variant this is undoubtedly the case; it quickly overcame the original variant and so the number of cases before the delta variant emerged are largely irrelevant to those that came after.  However, delta only emerged in the UK as the second wave decayed and after the majority of deaths, so it makes little difference to the overall tally.

Alpha is more complex.  Nearly all second wave deaths were due to alpha, and these constitute the larger part of winter 2020–2021 Covid deaths.

It is almost certain that alpha developed in the UK.  It could be that it developed in a person who would have been infected anyway irrespective of the universities.  If so then only around a half of pre-Christmas deaths should be attributed to the universities. However, if it developed as a mutation in someone who would not have been otherwise infected, not only all of the alpha variant UK deaths, but also all alpha variant deaths worldwide would land at our doorstep.

There is no way of knowing, but the odds as to which of these is the case run exactly with the proportion of cases due to the universities, so the best estimate is still to count that proportion of UK deaths and in principle a proportion of worldwide alpha-variant deaths also, but I don’t have the heart to calculate that figure, only knowing it is a lot, lot higher.

Why not blame schools?

Arguably, it is unfair to pin the increase entirely on the universities.

According to the SAGE estimates in Sept 2020, the two largest potential drivers of Covid were schools and universities.  Each were expected to lead to increases in R of 0.2 to 0.5. That is, if universities had returned but schools not reopened, while the universities would have still doubled the number of cases, this would have doubling a smaller number.  Given both schools and universities have similar figures then maybe it would be more fair to divide the combined impact between them, leading to maybe 3/8 of cases being assigned to each rather than half the cases to the universities.

This is a tenable argument, and indeed it is always hard to apportion blame or cost when faced with multiple causes that lead to non-linear effects.

Personally, I discount this.  First because it doesn’t make so much difference, 3/8 of a big number is still very large.  Second there were far stronger arguments for reopening schools: (i) because being more local to start with it was easier to mitigate their impact; (ii) because school children are younger it is harder for them to cope with remote learning, and (iii) because reopening schools freed up parents from childcare allowing other sectors of the economy to recover.  However, if you disagree knock a quarter off all of the figures for the impact of universities.

Maybe not so bad – lockdowns and government policy

Finally, while the bald figure of one death for every 50 to 100 students educated is frighteningly large, there is I think there is a good argument to reduce this substantially, albeit opening up the issue of wider non-mortality costs for society.

Last autumn Covid cases were increasing rapidly and the UK government was set against any further control measures.  Eventually it was forced to instigate a November lockdown across England after the earlier Wales ‘firebreak’.  The trigger for this was not the cases per se, but the danger of overwhelming the NHS ability to cope.

Those on the front-line of the NHS would debate how close we got to breakdown, and indeed whether in many ways we went beyond it.  However, crucially the driver of policy has been not Covid cases as such, nor even Covid deaths, but the number of hospital and especially intensive care admissions.

If Covid cases had been only half as high, there might not have been a pre-alpha lockdown at all before Christmas, or if there had been it would have been later as would the January lockdown.

By this argument, which I believe is a sound one, the impact of last year’s universities reopening was to accelerate growth, leading to earlier and longer lockdowns.  The increase in university-attributable deaths would by this argument still not be negligible, but lower, maybe less than 10,000 (about one for every 250 students educated).  However, this is then offset against the additional strain put on the rest of society, not least on the jobs of the other 50% of 18-21 year olds who don’t go to university.

In summary

First of all, it should be noted that there will be a further hit as universities return now, and a recent Times Higher survey reported that more than half of lecturers had serious concerns about the new term. However, the corresponding figures for this year will be an order of magnitude lower.  This does not mean we should not take every precaution possible, Covid deaths are still at levels that would be inconceivable if we hadn’t seen them so much higher previously.  At the time of writing, there are as many deaths due to Covid in two weeks as a whole year’s worth of road deaths.

