[1] MLHOps: Machine Learning for Healthcare Operations. Faiza Khan Khattak, Vallijah Subasri,
Amrit Krishnan, Chloe Pou-Prom, Sedef Akinli-Kocak, Elham Dolatabadi, Deval Pandya, Laleh
Seyyed-Kalantari, Frank Rudzicz. IEEE Access. December 2024.

[2] Dialectic Preference Bias in Large Language Models. Muhammad Furquan Hassan, Faiza Khan
Khattak, Laleh Seyyed-Kalantari. Under submission.

[3] On The Role of Reasoning in the Identification of Subtle Stereotypes in Natural Language. JacobJunqi Tian, Omkar Dige, D. B. Emerson, Faiza Khan Khattak. Under submission.

[4] The Impact of Unstated Norms in Bias Analysis of Language Models Farnaz Kohankhaki, David
Emerson, Jacob Junqi Tian, Laleh Seyyed-Kalantari, Faiza Khan Khattak. Under submission to
Transaction of ACL (TACL).

[5] Red-Teaming for Inducing Bias in Large Language Models Chu-Fei Luo, Ahmad Ghawanmeh,
, Bharat Bhimshetty, Kashyap Coimbatore Murali, Murli Jadhav, Xiaodan Zhu, Faiza Khan
Khattak. Under submission.

[6] Can Machine Unlearning Reduce Social Bias in Language Models? Omkar Dige, Diljot Singh, Tsz
Fung Yau, Qixuan Zhang , Mohammad Borna Bolandraftar, Xiaodan Zhu, Faiza Khan Khattak,
Empirical Methods in Natural Language Processing (EMNLP) 2024, Industry track.

[7] BiasKG: Adversarial Knowledge Graphs to Induce Bias in Large Language Models. Chu-Fei Luo,
Ahmad Ghawanmeh, Xiaodan Zhu, Faiza Khan Khattak. TrustNLP Workshop at Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL
2024)

[8] Mitigating Social Biases in Language Models through Unlearning. Omkar Dige, Diljot Singh, Tsz
Fung Yau, Qixuan Zhang , Mohammad Borna Bolandraftar, Xiaodan Zhu, Faiza Khan Khattak,
TrustNLP Workshop at Annual Conference of the North American Chapter of the Association for
Computational Linguistics (NAACL 2024).

[9] Reevaluating Bias Detection in Language Models: The Role of Implicit Norms. Farnaz Kohankhaki, Jacob Junqi Tian, David Emerson, Laleh Seyyed-Kalantari, Faiza Khan Khattak.
TrustNLP Workshop at Annual Conference of the North American Chapter of the Association for
Computational Linguistics (NAACL 2024).

[10] Responsible Language Models. Faiza Khan Khattak, Lu Cheng, Sedef Akinli-Kocak, Mengnan
Du, Fengxiang He, Bo Li, Blessing Ogbuokiri, Shaina Raza, Laleh Seyed-Kalantari, Yihang Wang,
Xiaodan Zhu, Graham W. Taylor. AAAI Workshop Proposal, 2024.

[11] Using Chain-of-Thought Prompting for Interpretable Recognition of Social Bias. Jacob-Junqi
Tian, Omkar Dige, D Emerson, Faiza Khan Khattak. Socially Responsible Language Modelling
Research (SoLaR) Workshop, NeurIPS, 2023

[12] Efficient Evaluation of Bias in Large Language Models through Prompt Tuning. Jacob-Junqi Tian,
D Emerson, Deval Pandya, Laleh Seyyed-Kalantari, Faiza Khan Khattak Socially Responsible
Language Modelling Research (SoLaR) Workshop, NeurIPS, 2023

[13] Can Instruction Fine-Tuned Language Models Identify Social Bias through Prompting? Omkar
Dige, Jacob-Junqi Tian, David Emerson, Faiza Khan Khattak arXiv preprint arXiv:2307.10472.
2023.

[14] Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer
Service Support. Stephen Obadinma, Faiza Khan Khattak, Shirley Wang, Tania Sidhom, Elaine
Lau, Sean Robertson, Jingcheng Niu, Winnie Au, Alif Munim, Karthik Raja K Bhaskar, Bencheng
Wei, Iris Ren, Waqar Muhammad, Erin Li, Bukola Ishola, Michael Wang, Griffin Tanner, YuJia Shiah, Sean X Zhang, Kwesi P Apponsah, Kanishk Patel, Jaswinder Narain, Deval Pandya,
Xiaodan Zhu, Frank Rudzicz, Elham Dolatabadi. EMNLP Industry Track 2022.

