About

Malihe Alikhani is an assistant professor in the Khoury College of Computer Sciences at Northeastern University, based in Boston. Her research interests include AI ethics, artificial intelligence, machine learning, and natural language processing. She believes in the potential of language technologies and AI to support critical education, health, and social justice efforts, while also studying how these technologies can become biased. Her vision is to design inclusive and equitable language technologies that communicate effectively with diverse populations, integrating insights from cognitive science, social sciences, and machine learning to capture diversity of interpretation and benefit underserved communities. Alikhani practices in the classroom what she advocates in her research, aiming to prepare a diverse generation of students to deploy the transformative power of AI. She has prior experience as a professor at the University of Pittsburgh, where she received multiple best paper honors. Her work has been supported by organizations such as DARPA, NIH, Google, and Amazon. She teaches courses on AI ethics and inclusive natural language processing, co-chairs the ethics team for the Association for Computational Linguistics’ rolling review, and is a member of Northeastern’s Natural Language Processing Research Group. Her research contributions include studying biases in sign language understanding models, evaluating theories of mind in conversational AI, and developing datasets and models for human online shopping behavior simulation, among others.

Research topics

• Computer Science
• Artificial Intelligence
• Natural Language Processing
• Information Retrieval
• Philosophy
• Data Mining
• Linguistics
• Geography
• Mathematics
• Cartography
• Programming language
• Chromatography
• Chemistry

Selected publications

• How Pragmatics Shape Articulation: A Computational Case Study in STEM ASL Discourse

  2026-04-30

  articleSenior author
  PublisherDOI

• BASIL: Bayesian Assessment of Sycophancy in LLMs

  arXiv (Cornell University) · 2025-08-23

  preprintOpen accessSenior author

  Sycophancy (overly agreeable or flattering behavior) poses a fundamental challenge for human-AI collaboration, particularly in high-stakes decision-making domains such as health, law, and education. A central difficulty in studying sycophancy in large language models (LLMs) is disentangling sycophantic belief shifts from rational changes in behavior driven by new evidence or user-provided information. Existing approaches either measure descriptive behavior changes or apply normative evaluations that rely on objective ground truth, limiting their applicability to subjective or uncertain tasks. We introduce a Bayesian probabilistic framework, grounded in behavioral economics and rational decision theory, that explicitly separates sycophancy from rational belief updating. Within this framework, we achieve three objectives: (i) a descriptive metric that measures sycophancy while controlling for rational responses to evidence; (ii) a normative metric that quantifies how sycophancy leads models astray from Bayesian-consistent belief updating; and (iii) the ability to apply both metrics in settings without ground-truth labels. Applying our framework across multiple LLMs and three uncertainty-driven tasks, we find robust evidence of sycophantic belief shifts and show that their impact on rationality depends on whether models systematically over- or under-update their beliefs. Finally, we demonstrate that a post-hoc calibration method and two fine-tuning strategies (SFT and DPO) substantially reduce Bayesian inconsistency, with particularly strong improvements under explicit sycophancy prompting.

  PublisherOA PDFDOI

• Coherence-Driven Multimodal Safety Dialogue with Active Learning for Embodied Agents

  2025-05-28 · 1 citations

  articleSenior author

  When assisting people in daily tasks, robots need to accurately interpret visual cues and respond effectively in diverse safety-critical situations, such as sharp objects on the floor. In this context, we present M-CoDAL, a multimodal-dialogue system specifically designed for embodied agents to better understand and communicate in safety-critical situations. The system leverages discourse coherence relations to enhance its contextual understanding and communication abilities. To train this system, we introduce a novel clustering-based active learning mechanism that utilizes an external Large Language Model (LLM) to identify informative instances. Our approach is evaluated using a newly created multimodal dataset comprising 1K safety violations extracted from 2K Reddit images. These violations are annotated using a Large Multimodal Model (LMM) and verified by human annotators. Results with this dataset demonstrate that our approach improves resolution of safety situations, user sentiment, as well as safety of the conversation. Next, we deploy our dialogue system on a Hello Robot Stretch robot and conduct a within-subject user study with real-world participants. In the study, participants role-play two safety scenarios with different levels of severity with the robot and receive interventions from our model and a baseline system powered by OpenAI's ChatGPT. The study results corroborate and extend the findings from the automated evaluation, showing that our proposed system is more persuasive in a real-world embodied agent setting.

  PublisherDOI

• OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation

  ArXiv.org · 2025-06-05

  preprintOpen access

  Can large language models (LLMs) accurately simulate the next web action of a specific user? While LLMs have shown promising capabilities in generating ``believable'' human behaviors, evaluating their ability to mimic real user behaviors remains an open challenge, largely due to the lack of high-quality, publicly available datasets that capture both the observable actions and the internal reasoning of an actual human user. To address this gap, we introduce OPERA, a novel dataset of Observation, Persona, Rationale, and Action collected from real human participants during online shopping sessions. OPERA is the first public dataset that comprehensively captures: user personas, browser observations, fine-grained web actions, and self-reported just-in-time rationales. We developed both an online questionnaire and a custom browser plugin to gather this dataset with high fidelity. Using OPERA, we establish the first benchmark to evaluate how well current LLMs can predict a specific user's next action and rationale with a given persona and history. This dataset lays the groundwork for future research into LLM agents that aim to act as personalized digital twins for human.

