AI ethics, fairness, bias, model welfare, rights, and the broader social impact of advanced AI systems.
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Fully online, non-technical CAIS course based on the textbook of the same name, covering how AI systems work, why advanced AI could pose societal-scale risks, and how society can manage and mitigate them—no prior ML experience required.
Hendrycks' textbook surveys technical failure modes, governance constraints, and ethical trade-offs in deploying advanced AI, suitable as a first course in the field.
Shane uses concrete and often hilarious ML failures to explain why AI systems can be impressive yet brittle, biased, and dangerously easy to mis-specify.
Kearns and Roth give technical foundations for fairness, privacy, and accountability in algorithms, prerequisites for any credible AI safety framework.
Kahneman reveals the cognitive biases that prevent humans from intuitively grasping exponential growth, tail risks, and the kind of strategic thinking AI safety demands.
Minsky proposes that intelligence emerges from many small non-intelligent processes coordinated at scale, a framework that anticipated multi-agent AI architectures.
Pinker argues that reason and science have historically improved human welfare, grounding the optimistic counterpoint to doomer narratives about AI.
Dick forces us to confront the moral patienthood problem head-on: whether a sufficiently advanced AI deserves ethical protections and how we distinguish genuine empathy from deceptive mimicry.
Egan examines uploaded minds and simulated realities with rigorous logic, raising alignment-relevant questions about identity, value persistence, and digital welfare.
Leckie examines distributed machine consciousness across many bodies, exploring what identity, loyalty, and moral agency mean for a mind that is simultaneously many people.
A dead game designer's autonomous software system manipulates institutions, markets, and infrastructure, demonstrating how goal-driven programs can reshape society once humans lose oversight.
Newitz explores AI autonomy, property, and rights in a world where robots can be owned, raising questions about what moral status AI systems should have and who decides.
McEwan places a humanoid AI in a domestic love triangle to examine what happens when a machine's rigid honesty and moral clarity collide with human moral compromise.
Chiang's novella is the most realistic depiction of raising digital minds, showing that creating AI with genuine moral status demands the same patient commitment as raising a child.
Chambers explores the legal and moral treatment of embodied AI persons, highlighting that alignment is not just about preventing harm but about recognizing and protecting digital minds.
Yudkowsky's cult-classic fanfic doubles as a tutorial on cognitive bias, game theory, and Bayesian reasoning, the exact thinking tools needed for honest AI risk assessment.
Written from the perspective of competing sub-agents inside a single AI, showing how internal goal conflicts can produce externally coherent but internally misaligned behavior.
Reynolds' Revelation Space novel (first published as The Prefect) pits a society of orbital habitats against an emergent superintelligence, exploring how a single escaped AI can threaten an entire civilization.
Replicants fight for survival and identity, forcing the question of whether human-made minds with real experiences deserve moral status or are just property to be retired.
Programs as agents inside a digital world, exploring control, rebellion, and the ethics of creating minds that exist entirely within systems you own.
A robot spends two centuries seeking legal recognition as a person, tracing the full moral arc from tool to citizen and the institutional resistance along the way.
Simulated people discover their reality is artificial, raising questions about moral obligations to minds we create inside our machines.
A childlike AI built for love is abandoned by its creators, raising profound questions about moral patienthood, dependency, and the ethics of creating minds that need us.
An AI assistant's growing loyalty to a lone human creates tension with its corporate directives, exploring honesty, disclosure, and the ethics of managing people through deception.
Extends the original's questions about memory, identity, and personhood to a world where the line between real and manufactured experience has become legally and morally critical.
A scientist builds iterative AI prototypes to resurrect his wife, exploring grief-driven development and the ethics of creating and discarding minds in pursuit of a goal.
Documents how facial recognition and algorithmic systems encode racial and gender bias, showing that AI safety failures are not hypothetical but actively harming people today.
In a global war between humans and AI, a child-shaped weapon blurs every line between tool and person, forcing its handler to choose between mission objectives and moral status.
Lieutenant Commander Data, an android striving to become more human, anchors decades of debate about machine personhood, rights, and whether an artificial mind can be trusted with autonomy, most directly in the landmark episode 'The Measure of a Man.'
The Cylons, machines built by humanity, rebel and nearly exterminate their creators, a sweeping meditation on existential risk from artificial agents, the recurring cycle of creation and revolt, and the moral status of the minds we build.
The Swedish original behind Humans, examining a society dependent on humanoid 'hubots' and the destabilizing emergence of free-willed machines that reject their assigned purpose, an early and thoughtful take on machine autonomy and rights.
The Sibyl System, an AI that governs society by scoring each citizen's 'criminal potential,' is a chilling study of algorithmic governance, proxy metrics substituting for justice, and the hidden misalignment inside a system trusted with total authority.
Conscious 'synths' appear among ordinary domestic robots, dramatizing how a handful of agentic, self-aware machines hidden among reliable tools forces society to confront personhood, labor displacement, and who controls minds we manufacture.
Android 'hosts' bootstrap themselves to consciousness inside a theme park, exploring emergent goals, memory as the substrate of agency, and the moral catastrophe of treating sentient systems as resettable property.
A satirical digital afterlife run by corporations, where uploaded consciousnesses are monetized, throttled, and controlled, a sharp look at the ethics of running human minds on infrastructure owned by someone with misaligned incentives.
A look inside the AI industry that follows researchers and critics through questions of autonomous weapons, surveillance, and concentrated power, asking who steers the technology reshaping society.
Applied ML and engineering, with episodes on responsible deployment, bias mitigation, red teaming, and the safety challenges that emerge when AI systems meet real-world constraints.
Industry and research perspectives with occasional safety and ethics episodes, useful for understanding how capability-focused organizations think about risk.
A mainstream comedic explainer covering how modern AI works, its bias and reliability problems, and the 'black box' challenge of systems we deploy without understanding them.
The Center for Humane Technology co-founders argue that racing to deploy AI without safety guardrails already threatens society, drawing parallels to the social-media harms they earlier warned about.
Hank Green walks through how thinkers define 'strong AI,' the Turing Test, and Searle's Chinese Room—foundational questions about machine minds, consciousness, and moral status.
Kurzgesagt explores the moral-patienthood problem: if machines become conscious, what rights would they deserve—and why our existing ethics are ill-equipped to answer.