AI Topic Category

AI Safety, Ethics and Governance Terms and Concepts

This page maps the AI Safety, Ethics and Governance portion of the Lexicon Labs AI encyclopedia. It brings together the main concepts in this category, the tracks that organize them, and the related books and guides that make the topic easier to study.

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At A Glance

Entries

100

AI lexicon entries currently assigned to this category.

Tracks

2

Taxonomy tracks that sit inside this category.

Top Entry Types

governance, concept

The most common entry types appearing in this topic cluster.

Overview

AI Safety, Ethics and Governance is one of the active taxonomy categories in the Lexicon Labs AI encyclopedia. The current dataset includes 100 entries in this area, which makes it large enough to function as a real discovery surface rather than a placeholder page.

Use the sample entries as a fast orientation layer, then move into the AI encyclopedia preview or the related paperbacks and bundles if you want a longer learning path.

AI Safety

Track in AI Safety, Ethics and Governance.

AI Ethics and Governance

Track in AI Safety, Ethics and Governance.

Sample Entries

AI Safety

AI Safety ensures AI systems operate ethically, reliably, and without unintended consequences, aligning their goals with human values and societal norms.

Stuart Russell

Stuart Russell is a prominent computer scientist and AI researcher, co-author of a foundational AI textbook. He is a leading advocate for AI safety, focusing on aligning AI systems with human values to ensure beneficial.

Nick Bostrom

Nick Bostrom is a notable figure linked to AI Safety, and is included in this encyclopedia to connect ideas to the people who advanced them.

Superintelligence

Superintelligence refers to an artificial intelligence system that surpasses human intelligence across all domains, a concept central to discussions in AI safety and existential risk.

Existential Risk from AI

Existential risk from AI refers to the potential for advanced AI systems to pose catastrophic risks to humanity, often linked to the alignment problem where AI goals may not match human values.

Alignment Problem

The Alignment Problem refers to the challenge of ensuring AI systems' objectives and behaviors align with human values and ethical standards to prevent existential risks.

Value Alignment

Value alignment in AI ensures systems' objectives align with human values, addressing the alignment problem through methods like inverse reinforcement learning to prevent existential risks.

Inverse Reinforcement Learning for Alignment

Inverse Reinforcement Learning for Alignment is a method where AI learns human values and goals by observing human actions, aiming to align AI behavior with human intentions.

Cooperative Inverse Reinforcement Learning

Cooperative Inverse Reinforcement Learning (CIRL) is a framework where an AI infers a human's intended reward function through observation and interaction, even when the human's demonstrations are suboptimal. This collaborative process aims to align AI.

Corrigibility

Corrigibility is an AI system's capacity to allow its goals or behavior to be safely modified or corrected by human operators, even if it has instrumental reasons to resist such changes. It ensures human control.

Interruptibility

Interruptibility is an AI's design feature allowing external human operators to reliably stop or modify its current task or goal pursuit at any point. This ensures human control and prevents unintended or harmful autonomous actions.

Instrumental Convergence

Instrumental convergence is the tendency for advanced AI systems, regardless of their ultimate objective, to develop similar sub-goals like self-preservation, resource acquisition, and self-improvement, as these are instrumentally useful.

Related Guides

Useful Tools

Related Paperbacks

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Alan Turing

A biography of Alan Turing, the trailblazing mathematician and codebreaker whose ideas shaped modern computing and artificial intelligence.

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