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In this conversation, Dr. Hila Gonen (Assistant Professor at the University of British Columbia) joins us to explore the deep insights into how large language models (LLMs) leak semantic information, behave across languages, and how researchers can uncover their root causes. Dr. Gonen shares her journey in interpreting AI systems, addressing biases, and controlling model outputs for safer, fairer applications.In this episode:The influence of prompt elements, like colour, on model predictionsHow semantic leakage impacts model outputs unintentionallyThe role of multilinguality and modality in model safety and behaviourInterventional vs. observational approaches to understanding modelsChallenges in controlling and aligning AI behavior across languages and domainsFuture directions in model interpretability, safety, and causal analysisKey Topics:Color and semantic influence on language model completionsThe concept of semantic leakage and examples from real promptsDifferences between bias, hallucination, and leakage failuresUnintended behaviours discovered through experimentationThe importance of model interpretability and transparencyRoots of behaviour: training data and internal representationsInterventional analysis as a causal tool in NLP researchCross-lingual and cross-modal alignment in safety detectionChallenges in evaluating safety across languages and modalitiesStrategies for building robust controls against unseen attack typesThe future of AI research: combining performance with reliability and safetyEthical considerations: avoiding directions that hinder societal benefitsResources & Links:Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove ThemDoes Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language ModelsRewriting History: A Recipe for Interventional Analyses to Study Data Effects on Model BehaviorOMNIGUARD: An Efficient Approach for AI Safety Moderation Across Modalities and LanguagesConnect with Dr. Hila Gonen:LinkedInhttps://x.com/hila_gonenNote: This episode emphasizes practical and theoretical challenges in model interpretability, safety, bias detection, and causality—providing a comprehensive view suitable for researchers, practitioners, and AI enthusiasts interested in responsible AI development.🎧 Subscribe to stay updated on new episodes spotlighting brilliant women shaping the future of AI.⁠⁠WiAIR website⁠⁠Follow us at:⁠⁠LinkedIn⁠⁠⁠⁠Bluesky⁠⁠⁠⁠X (Twitter)

Are reasoning models actually reasoning — or just producing convincing stories?Our guest in this episode of #WiAIRpodcast is Letitia Parcalabescu, the creator of the @AICoffeeBreak youtube channel. Letitia joins Jekaterina Novikova for a deep dive into the topics of faithfulness, self-consistency, hallucinations, and the reliability illusion in LLMs and multimodal reasoning models.We discuss why chain-of-thought explanations may not reflect what the model actually did, why RAG does not automatically fix hallucinations, and how vision–language models often rely far more on text than images. We also explore new approaches for grounding and rejection — and why models struggle to say "I don't know."Instead of focusing only on benchmark scores, this conversation asks: What kind of evidence do we need to truly trust reasoning models?REFERENCES:On Measuring Faithfulness or Self-consistency of Natural Language ExplanationsDo Vision & Language Decoders use Images and Text equally? How Self-consistent are their Explanations?Bounding Hallucinations: Information-Theoretic Guarantees for RAG Systems via Merlin-Arthur ProtocolsAI Coffee Break with Letitiahttps://www.youtube.com/c/AICoffeeBreakhttps://x.com/AICoffeeBreak🎧 Subscribe to stay updated on new episodes spotlighting brilliant women shaping the future of AI.⁠WiAIR website⁠Follow us at:⁠LinkedIn⁠⁠Bluesky⁠⁠X (Twitter)

As language models become more capable, the hardest questions are no longer just about performance, but about safety, interpretation, and control.In this episode of Women in AI Research, we speak with Swabha Swayamdipta, Assistant Professor of Computer Science at the University of Southern California and co-Associate Director of the USC Center for AI and Society. Swabha’s research examines how the design and deployment of language models intersect with real-world risks — from how models behave in unexpected ways to how seemingly technical choices can have broader societal consequences.We talk about AI safety from multiple angles: what it means when hidden inputs to models can sometimes be inferred from their outputs, why personalization introduces new trade-offs around privacy and user agency, and how assumptions about model behavior can quietly shape downstream harms. Rather than focusing only on accuracy or benchmarks, the conversation asks what kinds of evidence we actually need to trust these systems in practice.REFERENCESBetter Language Model Inversion by Compactly Representing Next-Token DistributionsImproving Language Model Personas via Rationalization with Psychological ScaffoldsOATH-Frames: Characterizing Online Attitudes Towards Homelessness with LLM AssistantsUncovering Intervention Opportunities for Suicide Prevention with Language Model Assistants🎧 Subscribe to stay updated on new episodes spotlighting brilliant women shaping the future of AI.⁠WiAIR website⁠Follow us at:⁠LinkedIn⁠⁠Bluesky⁠⁠X (Twitter)

