Becoming a real data scientist

Lowrie, Ian

doi:10.7765/9781526127600.00010

Ethnography for a Data-Saturated World 2018

DOI: 10.7765/9781526127600.00010

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Becoming a real data scientist

Ian Lowrie¹

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2024

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Introduction: Towards an anthropology of data

Douglas‐Jones

Walford

Seaver

2021

Royal Anthropological Inst

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The world is talking ‘data’. The early cross‐disciplinary, business‐orientated hype around the potential of ‘big’ data, with its promises of unprecedented insight into social life, has given way. Data now motivates a sweep of dystopian visions, from rampant commodification to the invasion of privacy, political manipulation, and shadowy data doubles. Yet anthropologists have been cautious in taking data itself as their object, even as the social life of data practices becomes manifest in our ethnographies. In this introduction, we argue for an anthropology of data that is ethnographically specific and theoretically ambitious, putting forward a case for why anthropological engagements with the data moment might be not only politically important but also conceptually generative.

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Introduction: Towards an anthropology of data

Douglas‐Jones

Walford

Seaver

2021

Royal Anthropological Inst

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Anthropology by Data Science

Paff¹

2021

Annals of Anthropol Pract

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This essay explores how to broaden the scope of what constitutes anthropological and ethnographic research by cross‐fertilizing with data science. I discuss four types of relationships anthropologists have sought to foster with data science: anthropology of data science, anthropology over data science, anthropology with data science, and, the least developed of the four, anthropology by data science. Data science as a field has cultivated abductive, bottom‐up forms of quantitative research, which provide useful quantitative parallels to similarly abductive, bottom‐up qualitative techniques in ethnographic research. Anthropologists should adopt an anthropology by data science perspective through incorporating machine‐learning and other data science techniques into anthropological research. Nick Seaver's concept of bastard disciplines and methodologies provides a helpful framework for such work. [data science, machine learning, ethnography, anthropology]

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Are They Doing Artificial Intelligence? (Re)Constructing the Primary Activity in Data Science

Żulicki

2024

PSJ

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Data science (DS) is concerned with building so-called artificial intelligence, i.e., computer systems that automate tasks based on historical data. This article is the first attempt to examine DS using Adele E. Clarke’s framework of social worlds. The main goal of this paper is to show the (re)construction of primary activity based on the example of the social world of DS in Poland. Methodological reflection on this (re)construction is an underdeveloped element in the study of social worlds; therefore, this paper strives to make this process explicit. The empirical background is a three-year ethnographic study, following Clarke’s situational analysis approach. The methodological results demonstrate the indispensability of collaborative ethnography in (re)constructing primary activity and the importance of finding palpable elements as those being crucial to understanding primary activity. The substantive results focus on the idea that data scientists do not refer to their activity as doing artificial intelligence.

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Becoming a real data scientist

Cited by 3 publications

References 0 publications

Introduction: Towards an anthropology of data

Introduction: Towards an anthropology of data

Anthropology by Data Science

Are They Doing Artificial Intelligence? (Re)Constructing the Primary Activity in Data Science

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