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Sujan Dutta

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7 papers
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7

AAAI Conference 2026 System Paper

PAGER: Proactive Monitoring Agent for Enterprise AI Assistant

  • Sujan Dutta
  • Junior Francisco Garcia Ayala
  • Pranav Pujar
  • Sai Sree Harsha
  • Dan Luo
  • Nikhil Vasudeva
  • Bikas Saha
  • Pritom Baruah

We present a Proactive Monitoring Agent designed for large-scale customer data platforms, such as Adobe Experience Platform (AEP), to predict and prevent workflow disruptions before they impact business operations. Unlike existing reactive solutions that assist engineers only after failures occur, our agent anticipates potential failures across multiple workflow stages, explains its predictions in natural language, and interacts with customer support engineers through a conversational interface. The system integrates a machine learning-based Prediction Module, Knowledge Graph APIs for contextual data access, and a Query Processor that powers an interactive Q&A experience, enabling timely and actionable insights to minimize operational risks and maximize business continuity.

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AAAI Conference 2025 Conference Paper

ARTICLE: Annotator Reliability Through In-Context Learning

  • Sujan Dutta
  • Deepak Pandita
  • Tharindu Cyril Weerasooriya
  • Marcos Zampieri
  • Christopher M. Homan
  • Ashiqur R. KhudaBukhsh

Ensuring annotator quality in training and evaluation data is a key piece of machine learning in NLP. Tasks such as sentiment analysis and offensive speech detection are intrinsically subjective, creating a challenging scenario for traditional quality assessment approaches because it is hard to distinguish disagreement due to poor work from that due to differences of opinions between sincere annotators. With the goal of increasing diverse perspectives in annotation while ensuring consistency, we propose ARTICLE, an in-context learning (ICL) framework to estimate annotation quality through self-consistency. We evaluate this framework on two offensive speech datasets using multiple LLMs and compare its performance with traditional methods. Our findings indicate that ARTICLE can be used as a robust method for identifying reliable annotators, hence improving data quality.

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AAAI Conference 2025 Short Paper

ARTICLE: Annotator Reliability Through In-Context Learning (Student Abstract)

  • Sujan Dutta
  • Deepak Pandita
  • Tharindu Cyril Weerasooriya
  • Marcos Zampieri
  • Christopher M. Homan
  • Ashiqur R. KhudaBukhsh

Ensuring annotator quality in training and evaluation data is a key piece of machine learning in NLP. Tasks such as sentiment analysis and offensive speech detection are intrinsically subjective, creating a challenging scenario for traditional quality assessment approaches because it is hard to distinguish disagreement due to poor work from that due to differences of opinions between sincere annotators. With the goal of increasing diverse perspectives in annotation while ensuring consistency, we propose ARTICLE, an in-context learning (ICL) framework to estimate annotation quality through self-consistency. We evaluate this framework on two offensive speech datasets using multiple LLMs and compare its performance with traditional methods. Our findings indicate that ARTICLE can be used as a robust method for identifying reliable annotators, hence improving data quality.

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IJCAI Conference 2024 Conference Paper

Down the Toxicity Rabbit Hole: A Framework to Bias Audit Large Language Models with Key Emphasis on Racism, Antisemitism, and Misogyny

  • Arka Dutta
  • Adel Khorramrouz
  • Sujan Dutta
  • Ashiqur R. KhudaBukhsh

This paper makes three contributions. First, it presents a generalizable, novel framework dubbed toxicity rabbit hole that iteratively elicits toxic content from a wide suite of large language models. Spanning a set of 1, 266 identity groups, we first conduct a bias audit of PaLM 2 guardrails presenting key insights. Next, we report generalizability across several other models. Through the elicited toxic content, we present a broad analysis with a key emphasis on racism, antisemitism, misogyny, Islamophobia, homophobia, and transphobia. We release a massive dataset of machine-generated toxic content with a view toward safety for all. Finally, driven by concrete examples, we discuss potential ramifications.

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IJCAI Conference 2023 Conference Paper

Disentangling Societal Inequality from Model Biases: Gender Inequality in Divorce Court Proceedings

  • Sujan Dutta
  • Parth Srivastava
  • Vaishnavi Solunke
  • Swaprava Nath
  • Ashiqur R. KhudaBukhsh

Divorce is the legal dissolution of a marriage by a court. Since this is usually an unpleasant outcome of a marital union, each party may have reasons to call the decision to quit which is generally documented in detail in the court proceedings. Via a substantial corpus of 17, 306 court proceedings, this paper investigates gender inequality through the lens of divorce court proceedings. To our knowledge, this is the first-ever large-scale computational analysis of gender inequality in Indian divorce, a taboo-topic for ages. While emerging data sources (e. g. , public court records made available on the web) on sensitive societal issues hold promise in aiding social science research, biases present in cutting-edge natural language processing (NLP) methods may interfere with or affect such studies. A thorough analysis of potential gaps and limitations present in extant NLP resources is thus of paramount importance. In this paper, on the methodological side, we demonstrate that existing NLP resources required several non-trivial modifications to quantify societal inequalities. On the substantive side, we find that while a large number of court cases perhaps suggest changing norms in India where women are increasingly challenging patriarchy, AI-powered analyses of these court proceedings indicate striking gender inequality with women often subjected to domestic violence.

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IJCAI Conference 2023 Conference Paper

For Women, Life, Freedom: A Participatory AI-Based Social Web Analysis of a Watershed Moment in Iran's Gender Struggles

  • Adel Khorramrouz
  • Sujan Dutta
  • Ashiqur R. KhudaBukhsh

In this paper, we present a computational analysis of the Persian language Twitter discourse with the aim to estimate the shift in stance toward gender equality following the death of Mahsa Amini in police custody. We present an ensemble active learning pipeline to train a stance classifier. Our novelty lies in the involvement of Iranian women in an active role as annotators in building this AI system. Our annotators not only provide labels, but they also suggest valuable keywords for more meaningful corpus creation as well as provide short example documents for a guided sampling step. Our analyses indicate that Mahsa Amini's death triggered polarized Persian language discourse where both fractions of negative and positive tweets toward gender equality increased. The increase in positive tweets was slightly greater than the increase in negative tweets. We also observe that with respect to account creation time, between the state-aligned Twitter accounts and pro-protest Twitter accounts, pro-protest accounts are more similar to baseline Persian Twitter activity.

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IJCAI Conference 2022 Conference Paper

A Murder and Protests, the Capitol Riot, and the Chauvin Trial: Estimating Disparate News Media Stance

  • Sujan Dutta
  • Beibei Li
  • Daniel S. Nagin
  • Ashiqur R. KhudaBukhsh

In this paper, we analyze the responses of three major US cable news networks to three seminal policing events in the US spanning a thirteen month period--the murder of George Floyd by police officer Derek Chauvin, the Capitol riot, Chauvin's conviction, and his sentencing. We cast the problem of aggregate stance mining as a natural language inference task and construct an active learning pipeline for robust textual entailment prediction. Via a substantial corpus of 34, 710 news transcripts, our analyses reveal that the partisan divide in viewership of these three outlets reflects on the network's news coverage of these momentous events. In addition, we release a sentence-level, domain-specific text entailment data set on policing consisting of 2, 276 annotated instances.

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