Sex, Drugs, and Alcohol in the Digital Neighborhood:  A multi-method analysis of online discourse amongst Black and Hispanic Youth

Stevens, Robin; Bonett, Stephen; Kenyatta, Kahaari; Chittamuru, Deepti; Bleakley, Amy

doi:10.24251/hicss.2019.261

Cited by 12 publications

(1 citation statement)

References 19 publications

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“…Publishers of scientific journals seem to encourage the reuse of these data sets, judging by the number of published articles engaging in this practice [ 18 ]. Any problem within these Internet platforms is exacerbated when it is integrated into a benchmark that serves as critical information infrastructures in both research and commerce [ 20 , 21 ].…”

Section: Information Infrastructure Of Open Data Benchmarksmentioning

confidence: 99%

Uncoupling inequality: Reflections on the ethics of benchmarks for digital media

Washington¹,

Rhue²,

Nakamura³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Our collaboration seeks to demonstrate shared interrogation by exploring the ethics of machine learning benchmarks from a socio-technical management perspective with insight from public health and ethnic studies. Benchmarks, such as ImageNet, are annotated open data sets for training algorithms. The COVID-19 pandemic reinforced the practical need for ethical information infrastructures to analyze digital and social media, especially related to medicine and race. Social media analysis that obscures Black teen mental health and ignores anti-Asian hate fails as information infrastructure. Despite inadequately handling non-dominant voices, machine learning benchmarks are the basis for analysis in operational systems. Turning to the management literature, we interrogate cross-cutting problems of benchmarks through the lens of coupling, or mutual interdependence between people, technologies, and environments. Uncoupling inequality from machine learning benchmarks may require conceptualizing the social dependencies that build structural barriers to inclusion.

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Section: Information Infrastructure Of Open Data Benchmarksmentioning

confidence: 99%

Uncoupling inequality: Reflections on the ethics of benchmarks for digital media

Washington¹,

Rhue²,

Nakamura³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

Ethical Data Science

Washington

2023

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Predictive data science is often viewed as a public good, yet too often human needs are pitted against the financial interests of large digital infrastructures. Can data science truly serve the public interest? The question of our time is whether the promise of predictive science will exceed the inherent conflicts of interests presented by the organizations that fund it. As one of the first books on public interest technology, Ethical Data Science: Prediction in the Public Interest acknowledges the institutional incentives that drive computational prediction. It argues that data science prediction embeds administrative preferences that often ignore the disenfranchised. The book introduces the prediction supply chain to highlight moral questions alongside the interlocking legal and commercial interests dominating data science. Situating data-driven analysis within multiple layers of effort systematically exposes risky dependencies while also pinpointing opportunities for principled action, research ethics, and policy interventions. The prediction supply chain organizes critical thinking throughout the typical data science workflow, from gathering data through learning from insights. Data scientists can explore the full human potential of prediction by thinking like chefs, painters, historians, librarians, philosophers, and architects. Each stage draws on the arts and humanities as methods to empower broader conversations across multiple forms of expertise. Practitioners, academics, students, and legislators will learn how to identify social dynamics in data trends, reflect on ethical questions, and deliberate over solutions. The book establishes the limits of predictive technology controlled by the few and urges for more inclusive data science.

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Washington

2023

Ethical Data Science

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Sex, Drugs, and Alcohol in the Digital Neighborhood: A multi-method analysis of online discourse amongst Black and Hispanic Youth

Cited by 12 publications

References 19 publications

Uncoupling inequality: Reflections on the ethics of benchmarks for digital media

Uncoupling inequality: Reflections on the ethics of benchmarks for digital media

Ethical Data Science

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