AI Governance Entries

Entries

141

Lexicon entries typed as governance.

Top Categories

2

Topic areas where this entry type appears most often.

AI Safety, Ethics and Governance

73 governance entries in this category.

AI Safety Organizations and Initiatives

68 governance entries in this category.

AI Ethics

AI Ethics is the field studying moral principles and guidelines for the responsible development, deployment, and use of artificial intelligence, aiming to prevent harm, bias, and ensure fairness and accountability in AI systems.

Timnit Gebru

Timnit Gebru is a prominent computer scientist and advocate for ethical AI, known for her research on bias in facial recognition and large language models. She co-founded the Distributed AI Research Institute (DAIR) to promote.

Margaret Mitchell

Margaret Mitchell is a leading AI ethics researcher, known for co-founding and co-leading Google's Ethical AI team. Her work focuses on fairness, bias, and interpretability in AI systems, addressing their societal impact.

Emily Bender

Emily M. Bender is a computational linguist and professor known for her critical work on large language models. She co-authored the influential "Stochastic Parrots" paper, highlighting ethical risks and limitations of current AI systems.

Angelina McMillan-Major

Angelina McMillan-Major is an AI ethics researcher and co-author of the influential "On the Dangers of Stochastic Parrots" paper. Her work focuses on responsible AI development, governance, and mitigating risks like bias in large language.

Stochastic Parrots

Coined by Emily Bender, "Stochastic Parrots" characterizes large language models as systems generating text by predicting sequences from training data, lacking true understanding. It emphasizes risks like bias, misinformation, and environmental impact.

On the Dangers of Stochastic Parrots

"On the Dangers of Stochastic Parrots" is a pivotal paper critiquing large language models. It argues LLMs are "stochastic parrots" that mimic patterns, potentially perpetuating bias, misinformation, and incurring high environmental costs.

Algorithmic Bias

Algorithmic bias occurs when an AI system produces unfair or discriminatory outcomes. This stems from flaws in its training data, design, or implementation, reflecting and amplifying societal biases and leading to unequal treatment for certain.

Fairness in Machine Learning

Fairness in Machine Learning ensures AI systems produce equitable, non-discriminatory outcomes for diverse groups. It involves identifying and mitigating algorithmic biases to prevent unfair treatment based on sensitive attributes like race or gender.

Demographic Parity

Demographic Parity is an AI fairness metric ensuring that a positive outcome or decision is predicted at the same rate across all predefined demographic groups. It aims for equal representation in predicted outcomes.

Equalized Odds

Equalized Odds is an AI fairness criterion ensuring a binary classifier achieves equal true positive rates and equal false positive rates across different sensitive groups. This aims for equitable performance for both positive and negative.

Individual Fairness

Individual fairness in AI means that similar individuals should receive similar treatment or outcomes from an AI system. It focuses on consistency for individuals, rather than groups, based on relevant attributes.

AI Hub

AI Learning Resources for Students, Parents, and Curious Readers

This hub connects the main AI learning surfaces on Lexicon Labs into one path: the encyclopedia preview, student-friendly books, themed bundles, and the tools that help readers turn concepts into working understanding.

Paperback Hub

AI Books for Kids, Pre-Teens, and Teens

This page groups together Lexicon Labs paperback titles that help younger readers understand artificial intelligence, computation, and the people behind modern computing.

Rational Decision Matrix

Score options by weighted criteria to make strategy and career decisions with clarity.

Citation Machine

Create citations for papers fast with APA/MLA formatting and copy-ready output.

Readability X-Ray

Analyze clarity in essays, emails, and articles with readability scores and instant issue flags.

AI for Smart Pre-Teens and Teens

A student-friendly intro to AI concepts, real-world use cases, and practical skills for the next generation.

Alan Turing

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

George Washington

A concise look at George Washington's leadership, decision-making, and role in shaping early American history.

Artificial Intelligence Bundle

Books that explain artificial intelligence clearly for young and curious readers.

Leadership Bundle

A curated collection of books in the Leadership Bundle series, designed for curious, independent readers.