Maarten Sap

I am a Postdoc/Young Investigator at the Allen Institute for AI (AI2), working on project Mosaic, and will be starting as an assistant professor at CMU's LTI department. My research focuses on endowing NLP systems with social intelligence and social commonsense, and understanding social inequality and bias in language.

I received my PhD from the University of Washington where I was advised by Noah Smith and Yejin Choi, and have interned at AI2 working on social commonsense reasoning, and at Microsoft Research working on deep learning models for understanding human cognition.
[bio for talks]

December 2021 updateπŸ§‘β€πŸŽ“:I will likely be taking students this coming PhD application cycle. If you're interested in working with me on social commonsense, social biases in language, or ethics in AI, please apply to CMU's LTI.

July 2021 updateπŸ‘¨πŸΌβ€πŸŽ“: I successfully defended my PhD thesis titled Positive AI with Social Commonsense Models (read the thesis here, or watch the recording here). Thanks to my advisors, committee, and everyone who attended!

May 2021 updateπŸ₯³: I will be joining CMU's LTI department as an assistant professorπŸ‘¨πŸΌβ€πŸ«in Fall 2022. If you wish to work with me, see the "contact" page. Before starting there, I will be a postdoc at AI2 on project Mosaic πŸ‘¨πŸΌβ€πŸ”¬ starting Fall 2021.


Overarching Research Themes

Detecting and Mitigating Social Biases in Language

Language can perpetuate social biases and toxicity against oppressed or marginalized groups. I want to investigate new ways of representing and detecting such harmful content in text (e.g., Social Bias Frames) or in conversations (e.g., with ToxiChat). Additionally, I want to harness NLP systems to combat stereotypical or harmful statements in language, through controllable text generation (e.g., with DExperts) or controllable text debiasing (e.g., with PowerTransformer).

In the future, I want to make this technology more context-aware and human-centric, e.g., by incorporating power differentials between speaker and listener, and studying human-in-the-loop methods for toxicity detection or text debiasing.

Commonsense Reasoning for Socially Aware NLP

Through theory-of-mind, Humans are trivially able to reason about other people's intents and reactions to everyday situations. I am interested in studying how AI systems can do this type of social commonsense reasoning. For example, this requires giving models knowledge of social commensense (e.g., with Event2Mind or ATOMIC, and methods like CoMET) or social acceptibility (Social Chemistry). Additionally, this requires creating benchmarks for measuring models' social commonsense abilities (e.g., with Social IQa, or Story Commonsense).

In the future, I want to keep investigating this elusive goal of machine social commonsense. Additionally, I want to explore positive applications of this research, e.g., for therapeutic setting or for helping people with cognitive disabilities.

Analyzing the Ethics and Transparency of AI models

AI and NLP systems unfortunately encode social biases and stereotypes. I'm passionate about analyzing and diagnosing the potential negative societal impacts of these systems. For example, I've uncovered severe racial bias in hate speech detection datasets and models, and subsequently analyzed whether robustness methods for NLP can mitigate them, as well as understanding the psychological attitudes that cause over- and under-detection of content as toxic. Additionally, I've scrutinized recent pretrained language models and their training data with respect to biases, toxicity, and fake news (e.g., measuring GPT-2 and GPT-3's neural toxic degeneration, and documenting the English C4 Webtext Crawl).

In the future, I plan to keep diagnosing and mitigating the ethical, fairness, and representation issues in AI systems, especially from a human-centric perspective of end-users and other stakeholders.