Datafication and data fiction: Narrating data and narrating with           data

Dourish, Paul; Cruz, Edgar Gómez

doi:10.1177/2053951718784083

Cited by 140 publications

(100 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…HCI and CSCW researchers have studied how data scientists work with their data (e.g., [11,46,48,50,53,54]). Passi and Jackson examined how data science workers deal with rules with doing data science and concluded that most scholars use rules as a basis and framework ("rules-based") but not as a set of required ways-of-working (i.e., not "rules-bound") [53].…”

Section: Human Intervention In Data Sciencementioning

confidence: 99%

Human-AI Collaboration in Data Science

Wang

Weisz

Müller

et al. 2019

Proc. ACM Hum.-Comput. Interact.

210

View full text Add to dashboard Cite

The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One application domain is data science. New techniques in automating the creation of AI, known as AutoAI or AutoML, aim to automate the work practices of data scientists. AutoAI systems are capable of autonomously ingesting and pre-processing data, engineering new features, and creating and scoring models based on a target objectives (e.g. accuracy or run-time efficiency). Though not yet widely adopted, we are interested in understanding how AutoAI will impact the practice of data science. We conducted interviews with 20 data scientists who work at a large, multinational technology company and practice data science in various business settings. Our goal is to understand their current work practices and how these practices might change with AutoAI. Reactions were mixed: while informants expressed concerns about the trend of automating their jobs, they also strongly felt it was inevitable. Despite these concerns, they remained optimistic about their future job security due to a view that the future of data science work will be a collaboration between humans and AI systems, in which both automation and human expertise are indispensable.

show abstract

Section: Human Intervention In Data Sciencementioning

confidence: 99%

Human-AI Collaboration in Data Science

Wang

Weisz

Müller

et al. 2019

Proc. ACM Hum.-Comput. Interact.

210

View full text Add to dashboard Cite

show abstract

“…Kitchin (2013) also makes a similar point, noting the underpinning assumptions of exhaustible data collection (where n = all) and maximal data resolution (greatest detail possible) that drive many big data ventures. Assumptions also dominate the field of data science (the growing disciplinary and professional body who work with these systems), with Forsythe (1993) describing how the work of doing AI and the epistemological assumptions behind AI are steeped in tacit assumptions that shape products and underlying knowledge of the system (Dourish, 2016;Dourish & Gómez Cruz, 2018;Frické, 2015;Leonelli, Rappert, & Davies, 2017;Lowrie, 2017). In other words, there are several social assumptions that underlie data abstraction techniques that are disguised or "black-boxed" by their function.…”

Section: Data Abstractionmentioning

confidence: 99%

The myth of the “data‐driven” society: Exploring the interactions of data interfaces, circulations, and abstractions

Lee

Cook

2019

Sociology Compass

View full text Add to dashboard Cite

The prominence of data and data technologies in society, such as algorithms, social media, mobile technology, and artificial intelligence, have heralded numerous claims of the revolutionary potential of these systems. From public policy, to business management, to scientific research, a “data‐driven” society is apparently imminent—or currently happening—where “objective” and asocial data systems are believed to be comprehensively improving human life. Through a review of existing sociological literature, in this article, we critically examine the relationship between data and society and propose a new model for understanding these dynamics. “Using the concept of the informatic,” we argue the relationship between data and society can be understood as representing the interaction of several different social trends around data; that of data interfaces (that connect individuals to digital contexts), data circulation (trends in the movement and storage of data), and data abstraction (data manipulation practices). Data and data technologies are founded to be entwined and embedded in numerous social relationships, and while not all are fair and equitable relationships, there is ample evidence of the deeply social nature of data across many streams of social life. Our three‐part informatic framework allows these complex relationships to be understood in the social dynamic through which they are witnessed and experienced.

show abstract

“…The second class of systems and activities aligns with practices in healthcare, where citizens' (or patient) records are used in the instrumental role of tending to the individual and informing the activities of the care professionals who orbit that individual while they recover [5,27,49,50,54]. Similar activities take place in public services, where social workers enter and track data about individuals to support their progress toward stability.…”

Section: Differences In Classes Of Systemsmentioning

confidence: 99%

Assembling the Case

Møller

Fitzpatrick

Dantec

2019

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

This study examines caseworkers' and citizens' interactions when assembling resource development applications for citizens with serious health and personal issues. As with other types of welfare schemes, the application serves as a mechanism of both support and control. From our study, we illustrate how an increased reliance on data is transforming the citizen-caseworker interaction in social welfare. We characterize this transformation as 'datafication': a phenomenon where the increased reliance on data for decision-support across contexts of data production makes it challenging for individual citizens to contest or correct data-born accounts of their situation. Our contribution is two-fold: first, we empirically characterize the citizen-caseworker interaction in the application process. Second, we discuss how citizens' private resourcing complements the formal application process and provides them with strategies to give authority to their case and exercise personal autonomy. The private resourcing practices we observed show how integrating supplementary accounts from citizens into the systems that caseworkers rely on could make citizens' experiences and social context legible. This in turn has policy and technology design implications as public services increasingly introduce data-driven modes of case management.

show abstract

Datafication and data fiction: Narrating data and narrating with data

Cited by 140 publications

References 35 publications

Human-AI Collaboration in Data Science

Human-AI Collaboration in Data Science

The myth of the “data‐driven” society: Exploring the interactions of data interfaces, circulations, and abstractions

Assembling the Case

Contact Info

Product

Resources

About