Women in AI Research (WiAIR)
Women in AI Research (WiAIR)
Podcast Description
Women in AI Research (WiAIR) is a podcast dedicated to celebrating the remarkable contributions of female AI researchers from around the globe. Our mission is to challenge the prevailing perception that AI research is predominantly male-driven. Our goal is to empower early career researchers, especially women, to pursue their passion for AI and make an impact in this rapidly growing field. You will learn from women at different career stages, stay updated on the latest research and advancements, and hear powerful stories of overcoming obstacles and breaking stereotypes.
Podcast Insights
Content Themes
The podcast focuses on topics such as bias in AI, the limitations of transformer models, and the personal journeys of women in AI research. Episodes include discussions on the social implications of AI, technical challenges in language models, and the overall impact of diverse voices in the AI field.
Women in AI Research (WiAIR) is a podcast dedicated to celebrating the remarkable contributions of female AI researchers from around the globe. Our mission is to challenge the prevailing perception that AI research is predominantly male-driven. Our goal is to empower early career researchers, especially women, to pursue their passion for AI and make an impact in this rapidly growing field. You will learn from women at different career stages, stay updated on the latest research and advancements, and hear powerful stories of overcoming obstacles and breaking stereotypes.
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
- 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
Connect with Dr. Malihe Alikhani:
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