Make Labour Visible
At Data Expo, Caroline Williams from WE Make Trouble presented seven principles for ensuring data and AI projects are built inclusively:
examine power
challenge power
rethink binaries and hierarchies
elevate emotion and embodiment
consider context
embrace pluralism
make labor visible.
These 7 principles are based on Data Feminism, the 2020 book by Catherine D'Ignazio and Lauren Klein, which offers seven principles for analysing power imbalances in data science.
Make Labour Visible
"Make Labour Visible" is the principle that struck me most.
"We're here at this data and AI event, talking about big questions and decisions. But do we know who makes our AI possible, far away, paid a fraction of the salary these technologies generate in the Global North?
And are the colleagues who work with you on data and AI given the same chance to be in rooms like this one? Is their work seen, and are they credited and rewarded for it?” said Caroline.
These questions might feel overwhelming at first, but I think they just invite us to start with a small step, one that is often forgotten: to inform ourselves.
Where to start?
With you, by being aware of which data and technologies you use.
Then look at the company behind them, its practices, and where its operations actually run, and by whom.
The next step?
Share what you learn with others.
Don't know where to start mapping your tech and data ecosystem, or how to act on what you find?
I can help you with that. Book a Free Discovery Call or choose your preferred way to connect here.