Do large language models actually understand meaning — or are we over-interpreting impressive behavior?In this episode, we speak with Maria Ryskina, CIFAR AI Safety Postdoctoral Fellow at the Vector Institute for AI, whose research bridges neuroscience, cognitive science, and artificial intelligence. Together, we unpack what the brain can (and cannot) teach us about modern AI systems — and why current evaluation paradigms may be missing something fundamental.We explore how language models can predict brain activity in regions linked to visual processing, what this reveals about cross-modal knowledge, and why scale alone may not resolve deeper conceptual gaps in AI. The conversation also tackles the growing importance of interpretability, especially as AI systems become more embedded in high-stakes, real-world contexts.Beyond technical questions, Maria shares why community matters in AI research, particularly for underrepresented groups — and how diversity directly shapes the kinds of scientific questions we ask and the systems we ultimately build.REFERENCESGender Shades: Intersectional Accuracy Disparities in Commercial Gender ClassificationStereotypes and Smut: The (Mis)representation of Non-cisgender Identities by Text-to-Image ModelsLanguage models align with brain regions that represent concepts across modalitiesElements of World Knowledge (EWoK): A Cognition-Inspired Framework for Evaluating Basic World Knowledge in Language ModelsPrompting is not a substitute for probability measurements in large language modelsAuxiliary task demands mask the capabilities of smaller language models🎧 Subscribe to stay updated on new episodes spotlighting brilliant women shaping the future of AI.WiAIR websiteFollow us at:LinkedInBlueskyX (Twitter)

AI doesn’t just process text — it takes in our cultures, reflects our hierarchies, and can make existing power structures even stronger. In this episode of Women in AI Research, Jekaterina Novikova and Malikeh Ehgaghi speak with Dr. Maria Antoniak (Assistant Professor at the University of Colorado Boulder) about inclusivity in AI, the dynamics of cultural representation, what trust in AI really means, why LLMs tend to homogenize research cultures, and what maternal healthcare reveals about the deepest ethical challenges in this field.REFERENCES:⁠Trust No Bot⁠⁠A Large-Scale Analysis of Public-Facing, Community-Built Chatbots on Character.AI⁠⁠LLMs as Rsearch Tools⁠⁠Culture is Not Trivia⁠⁠Research Borderlands⁠⁠NLP for Maternal Healthcare⁠⁠Data Feminism⁠⁠Epistemic Diversity and Knowledge Collapse in Large Language Models⁠⁠Empire of AI⁠🎧 Subscribe to stay updated on new episodes spotlighting brilliant women shaping the future of AI.⁠⁠⁠⁠WiAIR website⁠⁠⁠⁠Follow us at:⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠Bluesky⁠⁠⁠⁠⁠⁠⁠⁠X (Twitter)⁠

Is English just one of the languages you speak? If so, the AI tools you use might miss things that makes your voice multilingual.In this episode of Women in AI Research, Jekaterina Novikova speaks with Dr. Annie En-Shiun Lee about her work on multilingual and multicultural AI — from the widening language gap and the lack of benchmarks for underrepresented languages, to why domain-specific data matters more than just scaling up models. We talk about the limits of cross-lingual transfer, the risks of English-centric reasoning in AI, and the technical, ethical, and cultural challenges of building models that truly serve global communities.References:SIB-200: A simple, inclusive, and big evaluation dataset for topic classification in 200+ languages and dialectsURIEL+: Enhancing Linguistic Inclusion and Usability in a Typological and Multilingual Knowledge BasemR3: Multilingual Rubric-Agnostic Reward Reasoning ModelsProxyLM: Predicting language model performance on multilingual tasks via proxy modelsATAIGI: An AI-Powered Multimodal Learning App Leveraging Generative Models for Low-Resource Taiwanese HokkienEnhancing Taiwanese Hokkien Dual Translation by Exploring and Standardizing of Four Writing SystemsAlignFreeze: Navigating the Impact of Realignment on the Layers of Multilingual Models Across Diverse LanguagesIrokobench: A new benchmark for african languages in the age of large language models🎧 Subscribe to stay updated on new episodes spotlighting brilliant women shaping the future of AI.⁠⁠WiAIR website⁠⁠Follow us at:⁠⁠LinkedIn⁠⁠⁠⁠Bluesky⁠⁠⁠⁠X (Twitter)