Krithika Ramesh

Hey! I’m Krithika, a first year Ph.D. student in Computer Science at the Johns Hopkins Centre for Language and Speech Processing, advised by Dr. Anjalie Field and Dr. Mark Dredze. My research interests lie in problems related to fairness, interpretability and privacy in language models.

I’ve previously worked as a research intern at Microsoft Research India under the guidance of Dr. Sunayana Sitaram where I worked primarily on fairness in language models. Prior to this, I’d interned at the Indian School of Business, where I was mentored by Dr. Sumeet Kumar and Dr. Ashique KhudaBukhsh and worked on problems related to the potential harm caused by AI systems and tools.

During my undergraduate degree, I co-founded the Research Society MIT, an organization dedicated to guiding undergraduate students from a multitude of different fields in research, to foster and make the research culture more habitable for students. We’re always looking for potential collaborations and would be grateful for external assistance, so if you would like to assist the organization in any way, you can reach out to them at their e-mail. A list of publications and projects underway is available at their website for your reference.

If you’re still reading this, you probably (maybe, hopefully) also want to know a little about me as a person. I run, I read (to some extent), I love video games (though I don’t play them as much anymore), specifically RPGs like Divinity Original Sin: II, Mass Effect and Dragon Age. I’ve even actually recently begun to learn to play the keyboard so that I can play the music from these games! Some of the podcasts that I think are fantastic are Unexplainable and Flash Forward (does this page sound more like an advert at this point?).

I’m always open to discussion, so if you have any queries related to anything, or if there’s any way I could help you, feel free to drop me a mail or contact me via Twitter!

news

May 2, 2023	Our paper A Comparative Study of Model Compression Techniques on Fairness in Language Models was accepted to ACL 2023!
Apr 15, 2023	I’ll be starting my Ph.D. at Johns Hopkins CLSP this fall!
Jan 21, 2023	Our paper Fairness in Language Models Beyond English: Gaps and Challenges was accepted to the Findings of EACL 2023!

selected publications

Fairness in Language Models Beyond English: Gaps and Challenges

Krithika Ramesh, Sunayana Sitaram, and Monojit Choudhury

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (Findings), May 2023

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With language models becoming increasingly ubiquitous, it has become essential to address their inequitable treatment of diverse demographic groups and factors. Most research on evaluating and mitigating fairness harms has been concentrated on English, while multilingual models and non-English languages have received comparatively little attention. This paper presents a survey of fairness in multilingual and non-English contexts, highlighting the shortcomings of current research and the difficulties faced by methods designed for English. We contend that the multitude of diverse cultures and languages across the world makes it infeasible to achieve comprehensive coverage in terms of constructing fairness datasets. Thus, the measurement and mitigation of biases must evolve beyond the current dataset-driven practices that are narrowly focused on specific dimensions and types of biases and, therefore, impossible to scale across languages and cultures.
Revisiting Queer Minorities in Lexicons

Krithika Ramesh, Sumeet Kumar, and Ashiqur Khudabukhsh

In Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH), Jul 2022

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Lexicons play an important role in content moderation often being the first line of defense. However, little or no literature exists in analyzing the representation of queer-related words in them. In this paper, we consider twelve well-known lexicons containing inappropriate words and analyze how gender and sexual minorities are represented in these lexicons. Our analyses reveal that several of these lexicons barely make any distinction between pejorative and non-pejorative queer-related words. We express concern that such unfettered usage of non-pejorative queer-related words may impact queer presence in mainstream discourse. Our analyses further reveal that the lexicons have poor overlap in queer-related words. We finally present a quantifiable measure of consistency and show that several of these lexicons are not consistent in how they include (or omit) queer-related words.