[15] Systems and methods for extracting information from a dialogue. Faiza Khan Khattak, Frank
Rudzicz, Muhammad Mamdani, Noah Crampton, Serena Jeblee. US Patent App. 17/675,189.
2022

[16] An Experimental Evaluation of Transformer-based Language Models in the Biomedical Domain.
Paul Grouchi, Shobhit Jain, Michael Liu, Kuhan Wang, Max Tian, Nidhi Arora, Hillary Ngai,
Faiza Khan Khattak, Elham Dolatabadi, Sedef Akinli Kocak. Poster presentation at ACM CHIL
workshop 2021.

[17] Extracting relevant information from physician-patient dialogues for automated clinical note taking. Serena Jeblee, Faiza Khan Khattak, Noah Crampton, Muhammad Mamdani, Frank Rudzicz.
To appear in LOUHI: The Tenth International Workshop on Health Text Mining and Information
Analysis. 2019. Hong Kong.

[18] A Survey of Word Embeddings for Clinical Text. Faiza Khan Khattak, Serena Jeblee, Chlo´e
Pou-Prom, Muhammad Abdullah, Christopher Meany, Frank Rudzicz. Journal of Biomedical
Informatics. Elsevier.

[19] Extracting Pertinent Information from Doctor-Patient Conversation. Faiza Khan Khattak, Serena Jeblee, Noah Crampton, Muhammad Mamdani, Frank Rudzicz. MedInfo Conference 2019.
Lyon, France.

[20] Predicting ICU transfers using text messages between nurses and doctors. Faiza Khan Khattak,
Chlo´e Pou-Prom, Robert Wu, Frank Rudzicz. The 2nd Clinical Natural Language Processing
Workshop At NAACL 2019. Minneapolis, USA.

[21] Toward a Robust Crowd-labeling Framework using Expert Evaluation and Pairwise Comparison.
Faiza Khan Khattak, Ansaf Salleb-Aouissi. Arxiv

[22] Faiza Khan Khattak, “Toward a Robust and Universal Crowd Labeling Framework.” Ph.D.
Thesis. Columbia University, New York. 2017.

[23] Effective Crowd Labeling using Expert Evaluation. Faiza Khan Khattak. ACM Special Interest
Group on Artificial Intelligence, Career Network and Conference (SIGAI CNC.) 2016

[24] Toward a Robust and Universal Crowd-labeling Framework. Faiza Khan Khattak, IJCAI Doctoral
Consortium 2016.

[25] Accurate Crowd-labeling using Item Response Theory. Faiza Khan Khattak, Ansaf SallebAouissi, Anita Raja, Collective Intelligence Conference 2016.

[26] An Item Response Theory (IRT) Like Approach to Crowd-labeling. Faiza Khan Khattak, Ansaf
Salleb-Aouissi, Workshop for Women in Machine Learning (WiML 2015) held in conjunction with
Neural Information Processing Systems (NIPS), Montreal, Canada. 2015.

[27] Robust Crowd Labeling using Little Expertise.Faiza Khan Khattak, Ansaf Salleb-Aouissi, Sixteenth International Conference on Discovery Science (DS 2013). Pages: 94-109, Singapore 2013.

[28] Improving Crowd Labeling through Expert Evaluation. Faiza Khan Khattak, Ansaf SallebAouissi, Association for the Advancement of Artificial Intelligence (AAAI). Spring Symposium
2012: Wisdom of Crowd.

[29] Quality Control of Crowd Labeling through Expert Evaluation. Faiza Khan Khattak, Ansaf
Salleb-Aouissi, Neural Information Processing Systems (NIPS) workshop: Computational Social
Sciences and the Wisdom of Crowds 2011, Granada, Spain.

[30] Understanding Infantile Colic: A Natural Language Processing and Machine Learning Challenge.
Ansaf Salleb-Aouissi, Axinia Radeva, Boyi Xie, Rebecca Passonneau, Faiza Khan Khattak, Workshop for Women in Machine Learning (WiML 2011) held in conjunction with Neural Information
Processing Systems (NIPS), Granada, Spain. 2011.

[31] Accurate Multiple Labeling by Mixing Expert and Crowd Labels. Faiza Khan Khattak , Ansaf
Salleb-Aouissi, Workshop for Women in Machine Learning (WiML 2011) held in conjunction with
Neural Information Processing Systems (NIPS), Granada, Spain. 2011.

[32] Diving into a Large Corpus of Pediatric Notes. Ansaf Salleb-Aouissi, Axinia Radeva, Boyi Xie,
Faiza Khan Khattak, Rebecca Passonneau, Ashish Tomar, Hatim Diab, David Waltz, Mary
McCord, Harriet McGurk, Noemie Elhadad, International Conference on Machine Learning (ICML)
2011: Workshop on Learning from Unstructured Clinical Text.

[33] Pairwise Similarity Measures To Estimate Dataset Density. Tristan Naumann, Faiza Khan Khattak and Ansaf Salleb-Aouissi, ‘ 5th Annual Machine Learning Symposium at the New York
Academy of Sciences (NYAS) 2010, New York, October 2010.