READING LIST

Considering the breadth of participants’ backgrounds and the novelty of some of
our discussions, we often created reading lists of resources to act as primers.
Here, we collate the key articles and papers shared with our experts for additional background reading.

EQUITABLE AI

• A Blueprint for Equitable AI, a report by the Aspen Institute Science and Society program

• Machine Super Intelligence, Shane Legg

• “Generative Adversarial Nets.”  Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, et al.

• Responsible AI Practices, Google

• “A Blueprint for Equity and Inclusion in Artificial Intelligence White Paper”, WEF Global Future Council on Artificial Intelligence for Humanity

• Decolonial AI, Shakir Mohamed, Marie-Therese Png and William Isaac, DeepMind

• Who Will Design the Future? Flynn Coleman, Nautilus

• We’re in a Diversity Crisis: Cofounder of Black in AI on What’s Poisoning Algorithms in Our Lives, Jackie Snow, MIT Technology Review

• Toward a Theory of Justice for Artificial Intelligence, Iason Gabriel, Daedalus

• Fairness for Unobserved Characteristics, Nenad Tomašev, Kevin McKee, Jackie Kay, Shakir Mohamed, DeepMind

• Investigating the impact of publicly naming biased performance results of commercial ai products, Inioluwa Deborah Raji, Joy Buolamwini 

• Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing, Inioluwa Deborah Raji, Andrew Smart, Rebecca N White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, Parker Barnes

• Data and its (dis)contents: A survey of dataset development and use in machine learning research, A. Paullada, I. Deborah Raji, E. M Bender, E. Denton, A. Hanna

• You can't sit with us: Exclusionary pedagogy in AI ethics education, Inioluwa Deborah Raji, Morgan Klaus Scheuerman, Razvan Amironesei

• The Head of Google’s Brain Team is More Worried About the Lack of Diversity in Artificial Intelligence than an AI Apocalypse, Johana Bhuiyan, Vox

• Robotics, AI Put Pressure on K-12 Education to Adapt and Evolve, Ulrik Juul Christensen, The Hill

• The Whiteness of AI, Stephen Cave, Kanta Dihal, Philosophy and Technology

AI FOR LEARNING

• How AI can accelerate students’ holistic development and make teaching more fulfilling, Wendy Kopp, Bo Stjerne Thomsen, World Economic Forum 

• How we can use AI to power career-driven lifelong learning, Times Higher Education

• ChatGPT Isn’t the Only Way to Use AI in Education, Nabeel Gillani, WIRED

• AI Will Transform Teaching and Learning. Let’s Get it Right., Claire Chen, Stanford University Human-Centered Artificial Intelligence

• Keeping Artificial Intelligence Human: Combining the Power of AI with the Experience of Examiners, Jing Xu, Cambridge Assessment  

• Everything that went wrong with the botched A-Levels algorithm, Will Bedingfield, WIRED 

• Education Secretary establishes government’s forward thinking AI approach, UK Government  

• The Commission publishes guidelines to help teachers address misconceptions about Artificial Intelligence and promote its ethical use, EU Commission 

• AI and Education: Guidance for Policymakers, UNESCO 

• Analyzing the Hiring Technologies of Large Hourly Employers, Upturn

DATA EQUITY & GOVERNANCE IN AI

• What is data equity and why does it matter?, Data.org

• The Politics of Language: The Controversial Origin Story of Kiswahili Sanifu, and Considerations for Building Language Technology, Mozilla 

• Socially Responsible AI Algorithms: Issues, Purposes, and Challenges, Lu Cheng, Kush R. Varshney, Huan Liu

• The History of the General Data Protection Regulation, European Data Protection Supervisor

• Finding Errors in Perception Data With Learned Observation Assertions, Stanford Dawn, 

• Large Language Models: A New Moore's Law?, Hugging Face

• Power, ecology and diplomacy in critical data infrastructures, Open Data Institute 

• Recalibrating assumptions on AI, Chatham House, 

• Lost in Translation: Large Language Models in Non-English Content Analysis, Center for Democracy & Technology

• A new vision of artificial intelligence for the people, MIT Tech Review 

• Algorithmic bias in data-driven innovation in the age of AI, Shahriar Akter,  Grace McCarthy et al. 

• Intelligence is dead: long live artificial intelligence, Chatham House

• On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?, Emily M. Bender, Timnit Gebru et al.

• Predictive policing algorithms are racist. They need to be dismantled., MIT Tech Review

• Artificial intelligence and algorithmic bias: implications for health systems, Trishan Panch, Heather Mattie et al.

• Understanding and Addressing Racial Disparities in Health Care, Health Care Financing Review, 21(4)

• No Equity without Data Equity: Data Reporting Gaps for Native Hawaiians and Pacific Islanders as Structural Racism Journal of Health Polit Policy Law,

• Using artificial intelligence and open data for innovation and accountability, Open Data Institute

• AI governance must balance creativity with sensitivity, Chatham House

• UK data bill favours big business and ‘shady’ tech firms, rights group claims, The Guardian

• A Comparative Analysis of the EU and U.S. Data Privacy Regimes and the Potential for Convergence, Ruben de Bruin

• AI Governance and Human Rights: Resetting the Relationship, Chatham House

• Informing the Global Data Future: Benchmarking Data Governance Frameworks, Sara Marcucci, Natalia González Alarcón et al. 