As is probably evident, certainly from previous writing about the issue, I believe the decision to reopen the HE sector in Autumn 2020 was fundamentally wrong.  As I have previously argued, the universities’ hands were largely tied, as were to a lesser extent the devolved governments, by decisions taken at Westminster.  I assume that these decisions were partly party political (not wanting to alienate half of first-time voters) and partly financial (reducing the need to prop up the HE sector groaning under the increased costs of dealing with remote teaching).

The result of this was a worst of all possible worlds: bad for students who often ended up paying for semi-useless accommodation and being taught remotely during lockdowns anyway; bad for lecturers trying to cope with mixed models of teaching and the uncertainty of constantly switching of models; and bad for society deepening both the health and economic crisis.

Possibly saying that the universities’ hands were tied by government and that in turn as an employee of the university I was just continuing to do my job is a version of the concentration-camp guard excuse.  Personally I feel the weight of this: I knew what was unfolding, I had written about it, but could I have done more to raise the issue?

Looking forward we can still make a difference.

I’m part of the Not-Equal research network focused on issues social justice in the digital economy.  We are coming to the end of our funded period and had originally hoped to have an in-person end-of-project event bringing together the many academics and third-sector stake-holders who have been part of the network to share experiences and maybe create new partnerships looking forward.  During the summer, after consulting with our advisory board, we unanimously decided to instead have a purely virtual event.  Meeting together would have clearly had great advantages, but it felt that holding such an event, however worthy would be irresponsible.

Each such decision only makes a small difference, but it is the tens of thousands of such small acts that make a big difference.  This has been one of the hard to comprehend lessons of Covid, but one that will continue to be important as we shift our focus back towards other massive issues of poverty, social injustice, climate change and the myriad diseases other than Covid that plague so many in the world.

Busy September – talks, tutorials and an ultra-marathon

September has been a full month!

During the last two weeks things have started to kick back into action, with the normal rounds of meetings and induction week for new students.  For the latter I’d pre-recorded a video welcome, so my involvement during the week was negligible.  However, in addition I delivered a “Statistics for HCI” day course organised by the BCS Interaction Group with PhD students from across the globe and also a talk “Designing User Interactions with AI: Servant, Master or Symbiosis” at the AI Summit London.  I was also very pleased to be part of the “60 faces of IFIP” campaign by the International Federation for Information Processing.

It was the first two weeks that stood out though, as I was back on Tiree for two whole weeks.  Not 100% holiday as during the stay I gave two virtual keynotes: “Qualitative–Quantitative Reasoning: thinking informally about formal things” at the International Colloquium on Theoretical Aspects of Computing (ICTAC) in Kazakhstan and “Acting out of the Box” at the University of Wales Trinity St David (UWTSD) Postgraduate Summer School.  I also gave a couple of lectures on “Modelling interactions: digital and physical” at the ICTAC School which ran just before the conference and presented a paper on “Interface Engineering for UX Professionals” in the Workshop on HCI Engineering Education (HCI-E2) at INTERACT 2021 in Bari.  Amazing how easy it is to tour the world from a little glamping pod on a remote Scottish Island.

Of course the high point was not the talks and meetings, but the annual Tiree Ultra-marathon.  I’d missed last year, so especially wonderful to be back: thirty five miles of coastline, fourteen beaches, not to mention so many friendly faces, old friends and new.  Odd of course with Covid zero-contact and social distancing – the usual excited press of bodies at the pre-race briefing in An Talla, the Tiree community hall, replaced with a video webinar and all a little more widely spaced for the start on the beach too.

The course was slightly different too, anti-clockwise and starting half way along Gott Bay, the longest beach.  Gott Bay is usually towards the end of the race, about 28 miles in, so the long run, often into the wind is one of the challenges of the race.  I recall in 2017 running the beach with 40 mile an hour head wind and stinging rain – I knew I’d be faster walking, but was determined to run every yard of beach..  Another runner came up behind me and walked in my shelter.  However, this year had its own sting in the tail with Ben Hynish, the highest point, at 26 miles in.

The first person was across the line in about four-and-a-quarter hours, the fastest time yet.  I was about five hours later!

