Owning Ethics: Corporate Logics, Silicon Valley, and the Institutionalization of Ethics

Metcalf, Jacob; Moss, Emanuel; boyd, danah

doi:10.1353/sor.2019.0022

Cited by 136 publications

(27 citation statements)

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“…However, we found many recent calls for more accountability for research innovations [40,55,83]. Several prominent computing conferences -such as the Conference on Neural Information Processing System (NeurIPS [15]), Annual Meetings of the Association for Computational Linguistics (ACL) [112], and the ACM Conference on Intelligent User Interfaces (IUI) [2] -have begun to experiment with ways to encourage or even require researchers to state both the positive and negative potential implications of their work in all paper submissions.…”

Section: Critiques Of Technologymentioning

confidence: 94%

“That’s important, but...”: How Computer Science Researchers Anticipate Unintended Consequences of Their Research Innovations

Pang

Jiang

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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Computer science research has led to many breakthrough innovations but has also been scrutinized for enabling technology that has negative, unintended consequences for society. Given the increasing discussions of ethics in the news and among researchers, we interviewed 20 researchers in various CS sub-disciplines to identify whether and how they consider potential unintended consequences of their research innovations. We show that considering unintended consequences is generally seen as important but rarely practiced. Principal barriers are a lack of formal process and strategy as well as the academic practice that prioritizes fast progress and publications. Drawing on these findings, we discuss approaches to support researchers in routinely considering unintended consequences, from bringing diverse perspectives through community participation to increasing incentives to investigate potential consequences. We intend for our work to pave the way for routine explorations of the societal implications of technological innovations before, during, and after the research process. CCS CONCEPTS• Human-centered computing → HCI theory, concepts and models.

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Section: Critiques Of Technologymentioning

confidence: 94%

“That’s important, but...”: How Computer Science Researchers Anticipate Unintended Consequences of Their Research Innovations

Pang

Jiang

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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“…Reputation and customer value are not new frameworks for legitimizing ethics work (Metcalf et al, 2019). We should not interpret concern for reputation or attention to market value as always an indication of empty veneer.…”

Section: Crosscurrents Within and Against The Supply Chainmentioning

confidence: 99%

“…Many big technology companies are building responsible artificial intelligence (AI) programs 1 (Jobin et al, 2019), but those "owning" these programs are limited in their ability to create change, resulting in varying levels of efficacy (Metcalf et al, 2019). Even those without designated ethics roles are called to follow responsible AI guidelines (Jobin et al, 2019), checklists (Madaio et al, 2020), and other processes (Sirur et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Dislocated accountabilities in the “AI supply chain”: Modularity and developers’ notions of responsibility

Widder

Nafus

2023

Big Data & Society

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Responsible artificial intelligence guidelines ask engineers to consider how their systems might harm. However, contemporary artificial intelligence systems are built by composing many preexisting software modules that pass through many hands before becoming a finished product or service. How does this shape responsible artificial intelligence practice? In interviews with 27 artificial intelligence engineers across industry, open source, and academia, our participants often did not see the questions posed in responsible artificial intelligence guidelines to be within their agency, capability, or responsibility to address. We use Suchman's “located accountability” to show how responsible artificial intelligence labor is currently organized and to explore how it could be done differently. We identify cross-cutting social logics, like modularizability, scale, reputation, and customer orientation, that organize which responsible artificial intelligence actions do take place and which are relegated to low status staff or believed to be the work of the next or previous person in the imagined “supply chain.” We argue that current responsible artificial intelligence interventions, like ethics checklists and guidelines that assume panoptical knowledge and control over systems, could be improved by taking a located accountability approach, recognizing where relations and obligations might intertwine inside and outside of this supply chain.

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“…In addition, since AI fairness efforts were not reflected in most organizations' annual employee evaluations or other organizational incentives, individuals who were motivated to do fairness work often felt responsible for "holding the entire team accountable for the fairness work" (MLE2), rather than having that accountability located in, for instance, "ethics owners" [60] or other organizational leadership. Similarly, MLE1 shared that they felt "frustrated by the attitude of [others] dodging responsibility for fairness work. "…”

Section: Invisible Labor and Burdens In Cross-functional Collaborationmentioning

confidence: 99%

Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice

Deng

Yildirim

Chang

et al. 2023

2023 ACM Conference on Fairness, Accountability, and Transparency

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An emerging body of research indicates that ineffective cross-functional collaboration -the interdisciplinary work done by industry practitioners across roles -represents a major barrier to addressing issues of fairness in AI design and development. In this research, we sought to better understand practitioners' current practices and tactics to enact cross-functional collaboration for AI fairness, in order to identify opportunities to support more effective collaboration. We conducted a series of interviews and design workshops with 23 industry practitioners spanning various roles from 17 companies. We found that practitioners engaged in bridging work to overcome frictions in understanding, contextualization, and evaluation around AI fairness across roles. In addition, in organizational contexts with a lack of resources and incentives for fairness work, practitioners often piggybacked on existing requirements (e.g., for privacy assessments) and AI development norms (e.g., the use of quantitative evaluation metrics), although they worry that these tactics may be fundamentally compromised. Finally, we draw attention to the invisible labor that practitioners take on as part of this bridging and piggybacking work to enact interdisciplinary collaboration for fairness. We close by discussing opportunities for both FAccT researchers and AI practitioners to better support crossfunctional collaboration for fairness in the design and development of AI systems.

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Owning Ethics: Corporate Logics, Silicon Valley, and the Institutionalization of Ethics

Cited by 136 publications

References 0 publications

“That’s important, but...”: How Computer Science Researchers Anticipate Unintended Consequences of Their Research Innovations

“That’s important, but...”: How Computer Science Researchers Anticipate Unintended Consequences of Their Research Innovations

Dislocated accountabilities in the “AI supply chain”: Modularity and developers’ notions of responsibility

Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice

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