Welcome to Women in AI Research

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)