Taking a critical look at the critical turn in data science: From “data feminism” to transnational feminist data science

Alicia E. Boyd Hana Winchester Brittany Johnson

1 Mei 2022

The growing ubiquity of machine learning technologies has led to concern and concentrated efforts at improving data-centric research and practice. While much work has been done on addressing equity concerns with respect to unary identities (e.g, race or gender), little to no work in Software Engineering has studied intersectionality to determine how we can provide equitable outcomes for complex, overlapping social identities in data-driven tech. To this end, we designed a survey to learn the landscape of intersectional identities in tech, where these populations contribute data, and how marginalized populations feel about the impact technology has on their day to day lives. Our data thus far, collected from 12 respondents and composed mostly of white and male identities, further highlights the lack of representation in modern data sets and need for contributions that explicitly explore how to support data-driven research and development. ACM Reference Format: Hana Winchester, Alicia E. Boyd, and Brittany Johnson. 2022. An Exploration of Intersectionality in Software Development and Use. In Third Workshop on Gender Equality, Diversity, and Inclusion in Software Engineering (GE@ICSE’22), May 20, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3524501.3527605

Michael Zimmer Katie Shilton Matthew J. Bietz + 5 lainnya

1 Juli 2021

Frequent public uproar over forms of data science that rely on information about people demonstrates the challenges of defining and demonstrating trustworthy digital data research practices. This paper reviews problems of trustworthiness in what we term pervasive data research: scholarship that relies on the rich information generated about people through digital interaction. We highlight the entwined problems of participant unawareness of such research and the relationship of pervasive data research to corporate datafication and surveillance. We suggest a way forward by drawing from the history of a different methodological approach in which researchers have struggled with trustworthy practice: ethnography. To grapple with the colonial legacy of their methods, ethnographers have developed analytic lenses and researcher practices that foreground relations of awareness and power. These lenses are inspiring but also challenging for pervasive data research, given the flattening of contexts inherent in digital data collection. We propose ways that pervasive data researchers can incorporate reflection on awareness and power within their research to support the development of trustworthy data science.

Warmhold Jan Thomas Mollema

23 Mei 2024

The development and deployment of machine learning and AI engender 'AI colonialism', a term that conceptually overlaps with 'data colonialism', as a form of injustice. AI colonialism is in need of decolonization for three reasons. Politically, because it enforces digital capitalism's hegemony. Ecologically, as it negatively impacts the environment and intensifies the extraction of natural resources and consumption of energy. Epistemically, since the social systems within which AI is embedded reinforce Western universalism by imposing Western colonial values on the global South when these manifest in the digital realm is a form of digital capitalism. These reasons require a new conceptualization of AI decolonization. First this paper draws from the historical debates on the concepts of colonialism and decolonization. Secondly it retrieves Achille Mbembe's notion of decolonization as disenclosure to argue that the decolonization of AI will have to be the abolishment of political, ecological and epistemic borders erected and reinforced in the phases of its design, production, development of AI in the West and drawing from the knowledge from the global South. In conclusion, it is discussed how conceiving of decolonial AI as form of disenclosure opens up new ways to think about and intervene in colonial instantiations of AI development and deployment, in order to empower 'the wretched of AI', re-ecologise the unsustainable ecologies AI depends on and to counter the colonial power structures unreflective AI deployment risks to reinforce.

Kristoffer Lind Glavind Andreas Bjerre-Nielsen

1 Januari 2022

Big data enables researchers to closely follow the behavior of large groups of individuals by using high-frequency digital traces. However, these digital traces often lack context, and it is not always clear what is measured. In contrast, data from ethnographic fieldwork follows a limited number of individuals but can provide the context often lacking from big data. Yet, there is an under-explored potential in combining ethnographic data with big data and other digital data sources. This paper presents ways that quantitative research designs can combine big data and ethnographic data and account for the synergies that such combinations can provide. We highlight the differences and similarities between ethnographic data and big data, focusing on the three dimensions: individuals, depth of information, and time. We outline how ethnographic data can validate big data by providing a “ground truth” and complement it by giving a “thick description.” Further, we lay out ways that analysis carried out using big data could benefit from collaboration with ethnographers, and we discuss the potential within the fields of machine learning and causal inference.

Michelle Wilkerson Kathryn A. Lanouette Victor R. Lee

23 September 2021

There is growing interest in how to better prepare K–12 students to work with data. In this article, we assert that these discussions of teaching and learning must attend to the human dimensions of data work. Specifically, we draw from several established lines of research to argue that practices involving the creation and manipulation of data are shaped by a combination of personal experiences, cultural tools and practices, and political concerns. We demonstrate through two examples how our proposed humanistic stance highlights ways that efforts to make data personally relevant for youth also necessarily implicate cultural and sociopolitical dimensions that affect the design and learning opportunities in data-rich learning environments. We offer an interdisciplinary framework based on literature from multiple bodies of educational research to inform design, teaching and research for more effective, responsible, and inclusive student learning experiences with and about data.