This was my fifth time doing the ultra, but the hardest yet, maybe in part due to lockdown couch-potato-ness!  My normal training pattern is that about a month before the ultra I think, “yikes, I’ve not run for a year” and then rapidly build up the miles – not the recommended training regime!  This year I knew I wasn’t as fit as usual, so I did start in May … but then got a knee injury, then had to self-isolate … and then it was into the second-half of July; so about a month again.

Next year it will be different, I will keep running through the winter … hmm … well, time will tell!

The different September things all sound very disparate – and they are, but there are some threads and connections.

The first thread is largely motivational.

The UWTSD keynote was about the way we are not defined by the “kind of people” we think of ourselves as being, but by the things we do.  The talk used my walk around Wales in 2013 as the central example, but the ultra would have been just as pertinent.  Someone with my waistline is not who one would naturally think as being an ultramarathon runner – not that kind of person, but I did it.

However, I was not alone.  The ‘winners’ of the ultra are typically the rangy build one would expect of a long-distance runner, but beyond the front runners, there is something about the long distance that attracts a vast range of people of all ages, and all body shapes imaginable.  For many there are physical or mental health stories: relationship breakdowns, illnesses, that led them to running and through it they have found ways to believe in themselves again.  Post Covid this was even more marked: Will, who organises the ultra, said that many people burst into tears as they crossed the finish line, something he’d never seen before.

The other thread is about the mental tools we need to be a 21st century citizen.

The ICTAC keynote was about “Qualitative–Quantitative Reasoning”, which is my term for the largely informal understanding of numbers that is so important for both day-to-day and professional life, but is not part of formal education.  The big issues of our lives from Covid to Brexit to climate change need us to make sense of large-scale numerical or data-rich phenomena.  These often seem too complex to make sense of, yet are ones where we need to make appropriate choices in both our individual lives and political voices.  It is essential that we find ways to aid understanding in the public, press and politicians – including both educational resources and support tools.

The statistics course and my “Statistics for HCI” book are about precisely this issue – offering ways to make sense of often complex results of statistical analysis and obtain some of the ‘gut’ understanding that professional statisticians develop over many years.

My 60 faces of IFIP statement also follows this broad thread:

“Digital techology is now essential to being a citizen. The future of information processing is the future of everyone; so needs to be understood and shaped by all. Often ICT simply reinforces existing patterns, but technology is only useful if we can use it to radically reimagine a better world.

More information on different events

Tiree Ultra

Tiree Ultramarathon web page and Facebook Group

Paper: Interface Engineering for UX Professionals

HCI-E2: Workshop on HCI Engineering Education – for developers, designers and more, INTERACT 2021, Bari, Italy – August 31st, 2021. See more – paper and links

Summer School Lecturea: Modelling interactions: digital and physical

Lecture at ICTAC School 2021: 18th International Colloquium on Theoretical Aspects of Computing, Nazarbayev University, Nur-Sultan, Kazakhstan, 1st September 2021. See more – abstract and links

Talk: Designing User Interactions with AI: Servant, Master or Symbiosis

The AI Summit London, 22nd Sept. 2021. See moreabstract and links

Day Course: Statistics for HCI

BCS Interaction Group One Day Course for PhD Students, 21st Sept. 2021.
See my Statistics for HCI Micro-site.

Keynote: Acting out of the Box

Rhaglen Ysgol Haf 2021 PCYDDS / UWTSD Postgraduate Summer School 2021, 10th Sept. 2021. See more – abstract and links

Keynote: Qualitative–Quantitative Reasoning: thinking informally about formal things

18th International Colloquium on Theoretical Aspects of Computing, Nazarbayev University, Nur-Sultan, Kazakhstan, 10th Sept. 2021. See more – full paper and links

Induction week greeting


The big stories buried beneath the headlines

In news stories this morning about pet abduction and sustainable fashion, the most critical parts are buried deep in the article: a chance remark that gives away the bigger story.

During the lockdown there has been a steep rise in the cost of dogs and other pets, and this has led to an increase in pet abductions. The most high profile example was when Lady Gaga’s dog walker was shot during the theft of her bulldogs in Los Angeles, but the BBC reports that there are over 2000 pet thefts in the UK alone last year.

