Linknote — — 144 words

User experience — User research

Work:

How to avoid common mistakes in user engagement — Website and Communications Blog

Have you ever participated in a user engagement session designed for you to share your views, but felt that you weren’t properly included, or that your views wouldn’t be acted on? Fed up with bad surveys and poorly planned focus groups?

Most of us want to engage with our users and stakeholders. We all want to make sure our users have a voice in projects that will affect them. But the approach you take can have a major effect on the success or failure of your engagement.

There are some basic truths about human behaviour that we know from psychology and other social sciences. But in many projects, these basic truths tend to be ignored.

Read this post on my team’s blog for tips on how to avoid the pitfalls of poorly planned user engagement, and how to make user research effective.

Linknote — — 117 words

Content design — Service design — Technology — User experience

Lessons on readability and bias — Reflections from the UCD Gathering conference — Website and Communications Blog

Back in October, I had the opportunity to attend the UCD Gathering conference, a new virtual event for practitioners of user-centred design in all its forms. Over on my work blog, I have published the first of two posts reflecting on what I learned.

This first post covers two themes:

  • Being aware of bias, and other cognitive considerations
  • Improving readability of content

The post also mentions my own session at the conference, about our user research into the needs of staff and students working with course materials online. The Learn Foundations project has proved fortuitous in that it has helped schools move their teaching online and prepare for hybrid teaching in the wake of the coronavirus outbreak.

Linknote — — 106 words

Media — Society

Groundbreaking report reveals racial bias in English football commentary — Sachin Nakrani — The Guardian

Footballers' legs of different colours

A very interesting-sounding study has analysed English language football commentary from seven broadcasters and 80 live matches.

RunRepeat ratio-adjusted its numbers to account for the fact there were 1,361 comments about lighter-skinned players and 713 about darker-skinned players and found the former group more widely praised for intelligence (62.60%), hard work (60.40%) and quality (62.79%). Commentators are also 6.59 times more likely to talk about the power of a player if he has darker skin and 3.38 times more likely to reference his pace.

The study also found that 63.33% of criticism from commentators in regards to the intelligence of a player is aimed at those with darker skin, while the figure for quality is 67.57%.

Linknote — — 188 words

Media — Technology

Microsoft’s robot editor confuses mixed-race Little Mix singers — Jim Waterson — The Guardian

Jade Thirlwall and Leigh-Anne Pinnock from Little Mix

How about this for dystopia? MSN have replaced human news editors with a robot powered by Microsoft artificial intelligence technology. The problem is, it has already begun making racist decisions.

And then, in case you thought the story wasn’t already absurd enough, this:

In advance of the publication of this article, staff at MSN were told to expect a negative article in the Guardian about alleged racist bias in the artificial intelligence software that will soon take their jobs.

Because they are unable to stop the new robot editor selecting stories from external news sites such as the Guardian, the remaining human staff have been told to stay alert and delete a version of this article if the robot decides it is of interest and automatically publishes it on MSN.com. They have also been warned that even if they delete it, the robot editor may overrule them and attempt to publish it again.

Then the article ends on a delicious snippet — that Microsoft itself is concerned about the reputational damage this scheme will cause to its AI technology.

I’m immediately reminded of Microsoft’s disastrous Tay experiment.

Linknote — — 180 words

Society

Academia uneasy with lack of diversity on ‘dude walls’ of honour — Nell Greenfieldboyce — NPR

A 'dude wall' featuring several portraits of white old men

On the campaign to remove — or make less prominent — walls of portraits of old white men from academia’s past.

“It just sends the message, every day when you walk by it, that science consists of old white men,” says [neuroscientist Leslie] Vosshall. “I think every institution needs to go out into the hallway and ask, ‘What kind of message are we sending with these oil portraits and dusty old photographs?'”

While defenders of dude walls warn of erasing history, the counterpoint is powerful:

…some argue that the old portraits themselves have erased history, by glorifying white men who hold power while ignoring the contributions to science and medicine made by women and people of colour.

Celebrations of individuals in this way always make me wary. It seems to be particularly common in higher education, where awards and buildings are routinely named after white males.

But very few breakthroughs are truly the work of a single individual. The people honoured in this way are likely the people most adept at taking all the credit for other people’s work.

Linknote — — 71 words

Business — Technology

A man checking dials

Why data is never raw — Nick Barrowman, the New Atlantis

Why it’s wrong and perhaps even dangerous to expect “raw data” to be neutral and strictly factual.

How data are construed, recorded, and collected is the result of human decisions — decisions about what exactly to measure, when and where to do so, and by what methods. Inevitably, what gets measured and recorded has an impact on the conclusions that are drawn.

Linknote — — 114 words

Social science — User experience — User research

Keeping yourself out of the story: Controlling experimenter effects

How do you stop yourself, as a user researcher, biasing the results? An important topic for user researchers to consider. (It’s also an excellent excuse to re-tell the story about Clever Hans, the horse who everyone thought could count, until they realised he was simply reacting to subtle, unintentional cues from his trainer.)

I recently undertook some usability testing, where I was asking people to complete tasks that I didn’t know how to complete myself. This meant I was less likely to bias the participant. But it was a strange experience for me, and it made me less certain about how to conduct the test.