Women in AI Research (WiAIR)

Is Your AI Just Flattering You? Sycophancy, AI Policies, and More, with Dr. Malihe Alikhani

Only 19% of Americans say AI has actually improved their productivity - so why the gap between the hype and reality? In this episode of Women in AI Research, Dr. Malihe Alikhani (Northeastern University, Contextual AI Lab) unpacks the hidden failures in how we build and deploy AI: why sycophancy is really a collapse of alignment, why bigger models aren't better aligned, and why "thin" alignment breaks down in the real world. Key topics * The impact of moving across different AI contexts on system design * The role of language as performative and active in shaping reality * Interactive inference and uncertainty in AI systems * The importance of context in meaning and system design * AI policy, transparency, and societal impact * Sycophantic behavior in large language models * Measuring AI alignment: thin vs. thick * AI adoption across sectors and demographic groups * The role of policy in AI development and safety * Ethical considerations in AI research and deployment Resources & Links: * Breaking the AI Mirror: Sycophancy, productivity, and the future of collaboration [https://www.brookings.edu/articles/breaking-the-ai-mirror/] * Hype and harm: Why we must ask harder questions about AI and its alignment with human values [https://www.brookings.edu/articles/hype-and-harm-why-we-must-ask-harder-questions-about-ai-and-its-alignment-with-human-values/] * How are Americans using AI? Evidence from a nationwide survey [https://www.brookings.edu/articles/how-are-americans-using-ai-evidence-from-a-nationwide-survey/] Connect with Dr. Malihe Alikhani: * https://x.com/malihealikhani [https://x.com/malihealikhani] 🎧 Subscribe to stay updated on new episodes spotlighting brilliant women shaping the future of AI. Follow WiAIR at: * ⁠LinkedIn⁠ [https://www.linkedin.com/company/women-in-ai-research/ ] * ⁠Bluesky⁠ [https://bsky.app/profile/wiair.bsky.social] * ⁠X (Twitter)⁠ [https://x.com/WiAIR_podcast] * ⁠⁠WiAIR website⁠ [https://women-in-ai-research.github.io]

Can Language Alone Create Intelligence? Insights from Neuroscience and AI, with Dr. Anna Ivanova

Do large language models truly understand language—or are they sophisticated pattern matchers? In this conversation, Dr. Anna Ivanova (Asst. Prof. at Georgia Tech) explores one of the important questions in AI: the relationship between language, thought, and intelligence. Drawing from neuroscience, cognitive science, and AI research, Anna explains why language understanding is harder to define than most people realize, why reasoning and language are not the same thing, and what today's LLMs can and cannot tell us about human cognition. Key Topics: * Do LLMs understand language or merely generate convincing text? * The difference between formal and functional linguistic competence * What LLMs can learn from language alone—and what they cannot * Why human cognition and AI cognition may be fundamentally different * Theory of mind, reasoning, and common misconceptions about AI capabilities * How cognitive scientists evaluate the "thinking" abilities of LLMs * What neuroscience can teach AI researchers about interpretability * Why understanding AI requires studying both behavior and internal representations * The future of multimodal models and AI cognition Resources & Links: * What does it mean to understand language? [https://arxiv.org/abs/2511.19757] * Dissociating language and thought in large language models [https://static1.squarespace.com/static/64c800a2f333f04f50bf2020/t/66f46f57900cdf4b7060e3ec/1727295320856/Mahowald_Ivanova_et_al_2024_TiCS.pdf] * How to evaluate the cognitive abilities of LLMs [https://drive.google.com/file/d/1OPCTpkluqs8xY3Bs6_--18eXU9u06oEN/view] * How Do LLMs Use Their Depth? [https://arxiv.org/abs/2510.18871] * True Lens [https://github.com/AlignmentResearch/tuned-lens] Connect with Dr. Anna Ivanova: https://bsky.app/profile/neuranna.bsky.social [https://bsky.app/profile/neuranna.bsky.social] https://x.com/neuranna [https://x.com/neuranna] 🎧 Subscribe to stay updated on new episodes spotlighting brilliant women shaping the future of AI. Follow WiAIR at: * LinkedIn [https://www.linkedin.com/company/women-in-ai-research/ ] * Bluesky [https://bsky.app/profile/wiair.bsky.social] * X (Twitter) [https://x.com/WiAIR_podcast] * ⁠WiAIR website⁠ [https://women-in-ai-research.github.io]

