Reclaiming the Digital Commons: A Public Data Trust for Training Data

Chan, Alan; Bradley, Herbie; Rajkumar, Nitarshan

doi:10.1145/3600211.3604658

Cited by 4 publications

References 74 publications

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Mapping the individual, social and biospheric impacts of Foundation Models

Domínguez Hernández,

Krishna,

Perini

et al. 2024

The 2024 ACM Conference on Fairness, Accountability, and Transparency

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Responding to the rapid roll-out and large-scale commercialization of foundation models, large language models, and generative AI, an emerging body of work is shedding light on the myriad impacts these technologies are having across society. Such research is expansive, ranging from the production of discriminatory, fake and toxic outputs, and privacy and copyright violations, to the unjust extraction of labor and natural resources. The same has not been the case in some of the most prominent AI governance initiatives in the global north like the UK's AI Safety Summit and the G7's Hiroshima process, which have influenced much of the international dialogue around AI governance. Despite the wealth of cautionary tales and evidence of algorithmic harm, there has been an ongoing over-emphasis within the AI governance discourse on technical matters of safety and global catastrophic or existential risks. This narrowed focus has tended to draw attention away from very pressing social and ethical challenges posed by the current brute-force industrialization of AI applications. To address such a visibility gap between real-world consequences and speculative risks, this paper offers a critical framework to account for the social, political, and environmental dimensions of foundation models and generative AI. Drawing on a review of the literature on the harms and risks of * Equal contribution as lead authors † Also with The Alan Turing Institute.

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Mapping the individual, social and biospheric impacts of Foundation Models

Domínguez Hernández,

Krishna,

Perini

et al. 2024

The 2024 ACM Conference on Fairness, Accountability, and Transparency

View full text Add to dashboard Cite

show abstract

Visibility into AI Agents

Chan,

Ezell,

Kaufmann

et al. 2024

The 2024 ACM Conference on Fairness, Accountability, and Transparency

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The Democratization of Artificial Intelligence: Theoretical Framework

Costa,

Aparicio,

Aparicio

et al. 2024

Applied Sciences

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The democratization of artificial intelligence (AI) involves extending access to AI technologies beyond specialized technical experts to a broader spectrum of users and organizations. This paper provides an overview of AI’s historical context and evolution, emphasizing the concept of AI democratization. Current trends shaping AI democratization are analyzed, highlighting key challenges and opportunities. The roles of pivotal stakeholders, including technology firms, educational entities, and governmental bodies, are examined in facilitating widespread AI adoption. A comprehensive framework elucidates the components, drivers, challenges, and strategies crucial to AI democratization. This framework is subsequently applied in the context of scenario analyses, offering insights into potential outcomes and implications. The paper concludes with recommendations for future research directions and strategic actions to foster responsible and inclusive AI development globally.

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Reclaiming the Digital Commons: A Public Data Trust for Training Data

Cited by 4 publications

References 74 publications

Mapping the individual, social and biospheric impacts of Foundation Models

Mapping the individual, social and biospheric impacts of Foundation Models

Visibility into AI Agents

The Democratization of Artificial Intelligence: Theoretical Framework

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