AliOmrani

Computer Science Ph.D. Candidate @USC | Ex-Intern @Snap | NLP and Computational Social Science | Interested in Ethics and Fairness of AI

[AliOmrani @Ali0mrani]

Excited to present our work, “Social-Group-Agnostic Bias Mitigation via the Stereotype Content Model”, at #ACL2023! Our bias mitigation approach goes beyond the norm by incorporating social psychological theories of human stereotyping. 📷📷https://t.co/9EHxzBN9mN

[AliOmrani @Ali0mrani]

For more details, check out our paper at:
📄 Paper: https://t.co/9EHxzBN9mN

You can find the code for our experiments on GitHub: 💻 Code: https://t.co/8bNzbjT252

Drop by our poster in Virtual Poster Session 3 at #ACL2023!

[AliOmrani @Ali0mrani]

🚨Thrilled to present our work published at ACL 2023 main conference! 🚨
We introduce a novel approach driven by bridging the gap between bias mitigation efforts in NLP and social psychological theories of human stereotyping! 🔍

https://t.co/9EHxzBN9mN

[Morteza Dehghani @MortezDehghani]

Leveraging the Stereotype Content Model from social psychology, we introduce a social-group-agnostic debiasing approach for word embeddings and LLMs. Achieving comparable performance on bias benchmarks, it offers both theoretical and practical advantages: https://t.co/4ph2pWPBOP

[AliOmrani @Ali0mrani]

Our results again show that our theory-driven approach can simultaneously mitigate biases for multiple social groups. Interestingly, our "debiased" language models perform on par with their pretrained (biased) counterparts on the GLUE benchmark!

[AliOmrani @Ali0mrani]

This work wouldn't have been possible without my awesome collaborators and mentors
@AlirezaZiabari , Charles Yu, @prenijee, @berendennedy, @MohammadAtari90, @elgreco_winter, @MortezDehghani

[AliOmrani @Ali0mrani]

Excited to present our work, “Social-Group-Agnostic Bias Mitigation via the Stereotype Content Model”, at #ACL2023! Our bias mitigation approach goes beyond the norm by incorporating social psychological theories of human stereotyping. 📷📷https://t.co/9EHxzBN9mN

[AliOmrani @Ali0mrani]

Drop by our poster in Gathertown!🏙️
Virtual Session 3, Room 1, P2703

[Dr. Tessa Charlesworth @TesCharlesworth]

At the @sfiscience Santa Fe institute we had a dream come true (two years in the making): cross disciplinary boundaries to study “language as a window into mind and society”. Co-organized with @banaji. I’m still buzzing from all I learned! A 🧵…

[AliOmrani @Ali0mrani]

@jieyuzhao11 @CSatUSC @nlp_usc Congratulations, Jieyu! 🎉✌🏽

[Mohammad Atari @MohammadAtari90]

🚨NEW PREPRINT🚨

Morality Beyond the WEIRD: How the Nomological Network of Morality Varies Across Cultures

We present the Moral Foundations Questionnaire-2 (MFQ-2).

https://t.co/EYb41YQ1fr

W/ @JonHaidt , J Graham, @SenaKoleva , S. Stevens, and @MortezDehghani

THREAD 1/20

[Morteza Dehghani @MortezDehghani]

After 3 editors, 6 reviewers & 5 revisions, after our text analysis of Gab was removed by editors post-review process along with references to “conservatives”, and after the Peer Review file was censored, a whitewashed, butchered version of our paper was published @NatureComms

[Morteza Dehghani @MortezDehghani]

We show 1. Moral concerns are observable in language 2. This signal is differential: each moral domain maps onto a distinct linguistic signature 3.Exclusive moral lang is not a great predictor of individuals' moral concerns @berendennedy @MohammadAtari90 https://t.co/nh8kd6ov2C

[AliOmrani @Ali0mrani]

The first project that I helped with in my PhD is out in Cognition! 🍾

Here we show that moral concerns are observable in language and explore the linguistic traces of each moral foundation.

[Brendan Kennedy @berendennedy]

🚨🚨🚨 New paper out in Cognition! Thanks to my wonderful coauthors and my advisor @MortezDehghani

In this descriptive/exploratory work, we found that moral concerns are *differentially* observable in language: each moral domain maps onto a distinct linguistic signature. 🧵

[Bill Yuchen Lin @billyuchenlin]

Excited to share our commonsense NLG dataset, CommonGen (->Findings of #emnlp2020), targeting generative #CommonSenseAI. Huge thanks to my wonderful co-authors: @wangchunshu @peizNLP @xiangrenNLP @nlp_usc + Chandra & @YejinChoinka @allen_ai.-->Link https://t.co/WzUMLbTo3A [1/3]

[USC NLP @nlp_usc]

USC Computer Science joins many other CS departments in an effort to waive GRE for 2021 graduate program applicants. If you’re interested in NLP, definitely apply to @CSatUSC PhD program and mention names of our NLP faculty @_jessethomason_ @robinomial @jonathanmay @xiangrenNLP !