Machines & Meaning

Using John Rawls’s theory of justice as fairness , we examine whether a society that willfully eliminates the conditions for meaningful work can be considered just, regardless of how many new jobs it creates.

Using Ayn Rand’s philosophy of Objectivism, we examine how AI’s efficiency gains are made possible by ignoring the quiet awareness of claiming skills we don’t fully possess.

Episode Description: Using Ibn Khaldun’s concept of asabiyyah (ah-sa-BEE-yah), a word derived from Arabic that roughly translates to tribal solidarity or social cohesion, we examine how AI is being rhetorically elevated to the status of collective purpose.

Using Miranda Fricker’s concept of testimonial injustice, we examine how AI creates new hierarchies of who gets taken seriously and how the credibility we assign (or don’t) affect people’s lives.

Exploring how Kierkegaard’s three spheres of existence reveal why AI might be creating the most sophisticated trap for authentic human development by appearing to create fulfillment while preventing genuine growth.

Exploring how Hannah Arendt’s concept of “thoughtlessness” reveals why AI systems create the perfect conditions for systematic harm that emerge from widespread non-engagement with consequences.

Exploring how Aristotle’s concept of practical wisdom reveals the meta-cognitive skills professionals will need to remain valuable in an age when AI can perform most technical tasks.

Exploring how artificial intelligence systematically undermines the conditions necessary for developing human expertise, creating what we might call “permanent intermediates,” people who achieve functional competence but never develop true mastery.

Exploring how Thomas Aquinas’ Doctrine of Double Effect helps us understand our complex relationship with AI’s unintended consequences. 

Exploring how Immanuel Kant’s concept of the categorical imperative parallels our current challenge of creating immutable ethical rules for artificial intelligence.

Using Simone de Beauvoir’s philosophical framework on categorization, we examine how rigid binary thinking and over-compartmentalization limit our ability to understand and govern A.I.

Using Descartes’ framework for how we acquire knowledge, we examine what happens when AI reasoning models confront problems where mathematical certainty isn’t enough.

We explore Lewis Mumford’s concept of ‘technics’ to answer an essential question in AI: are we creating technologies that adapt to serve human needs, or are we increasingly adapting ourselves to serve theirs?

We explore Alisdair MacIntyre’s concept of narrative fragmentation and whether large language models (LLMs) contribute to it through their underlying architecture.