Mitchell Gordon @mitchellgordon Twitter profile

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Mitchell Gordon

@mitchellgordon

1 year

Beyond excited to join @MITEECS @MIT_CSAIL as an assistant professor, starting fall 2024! I’m so lucky to have had @msbernst @landay @tatsu_hashimoto @foil as my advisors and mentors at Stanford. I can’t thank you all enough!

James Landay

@landay

1 year

So proud of @mitchellgordon and his brilliant PhD defense talk on “Human-AI Interaction Under Societal Disagreement”. Great lead advising by @msbernst ! Excellent example of human-centered AI research for @StanfordHAI . On to his faculty position @MITEECS ! Congrats!

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Mitchell Gordon

@mitchellgordon

5 months

I’m recruiting PhD students and there are still a few days left to apply! If you’re excited about working at the intersection of HCI and AI, come join my new group @MITEECS . Please submit at by 12/15!

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Mitchell Gordon

@mitchellgordon

2 years

1/n What should ML models do when a dataset’s annotators — the people that models are trying to emulate — disagree? In today’s typical supervised learning pipeline, we model an aggregate pseudo-human, predicting the majority vote label while ignoring annotators who disagree.

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Mitchell Gordon

@mitchellgordon

2 years

hi, I’ll be at #CHI2022 ! So excited to present jury learning, and please say hi if you’re interested in talking about human-centered AI, re-thinking today’s ML pipeline, evaluation metrics, or anything else!

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Mitchell Gordon

@mitchellgordon

4 years

I’m so excited and honored to be included among Apple’s first class of PhD fellows in AI/ML — and grateful for the opportunity to work with incredible mentors like @foil .

Introducing Apple Scholars in AIML

Apple Scholars is a program created to recognize the contributions of emerging leaders in computer science and engineering at the graduate…

machinelearning.apple.com

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Mitchell Gordon

@mitchellgordon

4 years

We’re presenting “HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models” as an oral at #NeurIPS2019 at 4:50 today in West Exhibition Hall C + B3!

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Mitchell Gordon

@mitchellgordon

2 years

4/n We introduce jury learning, a new supervised learning architecture — a technical and normative approach — that models the individual voices in your training dataset...

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Mitchell Gordon

@mitchellgordon

2 years

3/n There’s not really a good answer, so we tried asking a different question: whose voices is our model emulating? Datasets are ultimately made up of individual people. So when annotators disagree, instead of modeling some aggregate pseudo-human, let’s model individual people.

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Mitchell Gordon

@mitchellgordon

2 years

2/n Or, maybe we do a bit better, and have our AI predict a distribution (e.g. 40% of annotators would think A, 60% would think B). But if you’re a practitioner who’s then got to make a single decision (e.g., do I remove this comment or not), what do you DO with that information?

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Mitchell Gordon

@mitchellgordon

10 months

So excited for this workshop, come join us!

Michael Bernstein

@msbernst

10 months

Join us at the upcoming #UIST2023 workshop, Architecting Novel Interactions with Generative AI Models. Featuring a keynote by Will Wright (creator of The Sims and Simcity) and Lauren Elliot (Where In The World Is Carmen Sandiego)!

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Mitchell Gordon

@mitchellgordon

2 years

5/n enabling us to then design a system for interactively exploring, tuning, and shifting the behavior of the classifier, by explicitly choosing which annotators our classifier will emulate, in what proportion.

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Mitchell Gordon

@mitchellgordon

2 years

Insightful and important work from a great researcher and person!

Harmanpreet Kaur

@harmankkaur

2 years

Beyond excited to share our #FAccT2022 paper "Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory" Building on incredible recent work in this space, this paper is about *who* interpretability and XAI are intended for. 🧵

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Mitchell Gordon

@mitchellgordon

3 years

@ani_nenkova @msbernst @tatsu_hashimoto For instance, while the Kaggle competition’s most popular model achieved a precision of .527 and recall of .827 over standard aggregated labels, our approach adjusted that down to a precision of .514, and recall of .499.

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Mitchell Gordon

@mitchellgordon

5 years

@jeffbigham Feel free to just tweet me your feedback

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Mitchell Gordon

@mitchellgordon

3 years

@ani_nenkova @msbernst @tatsu_hashimoto Agreed that Jigsaw's class imbalance makes AUC a pretty bad metric for that task, even ignoring the issue of disagreement. Though I'd suggest that precision and recall still aren't doing enough to encode the task's subjectivity.

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Mitchell Gordon

@mitchellgordon

2 years

@SkotBotCambo @JessicaHullman Thanks @JessicaHullman ! Just checked out @SkotBotCambo 's paper and definitely agreed.

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Mitchell Gordon

@mitchellgordon

2 years