Stock image of a person stealing a dog

Pet abduction to be made new criminal offence in thefts crackdown – BBC News

In principle pet theft is a crime covered by the UK Theft Act, but the use of this evidently does not reflect the emotional harms of pet abductions, hence the need for the new law. Reading further the article says:

Although offences under the Theft Act 1968 carry a maximum term of seven years, ministers say there is little evidence of that being used, because the severity of the sentence is partly determined by the monetary value of the item taken.

It was this that caught my eye.  The most severe penalties under the Theft Act are for the most valuable items.  If the second-hand car of a pensioner near the poverty line is stolen, it will attract a less severe sentence than the trophy Porsche from the millionaire’s collection.  This sounds like a law made in the 17th century, but is in fact from 1968 and applies today.

The lesson is clear, if you are poor then even the criminal law does nothing for you.

The second story is about Molly-Mae, ex-Love Island contestant and social media influencer, who has just been recruited as creative director of Pretty Little Things with a particular focus on sustainable fashion.


Molly Mae

Molly-Mae: “I’m not just an influencer anymore”

Reading further there is a section entitled “Wearing the same dress twice”, that has the following quote from Molly-Mae:

“I even captioned one of my Instagram pictures the other day saying ‘PSA it’s ok to wear the same dress twice’ – it’s a bad habit us girls have got into, like if you put it on Instagram it means you can’t wear it again.”

Although I did know some of the figures for this before, it still shocked me to hear that “wearing the same dress twice” is regarded as a significant message.

Sadly, this does reflect the previous figures I’ve seen suggesting that the median number of times a garment is worn is indeed one, with something like 20% of clothes never worn at all once bought.  This all has to be added to around 1/3 of fashion clothing that is shredded or otherwise disposed of without ever being sold, due to end of season, returns, or other reasons.

The fashion industry is estimated to contribute 10% of all global carbon emissions, not to mention plastic micro-fibres, chemical, water and other environmental impacts, as well as being built upon near slave-labour conditions across the world.

Given this, even wearing clothes twice could be a major benefit.

However, just imagine how the statement sounds to someone who lived through the second world war, or even anyone over 50.  This is reflected in figures for environmental action by age group: awareness is greatest in the younger age groups, but in nearly all areas life-style action is greatest in the older ones.  Perhaps influencers such as Molly-Mae can help turn this round.

So as you read the news, do look beyond the headlines, the most hard-hitting parts are often buried deep.

dog digging

Image: jimbomack66, CC BY 2.0, via Wikimedia Commons

Darwinian markets and sub-optimal AI

Do free markets generate the best AI?  Not always, and this not only hits the bottom line, but comes with costs for personal privacy and the environment.  The combination of network effects and winner-takes-all advertising expenditure means that the resulting algorithms may be worst for everyone.

A few weeks ago I was talking with Maria Ferrario (Queens University Belfast) and Emily Winter (Lancaster University) regarding privacy and personal data.  Social media sites and other platforms are using ever more sophisticated algorithms to micro-target advertising.  However, Maria had recently read a paper suggesting that this had gone beyond the point of diminishing returns: far simpler  – and less personally intrusive – algorithms achieve nearly as good performance as the most complex AI.  As well as having an impact on privacy, this will also be contributing to the ever growing carbon impact of digital technology.

At first this seemed counter-intuitive.  While privacy and the environment may not be central concerns, surely companies will not invest more resources in algorithms than is necessary to maximise profit?

However, I then remembered the peacock tail.

Jatin Sindhu, CC BY-SA 4.0, via Wikimedia Commons
The peacock tail is a stunning wonder of nature.  However, in strict survival terms, it appears to be both flagrantly wasteful and positively dangerous – like eye-catching supermarket packaging for the passing predator.

The simple story of Darwinian selection suggests that this should never happen.  The peacocks that have smaller and less noticeable tails should have a better chance of survival, passing their genes to the next generation, leading over time to more manageable and less bright plumage.  In computational terms, evolution acts as a slow, but effective optimisation algorithm, moving a population ever closer to a perfect fit with its environment.

However, this simple story has twists, notably runaway sexual selection.  The story goes like this.  Many male birds develop brighter plumage during mating season so that they are more noticeable to females.  This carries a risk of being caught by a predator, but there is a balance between the risks of being eaten and the benefits of copulation.  Stronger, faster males are better able to fight off or escape a predator, and hence can afford to have slightly more gaudy plumage.  Thus, for the canny female, brighter plumage is a proxy indicator of a more fit potential mate.  As this becomes more firmly embedded into the female selection process, there is an arms race between males – those with less bright plumage will lose out to those with brighter plumage and hence die out.  The baseline constantly increases.

Similar things can happen in free markets, which are often likened to Darwinian competition.

Large platforms such as Facebook or Google make the majority of their income through advertising.  Companies with large advertising accounts are constantly seeking the best return on their marketing budgets and will place ads on the platform that offers the greatest impact (often measured by click-through) for the least expenditure.  Rather like mating, this is a winner-takes-all choice.  If Facebook’s advertising is 1% more effective than Google’s  a canny advertiser will place all their adverts with Facebook and vice versa.  Just like the peacock there is an existential need to outdo each other and thus almost no limit on the resources that should be squandered to gain that elusive edge.

In practice there are modifying factors; the differing demographics of platforms mean that one or other may be better for particular companies and also, perhaps most critically, the platform can adjust its pricing to reflect the effectiveness so that click-through-per-dollar is similar.

The latter is the way the hidden hand of the free market is supposed to operate to deliver ‘optimal’ productivity.  If spending 10% more on a process can improve productivity by 11% you will make the investment.  However, the theory of free markets (to the extent that it ever works) relies on an ‘ideal’ situation with perfect knowledge, free competition and low barriers to entry.  Many countries operate collusion and monopoly laws in pursuit of this ‘ideal’ market.

Digital technology does not work like this. 

For many application areas, network effects mean that emergent monopolies are almost inevitable.  This was first noticed for software such as Microsoft Office – if all my collaborators use Office then it is easier to share documents with them if I use Office also.  However, it becomes even more extreme with social networks – although there are different niches, it is hard to have multiple Facebooks, or at least to create a new one – the value of the platform is because all one’s friends use it.

For the large platforms this means that a substantial part of their expenditure is based on maintaining and growing this service (social network, search engine, etc.).  While the income is obtained from advertising, only a small proportion of the costs are developing and running the algorithms that micro-target adverts.

Let’s assume that the ratio of platform to advertising algorithm costs is 10:1 (I suspect it is a lot greater).  Now imagine platform P develops an algorithm that uses 50% more computational power, but improves advertising targeting effectiveness by 10%; at first this sounds a poor balance, but remember that 10:1 ratio.

The platform can charge 10% more whilst being competitive.   However, the 50% increase in advertising algorithm costs is just 5% of the overall company running costs, as 90% are effectively fixed costs of maintaining the platform.  A 5% increase in costs has led to a 10% increase in corporate income.  Indeed one could afford to double the computational costs for that 10% increase in performance and still maintain profitability.

Of course, the competing platforms will also work hard to develop ever more sophisticated (and typically privacy reducing and carbon producing) algorithms, so that those gains will be rapidly eroded, leading to the next step.

In the end there are diminishing returns for effective advertising: there are only so many eye-hours and dollars in users’ pockets. The 10% increase in advertising effectiveness is not a real productivity gain, but is about gaining a temporary increase in users’ attention, given the current state of competing platforms’ advertising effectiveness.

Looking at the system as a whole, more and more energy and expenditure are spent on algorithms that are ever more invasive of personal autonomy, and in the end yield no return for anyone.

And it’s not even a beautiful extravagance.

