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Artificial Intelligence Index Report 2024
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Artificial Intelligence Index Report 2024
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The 2024 Index is our most comprehensive to date and arrives at an important moment when AI's influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surrounding its development. Featuring more original data than ever before, this edition introduces new estimates on AI training costs, detailed analyses of the responsible AI landscape, and an entirely new chapter dedicated to AI's impact on science and medicine. The AI Index report tracks, collates, distills, and visualizes data related to artificial intelligence (AI). Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI. The AI Index is recognized globally as one of the most credible and authoritative sources for data and insights on artificial intelligence. Previous editions have been cited in major newspapers, including the The New York Times, Bloomberg, and The Guardian, have amassed hundreds of academic citations, and been referenced by high-level policymakers in the United States, the United Kingdom, and the European Union, among other places. This year's edition surpasses all previous ones in size, scale, and scope, reflecting the growing significance that AI is coming to hold in all of our lives.
Forward citations
Cited by 6 Pith papers
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Towards Evaluation Engineering: An Empirical Study of ML Evaluation Harnesses in the Wild
An empirical study of 57 ML evaluation harnesses shows 41.4% of operational issues occur in the specification stage, driven mainly by unimplemented features, documentation gaps, and missing input validation.
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Robust LLM Unlearning Against Relearning Attacks: The Minor Components in Representations Matter
Targeting minor components in LLM representations during unlearning yields substantially better resistance to relearning attacks than prior methods.
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The Human Utility Factor: A Computable Welfare Metric That Reframes AI Governance as a Constrained Optimisation Problem
A proposed 'Human Utility Factor' metric claims to set computable automation and redistribution limits for AI governance, but the key threshold formula is inconsistent with the paper's own parameters.
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AI Identification: An Integrated Framework for Sustainable Governance in Digital Enterprises
The paper introduces a dual-layer AI identification framework that integrates cryptographic, blockchain, and zero-knowledge techniques with governance checkpoints to support lifecycle accountability in digital enterprises.
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GAGI: A Gini-Adjusted GDP-per-Capita Index for Distribution-Aware Macroeconomic Welfare Monitoring
Proposes GAGI, a publicly computable index adjusting GDP per capita for inequality and prices to monitor welfare-adjusted prosperity in G7 economies from 2010-2026.
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Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions
A systematic literature review defines self-explainability, proposes a taxonomy and levels framework, and reports that most approaches are conceptual with no standard evaluation method.
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