• Data Intermediary, Heleen Janssen, Jatinder Singh et al. 

• Towards an international data governance framework, Steve MacFeely, Angela Me et al.

• Rethinking data and rebalancing digital power, Ada Lovelace Institute

• The future of data trusts and the global race to dominate AI, Bennett Institute for Public Policy

• Responsible Sourcing of Data Enrichment Services, Partnership on AI

• A Relational Theory of Data Governance, Salomé Viljoen, The Yale Law Journal

• The CARE Principles for Indigenous Data Governance, Stephanie Russo Carroll, Ibrahim Garba et al.

• Artificial intelligence (AI) deployments in Africa : benefits, challenges and policy dimensions, Arthur Gwagwa, Erika Kraemer-Mbula et al.

• Data governance: Organizing data for trustworthy Artificial Intelligence, Marijn Janssen, Paul Brous et al.

• When data is capital: Datafication, accumulation, and extraction, Jathan Sadowski

• Land grab/data grab: precision agriculture and its new horizons, Alistair Fraser

AI & THE FUTURE OF FOOD SECURITY

• Machine learning for food security: Principles for transparency and usability, Zhou et al, Economic Perspectives and Policy 

• GPAI climate report, Global Partnership on AI in collaboration with the Climate Change AI and the Centre for AI & Climate

ANTICIPATING AND MANAGING RISKS FROM RAPIDLY ADVANCING AI SYSTEMS

• Attention Is All You Need, Ashish Vaswani, Noam Shazeerar et al.

• General Purpose AI Poses Serious Risks, Should Not Be Excluded From the EU’s AI

• Act | Policy Brief, Amba Kak, Dr. Sarah Myers West et al., AI Now Institute 

• Frontier AI Regulation: Managing Emerging Risks to Public Safety, Markus Anderljung, Joslyn Barnhart et al.

• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Jason Wei, Xuezhi Wang et al.

• Generative Agents: Interactive Simulacra of Human Behavior, Joon Sung Park, Joseph C. O'Brien et al. 

• Now That ChatGPT Is Plugged In, Things Could Get Weird, Will Knight, Khari Johnson, Wired

• Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection, Abdelnabi, Shailesh Mishra, Christoph Endres et al.  

• On the Adversarial Robustness of Multi-Modal Foundation Models, Christian Schlarmann and Matthias Hein et al. 

• The Fallacy of AI Functionality, Inioluwa Deborah Raji, I. Elizabeth Kumar et al.

• Model evaluation for extreme risks, Toby Shevlane, Sebastian Farquhar et al.

• Frontier Threats Red Teaming for AI Safety, Anthropic 

• Evaluating Language-Model Agents on Realistic Autonomous Tasks, Megan Kinniment, Lucas Jun et al., Alignment Research Center

• Situational awareness, Kelsey Piper

• OpenAI, Google, Microsoft and Anthropic form body to oversee safe ‘frontier AI’ development, Paul Sawers, TechCrunch

• Securing AI Makes for Safer AI, John Bansemer and Andrew Lohn, Center for Security and Emerging Technology

• National Risk Register 2020, Gov.uk

• FACT SHEET: Biden-Harris Administration Secures Voluntary Commitments from Leading Artificial Intelligence Companies to Manage the Risks Posed by AI, The White House Briefing Room

• Strengthening Resilience to AI Risk, Centre for Emerging Technology and Security (CETAS)

• Model Cards for Model Reporting, Margaret Mitchell, Inioluwa Deborah Raji, Timnit Gebru et al.

AI & DISABILITY

• Disability, Bias, and AI – Report, AI Now Institute

• Algorithm-driven Hiring Tools: Innovative Recruitment or Expedited Disability Discrimination?, Centre for Democracy & Technology (CDT)

• AI & Disability Inclusion Toolkit, PEAT

• Towards Fairness in AI for People with Disabilities: A Research Roadmap, Sig Access

• Employers making progress supporting employees with disabilities but must make it a priority to recruit and retain, Adobe 

• Why disability bias is a particularly stubborn problem, The Harvard Gazette

• Algorithmic Diversity: Mitigating AI Bias And Disability Exclusion, Forbes

• AI language models show bias against people with disabilities, study finds, PennState

• EEOC Releases New Resource on Artificial Intelligence and Title VII, EEOC

• ChatGPT as Assistive Tech: How AI is Improving Accessibility for Neurodiverse Individuals, Medium

• Civil Rights Standards for 21st Century Employment Selection Procedures, Centre for Democracy & Technology (CDT)

• IDRC’s Inclusive Artificial Intelligence (AI) Initiatives, WeCount

SCIENCE

• Scientific discovery in the age of artificial intelligence, Nature

• Artificial Intelligence in Science: Challenges, Opportunities, and the Future of Research, OECD

• The Values Encoded in Machine Learning Research, Abeba Birhane, Pratyusha Kalluri, Dallas Card et al.

• Deep Learning in Science, Stefano Bianchini, Moritz Müller, Pierre Pelletier

• AI Roadmap, AI Council, UK Government