In this episode of The AI Fundamentalists, hosts Andrew and Sid are joined by AI alignment and safety researcher Dr. Jillian Fisher to unpack the complex realities of political bias in Large Language Models. Dr. Fisher explains that bias isn't just a byproduct of noisy training data; it is also embedded directly into the architectural choices of the models, such as relying on a "majority vote" mechanism to determine the right answer.

The conversation explores why achieving true political neutrality in AI is widely considered impossible due to the inescapable human element involved in AI development. Instead, developers must rely on imperfect approximations of neutrality. Dr. Fisher breaks down approaches like "reasonable pluralism"—which attempts to present all reasonable sides of an argument—and flat-out refusal to answer, noting that both strategies come with distinct trade-offs for user agency and safety.

Listeners will also discover fascinating insights into the psychology of AI persuasion. Dr. Fisher highlights research showing that unlike humans, who typically persuade through empathy and storytelling, AI is most convincing to users through "information packing". Delivering dense walls of facts, combined with natural conversational fluency, can trick our brains into viewing the model as an unquestionable authority. Finally, the group discusses the critical need for socio-technical AI literacy, exploring how teaching the public about AI's limitations and its reliance on flawed internet data could be the ultimate tool for inoculating users against sycophantic behaviors and unwanted persuasion.

What did you think? Let us know.

Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics:

• LinkedIn - Episode summaries, shares of cited articles, and more.
• YouTube - Was it something that we said? Good. Share your favorite quotes.
• Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

In this episode we conclude our series about Metaphysics and modern AI, we explore the definitions of consciousness, reasoning, and thinking to understand if AI possesses these traits. From examining legal accountability and the concept of personhood to analyzing human cognitive frameworks, we map out the differences between actual contemplative problem-solving and probabilistic pattern recognition. The episode covers:

• Defining consciousness, reasoning, and what it means to be a "thinking thing"
• The Turing Test as a low bar and why natural language capabilities create the illusion of intelligence
• Accountability and agency: Why AI models like Claude are not legally recognized as persons
• Daniel Kahneman’s System 1 (fast heuristics) vs. System 2 (contemplative reasoning) thinking
• Why LLMs function primarily as System 1 pattern recognizers rather than true reasoners
• Complex systems, Descartes' dualism, and whether thinking is an emergent property requiring a physical body
• How chatbots use psychological mirroring, filler words, and pauses to trick human biases
• The dangers of anthropomorphizing AI driven by fear of change or financial incentives

This is the final episode in our metaphysics and AI series. You can find the previous episodes here:

• Metaphysics and modern AI: What is causality?
• Metaphysics and modern AI: What is reality?
• Metaphysics and modern AI: What is thinking? - Series Intro

What did you think? Let us know.

Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics:

• LinkedIn - Episode summaries, shares of cited articles, and more.
• YouTube - Was it something that we said? Good. Share your favorite quotes.
• Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

As the AI landscape evolves, the methods we use to process structured data are undergoing a silent revolution. Join us to explore how Tabular Foundation Models (TFMs) are challenging the decade-long reign of tree-based algorithms, why the traditional "train and predict" workflow is being replaced by "in-context learning," and what this shift means for the future of resilient modeling.

To help us, Christoph Molnar, renowned expert in machine learning interpretability and author of the Mindful Modeler newsletter, joins us to share his perspective on the emergence of tabular transformers, the surprising power of synthetic data, and how to maintain model safety in a world without parameter updates.

• The decline of the "fit and predict" paradigm in tabular data
• Transformer architectures vs. traditional models like XGBoost and LightGBM
• In-context learning: Predicting without traditional training steps
• The role of Structural Causal Models (SCMs) in generating training data
• Why models trained on "math and probability" succeed on real-world datasets
• Hardware accessibility and running foundation models on local MacBooks
• Integrating SHAP values and conformal prediction for model interpretability
• The future of the data science workflow: One tool among many or a total shift?

This episode is full of technical insights and forward-looking predictions that are sure to change how you approach your next dataset. As we move into a new era of AI, it’s the perfect time to explore the fundamentals of the next frontier!

What did you think? Let us know.

Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics:

• LinkedIn - Episode summaries, shares of cited articles, and more.
• YouTube - Was it something that we said? Good. Share your favorite quotes.
• Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.