Advancing Data Justice Research and Practice: An Integrated Literature Review

Leslie, David; Katell, Michael; Aitken, Mhairi; Singh, Jatinder; Briggs, Morgan; Powell, Rosamund; Rincón, Cami; Chengeta, Thompson; Birhane, Abeba; Perini, Antonella; Jayadeva, Smera; Mazumder, Abhishek

doi:10.2139/ssrn.4073376

Cited by 16 publications

(1 citation statement)

References 97 publications

(110 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The environmental costs of AI outlined above are unevenly distributed, with historically marginalized communities bearing the brunt of the impacts (Domínguez Hernández et al, 2024). As stressed by environmental and climate justice scholars, individuals and communities with the least influence in decisionmaking processes (and the conditions in which they live and work) within and between communities are the most exposed and affected by negative environmental impacts like pollution, ecosystem destruction, and involuntary displacement (Bender et al, 2021;Bullard, 1993;Domínguez Hernández et al, 2024;Leslie et al, 2022;Nixon, 2011;Sachs & Santarius, 2007;Westra & Lawson, 2001).…”

Section: Risks From Model Scaling: Exponential Expansion Of Compute A...mentioning

confidence: 99%

Future Shock: Generative AI and the International AI Policy and Governance Crisis

Leslie,

Perini

2024

Harvard Data Science Review

View full text Add to dashboard Cite

stakeholders from across industry, academia, government, and civil society, and from around the globe, had made concerted efforts to develop standards, policies, and governance mechanisms to ensure the ethical, responsible, and equitable production and use of AI systems.However, as we then show, despite these ostensibly supportive activities and background conditions, several primary drivers of future shock converged to produce an international AI policy and governance crisis in the wake of the dawning of the GenAI era. Such a crisis, we argue, was marked by the disconnect between the strengthening thrust of public concerns about the hazards posed by the hasty industrial scaling of GenAI and the absence of effectual regulatory mechanisms and needed policy interventions to address such hazards. In painting a broad-stroked picture of this crisis, we underscore two sets of contributing factors. First, there have been factors that have demonstrated the absence of various vital aspects of AI policy and governance capability and execution-and thus the absence of key preconditions for readiness and resilience in managing technological transformation. These include prevalent enforcement gaps in existing digital-and data-related laws (e.g., intellectual property and data protection statutes), a lack of regulatory AI capacity, democratic deficits in the production of standards for trustworthy AI, and widespread evasionary tactics of ethic washing and state-enabled deregulation.Second, there have been factors that have significantly contributed to the presence of a new scale and order of systemic-, societal-, and biospheric-level risks and harms. Chief among these were the closely connected dynamics of unprecedented scaling and centralization that emerged as both drivers and by-products of the GenAI revolution. We focus, in particular, on model scaling and industrial scaling. Whereas the scaling of data, model size, and compute were linked to the emergence of serious model intrinsic risks deriving from the unfathomability of training data, model opacity and complexity, emergent model capabilities, and exponentially expanding compute costs, the rapid industrialization of FMs and GenAI systems meant the onset of a new scale of systemic risks that spanned the social, political, economic, cultural, and natural ecosystems in which these systems were embedded. The brute-force commercialization of GenAI ushered in a new age of widespread exposure in which increasing numbers of impacted people and communities at large were made susceptible to the risks and harms issuing from model scaling and to new possibilities for misuse, abuse, and cascading system-level effects.Alongside these aspects of model scaling and industrial scaling, patterns of economic and geopolitical centralization only further intensified conditions of future shock. The steering and momentum of these scaling dynamics lay largely in the hands of a few large tech corporations, which essentially controlled the data, compute, and skills and knowledge infrastructures r...

show abstract

Section: Risks From Model Scaling: Exponential Expansion Of Compute A...mentioning

confidence: 99%

Future Shock: Generative AI and the International AI Policy and Governance Crisis

Leslie,

Perini

2024

Harvard Data Science Review

View full text Add to dashboard Cite

show abstract

A powerful potion for a potent problem: transformative justice for generative AI in healthcare

Gross

2024

AI Ethics

View full text Add to dashboard Cite

Generative Artificial Intelligence (AI), as a transformative technology, holds significant promise for applications in healthcare. At the same time, the datafication, AI integration, and commodification of health have opened the floodgates for ethical issues, including those related to fairness, access, beneficence, democracy, solidarity, inclusion, and societal harms. As further the digitalization, innovation, and disruption of healthcare is inevitable, the paper maps out how power, equity, access, identity, participation, and knowledge contribute to creating social injustice issues. It also discusses that current justice approaches—distributive justice, representational justice, restorative justice, and capabilities-centered justice—do not have enough impact to prevent or remedy the many harms and injustices that AI has already created in healthcare or will continue to do so. The paper proposes that a transformative justice approach is needed for generative AI as a transformative technology, focused on (1) peace, emancipation, and eliminating the root causes of injustice, (2) holistic conflict resolution, (3) human rights-based approaches, and (4) the empowerment of agency and actors.

show abstract

Data: Societal Datafication & Libraries

Laskowski

2025

Encyclopedia of Libraries, Librarianship, and Information Science

View full text Add to dashboard Cite

Advancing Data Justice Research and Practice: An Integrated Literature Review

Cited by 16 publications

References 97 publications

Future Shock: Generative AI and the International AI Policy and Governance Crisis

Future Shock: Generative AI and the International AI Policy and Governance Crisis

A powerful potion for a potent problem: transformative justice for generative AI in healthcare

Data: Societal Datafication & Libraries

Contact Info

Product

Resources

About