Let me just say, being a woke IT director is exhausting.
Not only do you end up writing theses about ‘diversity and digital leadership‘, you also find yourself employing OER service managers (to research and promote equitable distribution of resources), E-Safety Officers (to support students and staff who discover that the internet is not a safe space), and Data and Equality Officers (to ensure that your services and workplaces even know what they are doing).
You end up talking about digital accessibility and inclusion at every meeting and you keep your antennae poised to nip any potential carcrashes in the bud.
So much of what we do is actually just about how we communicate it.
This month I’ve suggested:
- That ‘Race Sub Group’ may be a difficult name for a good effort.
- That ‘Welcome Period’ sounds odd too.
- As do ‘Courses to help you with transitions’
- That Estates and Digital Infrastructure (EDI) is not the same as Equality, Diversity and Inclusion (EDI)
- That using a picture of Chinese students wearing masks might contribute to Sinophobia on campus
- That there’s not much evidence that ‘EDI training’ actually works.
- That some staff may want to attend more than one ‘identity network’ in the workplace.
- That we could add rainbows to Teams backgrounds instead of distributing rainbow lanyards to people’s homes.
- That even if you are wearing a mask you should still wear a lapel mike.
- That Teams, Zoom and Collaborate may tend to search for white faces on camera more easily than black ones.
- That those huge video files you are struggling to upload to Media Hopper are the same ones your students with low bandwidth will struggle to download.
- That as well as removing the name ‘David Hume Tower’ we should check the slavery credentials of George Brown after whom the square is named.
- That no EQIA was done on the decision to not fund an improved subtitling service. ART was offered several options but chose to accept the risk of putting the workload on to individual owners of their materials. The nature of these speech to text robots (and many other algorithms) is that they are structurally biased. The data sets on which they are trained are largely spoken corpora from business settings, in male voices and with US accents. So the burden of correction will fall disproportionately on women, people with accents and anyone teaching disciplines with words the robots do not already know.
- That students choosing to study online rather than come into class isn’t evidence that the online learning is excellent, only that it is more attractive than catching Covid.
- That working from home may infact be the best thing to happen to menospausal women as we now have choice and flexibiity and control over the temperature, number of cushions and our layers of clothing.