AI Resource Hub

• What Is Artificial Intelligence (AI)? – IBM
  IBM provides an accessible and well-structured overview of artificial intelligence, explaining how machines simulate human reasoning, learning, and decision-making. The article introduces types of AI, common techniques, and how AI is applied across industries.
• Artificial Intelligence: Methods and Goals – Britannica
  This Britannica entry covers the core methods used in AI, such as symbolic reasoning and neural networks, and outlines the broader goals of AI research. It’s a clear reference for understanding the foundational ideas behind the field.
• What Is AI? – McKinsey
  McKinsey explains AI in plain language, walking through how it works, where it’s used, and what it means for society and business. The article also clarifies terms like machine learning and generative AI, helping readers build a confident baseline.

• IBM AI Glossary
  Business-oriented definitions of common AI terms, each with clear explanations and links to further learning.
• The Enterprisers Project AI Glossary
  Plain-English glossary of AI terms for business and IT professionals, covering foundational concepts and buzzwords.
• Google AI Glossary
  Technical glossary organized by machine learning topics. A developer-friendly resource with concise definitions.
• Stanford AI Index Glossary
  Glossary from the annual AI Index report, emphasizing key terms in AI impact, governance, and global trends.
• DeepAI Definitions
  Brief, crowd-sourced explanations of machine learning and AI terms. A quick reference for casual learners.
• Microsoft Azure Machine Learning Glossary
  Terms related to AI services and cloud computing, focused on Microsoft’s enterprise tools and ecosystem.
• CIRCLS AI Glossary for Educators
  A glossary designed to help educators understand and teach core AI concepts, tailored for classroom and learning environments.
• NVIDIA AI Glossary
  Terms related to GPU-accelerated AI, hardware performance, and deep learning infrastructure.

Ethics and Governance

• “Recommendation on the Ethics of Artificial Intelligence” – UNESCO
  A globally endorsed framework adopted by 193 countries, setting standards for ethical AI development with a focus on human rights, transparency, and accountability.
• “AI Governance in Practice Report 2024” – IAPP & FTI Consulting
  A detailed report providing practical guidance on building and maturing AI governance programs, addressing regulatory challenges, risk management, and organizational strategies.
• “AI Policy Research & Publications” – Stanford HAI
  A collection of reports and briefings from Stanford’s Human-Centered AI initiative, exploring regulation, safety, global coordination, and governance frameworks for responsible AI.

Healthcare and Biomedicine

• “Ethics and Governance of Artificial Intelligence for Health” – World Health Organization (WHO)
  This comprehensive report by WHO identifies the ethical challenges and risks associated with the use of AI in health. It outlines six consensus principles to ensure AI technologies work for the public benefit, emphasizing transparency, inclusiveness, responsibility, and sustainability.
• “AI Governance in Health Systems: Aligning Innovation, Accountability, and Trust” – Duke-Margolis Center for Health Policy
  This paper explores the main components of health system governance and how different systems approach these components. It offers recommendations for policymakers and stakeholders on standardizing and simplifying processes to democratize access to AI-enabled health tools.
• “Managing Risks in AI-Powered Biomedical Research” – Stanford HAI
  A consortium of scientists and ethicists at Stanford discusses the best ways to manage risks associated with AI in biomedical research. The article emphasizes the need for ethical frameworks to guide researchers in accounting for and protecting against unintended negative consequences.

Future of Work and Industry

• “The Impact of AI on the Workplace: Main Findings from the OECD AI Surveys of Employers and Workers” – OECD
  This report presents insights from OECD surveys conducted among employers and workers in the manufacturing and finance sectors across seven countries. It reveals that while AI adoption is generally viewed positively regarding performance and working conditions, concerns about job displacement persist. The findings underscore the importance of training and worker consultation in achieving favorable outcomes.
• “Mind the AI Divide: Shaping a Global Perspective on the Future of Work” – International Labour Organization (ILO)
  This publication addresses the uneven adoption of AI technologies and their implications for global equity and social justice. It emphasizes the need for inclusive policies and international cooperation to ensure that AI advancements benefit all workers, particularly in developing economies.
• “Work of the Future Initiative” – MIT Industrial Performance Center
  MIT’s initiative conducts multidisciplinary research on how technology is transforming work. It aims to understand the interplay between technological advancements and labor markets, providing insights into creating equitable and inclusive work environments in the age of AI.

Environmental Sustainability

• “Explained: Generative AI’s Environmental Impact” – MIT News
  A clear overview of the energy and water demands tied to training and deploying large-scale AI models. Highlights the need for transparency, efficiency, and sustainable design choices in AI systems.
• “AI Has an Environmental Problem. Here’s What the World Can Do About It” – United Nations Environment Programme (UNEP)
  This article outlines global concerns about AI’s environmental costs and the steps organizations and governments can take to integrate sustainability into AI development and regulation.
• “AI’s Climate Impact Goes Beyond Its Emissions” – Scientific American
  Looks beyond emissions to explore AI’s broader influence on consumption patterns and infrastructure needs. Suggests long-term thinking in tech design, policy, and energy planning.

Culture, Arts, and Society

• “How Culture Shapes What People Want from AI” – Stanford HAI
  Stanford researchers explore how cultural backgrounds influence individuals’ preferences and expectations regarding AI. The study emphasizes the need for culturally inclusive AI design, ensuring that AI systems align with diverse societal values and norms.
• “AI’s Impact on Creativity and Cultural Participation” – UNESCO
  This article examines how AI is transforming artistic expression and cultural access. It raises questions about authorship, inclusion, and the preservation of cultural diversity in an era of generative technologies.
• “Dialogue with the Machine and Dialogue with the Art World: Evaluating Generative AI for Culturally-Situated Creativity” – arXiv (Cornell University)
  A research study proposing a method to evaluate AI tools within the context of culturally grounded creative practice. It reflects on the tensions between algorithmic output and human cultural expression through conversations between artists, machines, and the art world.