Technological innovations have become a key driver of societal advancements. Nowhere is this more evident than in the field of machine learning (ML), which has developed algorithmic models that shape our decisions, behaviors, and outcomes. These tools have widespread use, in part, because they can synthesize massive amounts of data to make seemingly objective recommendations. Yet, in the past few years, the ML community has been drawing attention to the need for caution when interpreting and using these models. This is because these models are created by humans, from data generated by humans, whose psychology allows for various biases that impact how the models are developed, trained, tested, and interpreted. As psychologists, we thus face a fork in the road: Down the first path, we can continue to use these models without examining and addressing these critical flaws and rely on computer scientists to try to mitigate them. Down the second path, we can turn our expertise in bias toward this growing field, collaborating with computer scientists to reduce the models’ deleterious outcomes. This article serves to light the way down the second path by identifying how extant psychological research can help examine and curtail bias in ML models.
Seam Work and Simulacra of Societal Impact in Networking Research: A Critical Technical Practice Approach
This paper explores how conceptions of societal impact are produced and performed during academic computer science research, by leveraging critical technical practice while building a digital agriculture networking platform. Our findings reveal how everyday practices of envisioning and building infrastructure require working across disciplinary and institutional seams, leading us as computer scientists to continuously reconceptualize the intended societal impact. By self-reflectively analyzing how we accrue resources for projects, produce research systems, write about them, and maintain alignments with stakeholders, we demonstrate that this seam work produces shifting simulacra of societal impact around which the system’s success is narrated. HCI researchers frequently suggest that technical systems’ impact could be improved by motivating computer scientists to consider impact in system-building. Our findings show that institutional and disciplinary structures significantly shape how computer scientists can enact societal impact in their work. This work suggests opportunities for structural interventions to shape the impact of computing systems.
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- Award ID(s):
- 1955125
- NSF-PAR ID:
- 10510022
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400703300
- Page Range / eLocation ID:
- 1 to 19
- Format(s):
- Medium: X
- Location:
- Honolulu HI USA
- Sponsoring Org:
- National Science Foundation
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