100% Jailbreak Success? The Hard Truth About AI Safety, with Dr. Saadia Gabriel (Part 2)

What actually happens when AI systems fail in the real world? In this final part of our conversation with Saadia Gabriel (UCLA), we unpack one of the most urgent challenges in modern AI: why even the most advanced models remain vulnerable to manipulation - and what that means for safety, fairness, and society. From multi-turn jailbreaking attacks with near 100% success rates to misinformation shaping human beliefs, this conversation goes beyond surface-level concerns and dives into how harms actually emerge in deployed systems. We explore: * Why current guardrails are not enough * How realistic attack scenarios differ from academic benchmarks * The connection between model vulnerabilities and societal harm * What AI can (and cannot) do about misinformation and persuasion * The open research problems that still don’t have solutions Resources & Links: * Generative AI in the Era of 'Alternative Facts' [https://aclanthology.org/2024.emnlp-main.487/] * ModelCitizens: Representing Community Voices in Online Safety [https://aclanthology.org/2025.emnlp-main.1571/] * Translation as a Scalable Proxy for Multilingual Evaluation [https://arxiv.org/abs/2601.11778] Connect with Dr. Saadia Gabriel: * https://x.com/GabrielSaadia [https://x.com/GabrielSaadia] * https://bsky.app/profile/skgabrie.bsky.social [https://bsky.app/profile/skgabrie.bsky.social]

From Hate Speech to Best Paper: Building Safer AI Systems, with Dr. Saadia Gabriel (Part 1)

What does it mean to build AI systems we can actually trust? In this first part of our conversation with Saadia Gabriel (UCLA), we explore the deeply personal and technical journey behind her work on AI safety, misuse, and responsible NLP. From experiencing targeted hate speech firsthand to receiving a best paper nomination, Saadia shares how her lived experience shaped her research — and why language models must be designed with both capability and risk in mind. 🧠 In this episode, we cover: * How personal experiences influence AI research directions * The intersection of NLP, security, and privacy * Why LLMs can be both powerful and dangerous * What it means to build trustworthy AI systems * Lessons from working across multiple research paradigms * How to pursue high-impact research as a PhD or early-career scientist Resources & Links: * X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agents [https://arxiv.org/abs/2504.13203] Connect with Dr. Saadia Gabriel: * https://x.com/GabrielSaadia [https://x.com/GabrielSaadia] * https://bsky.app/profile/skgabrie.bsky.social [https://bsky.app/profile/skgabrie.bsky.social]

EACL 2026: LLMs Can Hear… But Can They Reason? A New Benchmark for Audio Intelligence

What does it actually mean for a model to understand audio Paper: https://arxiv.org/abs/2601.19673 [https://arxiv.org/abs/2601.19673] In this episode, I talk with Iwona Christop, a PhD student at Adam Mickiewicz University, about her recent EACL paper introducing ART (Audio Reasoning Tasks) — a new benchmark designed to evaluate whether multimodal LLMs can truly reason over audio, not just transcribe or classify it. Most existing benchmarks test audio skills in isolation (like ASR or classification). But real-world intelligence requires something deeper: combining signals, comparing sounds, tracking context, and making decisions. This work takes a different approach: * No text-only shortcuts — tasks can’t be solved via transcription alone * Reasoning-first design — models must combine multiple audio cues * No expert knowledge required — anyone can verify correctness We also dive into the diverse task design, including: * Audio arithmetic (counting and comparing sounds) * Cross-recording speaker & language identification * Sound-based reasoning (e.g., inferring properties from audio) * Speech feature comparison (accents, variations) * Multimodal reasoning across text and sound The dataset includes 9 tasks, 9,000 samples, and 30+ hours of audio — all generated in a scalable way using templates and TTS. 👉 If you care about multimodal reasoning, evaluation, or the limits of current LLM capabilities, this conversation is for you. Iwona Christop: https://www.linkedin.com/in/iwona-christop/ [https://www.linkedin.com/in/iwona-christop/] 👍 Like & subscribe for more deep dives into cutting-edge AI research 🔔 New episodes from EACL 2026 coming soon #WiAIR #EACL2026