Developer call May 7, 2020¶
Scribe: Roman Lutz
Microsoft & Fairlearn:
goal is to be an open source, community driven project
sociotechnical framing is central to the project, careful and precise wording matter
currently many core members are from Microsoft, but the project is an independent open source project
more focus on governance when the project matures
The Azure ML team will try to make Fairlearn easily usable in Azure, but Fairlearn is a standalone and independent project. Any functionality that can exist in the Fairlearn repository should live there. The Azure ML goals do not affect Fairlearn project decisions, just like with any other open source projects.
regular occurrence of developer call (2nd Thursday each month)
new newsletter through python.org, used to keep community in the loop about major news/updates
university collaborations with UMass Amherst and Stanford are currently ongoing
documentation/website updates: overall proposal currently in PR, landing page designed, to be added mid-May
The rest of the call was meant to be about the UX roadmap and potential collaboration with scikit-fairness. We knew ahead of time that this was ambitious, especially with lots of new people on the call. A lot of good points about the general direction of the project were made, which I’ll try to summarize below:
So far Fairlearn has focused on group fairness as opposed to individual or counterfactual fairness. The covered tasks are classification & regression only. Other settings are currently missing, e.g., partial observability (example: we don’t have data on those that didn’t get an opportunity such as getting the chance to pay back a loan).
Guiding question: how to cover the sociotechnical frame properly? [summary of points made by various participants]
The sociotechnical nature of fairness needs to be at the center of the project. User experience starts with broadly accessible educational material to encourage people to think bigger, not necessarily visualizations (people first, not technology first). Fairlearn and similar toolkits will not ‘solve’ fairness because fairness is fundamentally sociotechnical. However, Fairlearn may have a role to play in a slice of the things that need to be done. This will require certain guardrails and lots of education. The project itself will need to go beyond just the code through community, outreach, and education. Accessibility serves as a source of inspiration after having faced some of the same obstacles including changing the way people talk about the topic.
UX-specific points made throughout the conversation:
A lot of things depend on the specific context. To create domain-specific UX flows we’ll need to work with partners in those domains.
For data scientists the job doesn’t end with metrics. They need to interpret disparities, so there’s potential for interactions with model interpretability to “debug” issues (e.g. InterpretML)
We need to provide a process for how to iterate on new UX flows as well as domain or case studies. To understand whether specific parts of the UX are useful to data scientists we can add them and see how people use them, e.g. through user studies. To some extent we’ve followed a similar approach with our example notebooks. They are already ahead of the dashboard in terms of context-specific visualizations (done with matplotlib). Those that resonate with users could be added to the dashboard.
For both major topics (UX, scikit-fairness) we’ll have to start follow-up discussions.