  PublisherOA PDFDOI

• SiLVERScore: Semantically-Aware Embeddings for Sign Language Generation Evaluation

  International conference Recent advances in natural language processing · 2025-01-01

  articleOpen accessSenior author
  PublisherDOI

• SignAlignLM: Integrating Multimodal Sign Language Processing into Large Language Models

  2025-01-01

  articleOpen accessSenior author

  Deaf and Hard-of-Hearing (DHH) users increasingly utilize Large Language Models (LLMs), yet face significant challenges due to these models' limited understanding of sign language grammar, multimodal sign inputs, and Deaf cultural contexts.Further, current approaches that try to address these limitations, frequently reduce sign language processing (SLP) to traditional translation tasks, neglecting the multimodal and linguistic complexity inherent in signed languages.In this paper, we present an empirical investigation informed by learning theory into natively integrating sign language support within LLMs, directly addressing the documented needs of DHH users.We introduce the first text-based and multimodal LLMs capable of sign language processing called SignAlignLM, and propose new prompting and fine-tuning strategies incorporating sign linguistic rules and conventions.We show that LLMs can be generalized interfaces for both spoken and signed languages if trained with a multitasking paradigm.Our code and model checkpoints are open-source 1 .

  PublisherOA PDFDOI

• Accounting for Sycophancy in Language Model Uncertainty Estimation

  2025-01-01 · 2 citations

  articleOpen accessSenior author

  Effective human-machine collaboration requires machine learning models to externalize uncertainty, so users can reflect and intervene when necessary.For language models, these representations of uncertainty may be impacted by sycophancy bias: proclivity to agree with users, even if they are wrong.For instance, models may be over-confident in (incorrect) problem solutions suggested by a user.We study the relationship between sycophancy and uncertainty estimation for the first time.We propose a generalization of the definition of sycophancy bias to measure downstream impacts on uncertainty estimation, and also propose a new algorithm (SyRoUP) to account for sycophancy in the uncertainty estimation process.Unlike previous works, we study a broad array of user behaviors, varying both correctness and confidence of user suggestions to see how model answers (and their certainty) change.Our experiments across conversation forecasting and question-answering tasks show that user confidence plays a critical role in modulating the effects of sycophancy, and that Sy-RoUP can better predict these effects.From these results, we argue that externalizing both model and user uncertainty can help to mitigate the impacts of sycophancy bias.

  PublisherOA PDFDOI

• Human-centered explanation does not fit all: The interplay of sociotechnical, cognitive, and individual factors in the effect AI explanations in algorithmic decision-making

  ArXiv.org · 2025-02-17 · 1 citations

  preprintOpen access

  Recent XAI studies have investigated what constitutes a \textit{good} explanation in AI-assisted decision-making. Despite the widely accepted human-friendly properties of explanations, such as contrastive and selective, existing studies have yielded inconsistent findings. To address these gaps, our study focuses on the cognitive dimensions of explanation evaluation, by evaluating six explanations with different contrastive strategies and information selectivity and scrutinizing factors behind their valuation process. Our analysis results find that contrastive explanations are not the most preferable or understandable in general; Rather, different contrastive and selective explanations were appreciated to a different extent based on who they are, when, how, and what to explain -- with different level of cognitive load and engagement and sociotechnical contexts. Given these findings, we call for a nuanced view of explanation strategies, with implications for designing AI interfaces to accommodate individual and contextual differences in AI-assisted decision-making.

  PublisherOA PDFDOI

• Empowering multilingual voices

  2025-07-08

  book-chapter
  PublisherDOI

• Better Slow than Sorry: Introducing Positive Friction for Reliable Dialogue Systems

  ArXiv.org · 2025-01-28

  preprintOpen accessSenior author

  While theories of discourse and cognitive science have long recognized the value of unhurried pacing, recent dialogue research tends to minimize friction in conversational systems. Yet, frictionless dialogue risks fostering uncritical reliance on AI outputs, which can obscure implicit assumptions and lead to unintended consequences. To meet this challenge, we propose integrating positive friction into conversational AI, which promotes user reflection on goals, critical thinking on system response, and subsequent re-conditioning of AI systems. We hypothesize systems can improve goal alignment, modeling of user mental states, and task success by deliberately slowing down conversations in strategic moments to ask questions, reveal assumptions, or pause. We present an ontology of positive friction and collect expert human annotations on multi-domain and embodied goal-oriented corpora. Experiments on these corpora, along with simulated interactions using state-of-the-art systems, suggest incorporating friction not only fosters accountable decision-making, but also enhances machine understanding of user beliefs and goals, and increases task success rates.

  PublisherOA PDFDOI

Frequent coauthors

• Matthew Stone

  49 shared

• Anthony Sicilia

  28 shared

• Baber Khalid

  Rutgers, The State University of New Jersey

  25 shared

• Katherine Atwell

  21 shared

• Sabit Hassan

  17 shared

• Mert İnan

  15 shared

• Thomas Kober

  13 shared

• Mark Steedman

  13 shared

Labs

• Khoury College of Computer SciencesPI

Awards & honors

• Three years as a professor at the University of Pittsburgh d…