A brief history of array indices — making programs that fit people

A colleague recently said to me “As computer scientists, our index always starts with a 0“, and my immediate thought was “not when I was a lad“!
As well as revealing my age, this is an interesting reflection on the evolution of programming languages, and in particular the way that programming languages in some ways have regressed in terms of human-centredness expecting the human to think like a machine, rather than the machine doing the work.
But let’s start with array indices.  If you have programmed arrays in Java, Javascript, C++, PHP, or (lists in) Python they all have array indices starting at 0: a[0],,a[1], etc.  Potentially a little confusing for the new programmer, an array of size 5 therefore has last index 4 (five indices: 0,1,2,3,4).  Also code is therefore full of ‘length-1’
double values[] = codeReturningArray();
double first = values[0];
double last = values[values.length-1];
This feels so natural  we hardly notice we are doing it.  However, it wasn’t always like this …
The big three early programming languages were Fortran (for science), Algol (for mathematics and algorithms) and COBOL (for business). In all of these arrays/tables start at 1 by default (reflecting mathematical conventions for matrices and vectors), but both Fortran and Algol could take arbitrary ranges – the compiler did the work of converting these into memory addresses.
Another popular early programming language was BASIC created as a language for learners in 1964, and the arrays in the original Basic also started at 1.  However, for anyone learning Basic today, it is likely to be Microsoft Visual Basic used both for small business applications and also scripting office documents such as Excel.  Unlike the original Basic, the arrays in Visual Basic are zero based arrays ending one less than the array size (like C).  Looking further into the history of this, arrays in the first Microsoft Basic in 1980 (a long time before Wiindows) allowed 0 as a start index, but Dim A(10) meant there were 11 items in the array 0–10. This meant you could ignore the zero index if you wanted and use A(1..10) like in earlier BASIC, Fortran etc, but meaning the compiler had to do less work.

Excerpt from 1964 BASIC manual (download)
In both Pascal and Ada, arrays are more strongly typed, in that the programmer explicitly specifies the index range, not simply a size.  That is, it is possible to declare zero-based arrays A[0..9], one-based arrays A[1..7] or indeed anything else A[42..47].  However, illustrative examples of both Pascal arrays and Ada arrays typically have index types stating at 1 as this was consistent with earlier languages and also made more sense mathematically.
It should be noted that most of the popular early language also allowed matrices or multi-dimensional arrays,
Fortran: DIMENSION A(10,5)
Algol:   mode matrix = [1:3,1:3]real; 
Basic:   DIM B(15, 20)
Pascal:  array[1..15,1..10] of integer;
So, given the rich variety of single and multi-dimensional arrays, how is it that arrays now all start at zero?  Is this the result of deep algebraic or theoretical reflection by the computer science community?  In fact the answer is far more prosaic.
Most modern languages are directly or indirectly influenced by C or one of its offshoots (C++, Java, etc.), and these C-family languages all have zero indexed arrays because C does.
I think this comes originally from BCPL (which I used to code my A-level project at school) which led to B and then C.  Arrays in BCPL were pointer based (as in C) making no distinction between array and pointer.  BCPL treated an ‘array’ declaration as being memory allocation and ‘array access (array!index) as pointer arithmetic.  Hence the zero based array index sort of emerged.
This was all because the target applications of BCPL were low-level system code.  Indeed, BCPL was intended to be a ‘bootstrap’ language (I think the first language where the compiler was written in itself) enabling a new compiler to be rapidly deployed on a new architecture. BCPL (and later C) was never intended for high-level applications such as scientific or commercial calculations, hence the lack of non-zero based arrays and proper multi-dimensional arrays.
This is evident in other areas beyond arrays. I once gave a C-language course at one of the big financial institutions. I used mortgage calculation as an example.  However, the participants quickly pointed out that it was not a very impressive example, as native integers were just too small for penny-accurate calculations of larger mortgages.  Even now with a 64 bit architecture, you still need to use flexible-precision libraries for major financial calculations, which came ‘for free’ in COBOL where numbers were declared at whatever precision you wanted.
Looking back with a HCI hat on, it is a little sad to see the way that programming languages have regressed from being oriented towards human understanding with the machine doing the work to transform that into machine instructions, towards languages far more oriented towards the machine with the human doing the translation 🙁   
Maybe it is time to change the tide.