“I’m just an engineer” inflicts foreseeable harm. This intentional ignorance is your value-laden choice.

Are you a well-known data visualization person who cares about disability access? Want me to do a live audit of your public-facing work as part of my workshop? Comment a link below. Bonus for news/journalism pieces! We will be looking how accessible pieces "in the wild" are.

Re-reading this bit right now in Su Lin Blodgett's awesome PhD thesis. Seems relevant

I'm looking for funded opportunities for interpretability + #NLProc research for summer 2021 (internships + other). If you know of anything, please pass along!

"a big part of the problem has to do with labeling... it's this cultural aspect that is giving this problem a very tricky dimension" "i've learned... it's very important to consider these cultural aspects of language before considering that the model is performing well"

In related news, the call for CRAFT workshops at ACM FAccT 2021 is live! We're looking for workshops that creatively engage with and address critiques of the FAccT field. Please share far and wide. facctconference.org/2021/cfw.html

Submit a workshop to FAccT!

We need to upgrade our dataset culture in ML. If we ever hope to make our models work, data needs to be more than an afterthought - its the engine driving the field & we need to treat it as such! Inspired by @timnitGebru (@dair-community.social/bsky.social) & others who continue asking questions on ML's data use.

Continually grateful to MMitchell and @timnitGebru (@dair-community.social/bsky.social) for creating a team that supported and fostered this kind of work. Also huge credit to them both for their seminal research in this area, which has paved the way for this work.

Fairness research in ML remains largely rooted in Western concerns: the injustices, datasets, measurement scales, & legal tenets used. These infrastructures, values, & legal systems cannot be naively generalized to non-Western countries -- A arxiv.org/abs/2012.03659

I need a name for that threshold when research findings about a category of people does more harm than good, when evidence becomes stereotype that hurts the exceptions. Favored research methods elide human diversity & complexity. #research #inclusion #DataScience

so, while the technical work of algorithmic transparency is important, it should be part of a broader perspective on how these systems are engineered, produced, and used. having a nice technical model of a dog's vision system won't alone tell me if it will bite me. (6/7)

Excited to speak today ACM FAccT 12:00-12:45 UTC on Fairness Metrics & Non-Discrimination Law: Can Fairness be Legally Automated? 2021.facctconference.org/agenda talk will also be recorded Oxford Internet Institute Oxford Comp Sci Berkman Klein Center for Internet & Society The Alan Turing Institute OxfordSocialSciences CyberSecurity Oxford Faculty of Law

What are the responsibilities of researching AI in the Global South? Many common to all fieldwork: How can research be productive not extractive? Awesome #FAccT2021 tutorial "AI on the Ground" with incredible scholars Vidushi Marda Radhika Radhakrishnan @tetisheri + Ranjit Singh!

I'm busy running this thing, but looks like we have some useful live-tweeting of our tutorial on teaching ethics in the tech classroom! 😍 #FAccT2021

A year or so ago while working on #datacards, I told Andrew Zaldivar "transparent documentation seems like an opaque deal. We need to make it more human." Today, we are stoked to open-source 1/2 of the #datacardsplaybook & present it at #facct2021 #facct21 Pair-code.github.io/datacardsplayb…

My ACM FAccT Diversity in Artificial Intelligence talk Fairness Metrics & Non-Discrimination Law: Can Fairness be Legally Automated? is online ft my paper Bias Preservation tinyurl.com/5fefced4 & Why Fairness Cannot Be Automated tinyurl.com/jytrxfmc Oxford Internet Institute The Alan Turing Institute Oxford Comp Sci

This is absolutely brilliant work! ❤️ imho it's up there with some of the best work on CS education👇 Epistemological Pluralism Sherry Turkle & Papert 1990 Stuck in The Shallow End Joanna Goode & pals 2010 You Can't Sit With Us Deb Raji Morgan Klaus Scheuerman, PhD (he/him) Razvan Amironesei 2021 💻❤️😎

Let's goooo!!! The second of two papers on AI education is coming up in a bit. As an AI educator focused on inclusion and co-generative pedagogy, I'm *really* excited for this talk on exclusionary pedagogy. Will tweet some take-aways in this thread: