Although many data scientists are eager to promote social justice, their efforts often fall short, and in some cases even exacerbate existing injustices. This gap between intentions and impacts follows from issues in how data scientists conceptualize their role in social justice efforts: they typically act as technicians, aiming to develop technical tools that will produce social justice. This default role generally results in depoliticized techno-fixes that fail to produce substantive changes. In this paper, we contribute “movement data science” as a framework to guide data scientists in taking on more productive and equitable roles in social justice efforts. Our framework is inspired by movement lawyering, a practice used by lawyers to situate themselves in grassroots social movements. Movement data science suggests that data scientists should ground their efforts to advance justice in sustained and solidaristic relationships with social movements. We contribute a process with four essential practices to help data scientists build these relationships and identify roles to play within specific social justice efforts. Movement data science does not provide a simple blueprint for data scientists to follow, but it can help them support social justice movements in substantive ways that go beyond attempting to solve injustices with technical tools.
Data Feminism
Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to d...
Data Feminism
A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.Today, data science is a form of power. I
Qualitative Research in an Era of Artificial Intelligence: A Pragmatic Approach to Data Analysis, Workflow, and Computation
Computational developments—particularly artificial intelligence—are reshaping social scientific research and raising new questions for in-depth methods such as ethnography and qualitative interviewing. Building on classic debates about computers in qualitative data analysis, we revisit possibilities and dangers in an era of automation, large language model chatbots, and big data. We introduce a typology of contemporary approaches to using computers in qualitative research: streamlining workflows, scaling up projects, hybrid analytical methods, the sociology of computation, and technological rejection. Drawing from scaled team ethnographies and solo research integrating computational social science alongside in-depth observation, we describe methodological choices across study life cycles, from literature reviews through data collection, coding, text retrieval, and representation. We argue that new technologies hold potential to address long-standing methodological challenges when deployed with knowledge, purpose, and ethical commitment. Yet, a pragmatic approach—moving beyond technological optimism and dismissal—is essential given rapidly changing tools that are both generative and dangerous. Computation now saturates research infrastructure, from algorithmic literature searches to scholarly metrics, making computational literacy a core methodological competence in and beyond sociology. We conclude that when used carefully and transparently, contemporary computational tools can meaningfully expand, rather than displace, the irreducible insights of qualitative research.
Democratizing Data
Democratizing Data builds a community-driven data ecosystem by identifying how datasets are used and reducing barriers to accessing high-quality public data. The initiative enhances the discoverability, usability, and relevance of data for researchers, policymakers, and stakeholder communities. A suite of tools and strategic partnerships supports this work by connecting users to the data, insights, and networks needed to inform decisions and generate impact.
A Polycentric Governance Lens on Data Infrastructures
Funding policies for data infrastructure promote open data sharing to drive positive social impact. However, concerns regarding the long-term management of data within and across distributed infrastructures can hinder data sharing. We draw upon the concept of polycentric governance to demonstrate how collaborative practices of data curation, in preparing and maintaining data for (future) sharing, provide a solid foundation for understanding data governance within data infrastructures. Based on a qualitative case study of a distributed ecological network, we investigate the conditions under which data are managed as a shared resource by local actors to ensure the long-term (re)usability of data. We contribute to CSCW by conceptualising data curation as a complex form of governance practice with multiple centres of decision-making, each of which operates with some degree of autonomy in data infrastructures. A polycentric governance lens on data infrastructures advances the CSCW conception of data curation as a collective governance practice that can cultivate a data democracy culture within and across organisations, empower individuals to be accountable for their data, and foster a mindset shift toward decentralised data governance.
Collaboratory at Columbia: An Aspen Grove of Data Science Education · Issue 3.4, Fall 2021
Many universities recognize the rapidly growing impact of data science in all fields of study and the professions and seek to embed this expertise widely across their educational offerings. There is often broad interest in developing new data science curricula, with some universities even allocating funds toward this purpose. Yet, it is often unclear what resources are needed for effective data science education, and how resources ought to be prioritized. Although university leadership might be aware of a growing number of successful data science acceleration programs and pedagogical models, many of which are either general purpose or specific to a particular discipline, there remains a lack of clarity about how these models might address their own specific needs. This article presents the Collaboratory Program at Columbia University (termed “the Collaboratory”), which is both a set of “data science in context” educational approaches, as well as a meta-model for an accelerator program that allows different institutions to respond flexibly to their own disciplinary heterogeneity in terms of data science educational needs. The novelty of the Collaboratory lies in its crowd-sourcing approach to creating new data science pedagogy and its ability to kindle transdisciplinary collaboration in doing so. By offering seed funding, it fosters proactive efforts to embed data science “in context” into more traditional domains through a cohort of compelling, transdisciplinary, crowd-sourced data science education proposals each year. Collaboratory educational offerings are required to be developed through a partnership between two faculty members, a data scientist and a domain expert from another field, or a larger team with complementary expertise. Over the past 5 years, the Collaboratory has supported the development of a wide spectrum of data science pedagogical models spread across more than 40 academic departments, centers, institutes, and professional schools at Columbia University. As a result, the Collaboratory has to date served the learning needs of more than 4,000 students. Furthermore, it has cultivated a thriving ecosystem that includes a funding mechanism and a community-support structure that all contribute to its agility and success. Here, we offer our experience and best practices in developing and managing the Collaboratory, which, we hope, will contribute to a blueprint for data science education leaders everywhere.
Data Feminism
A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.
Feminist Data Manifest-No
1. We refuse to operate under the assumption that risk and harm associated with data practices can be bounded to mean the same thing for everyone, everywhere, at every time. We commit to acknowledging how historical and systemic patterns of violence and exploitation produce differential vulnerabilities for communities.
Data Scientist: The Sexiest Job of the 21st Century
Back in the 1990s, computer engineer and Wall Street “quant” were the hot occupations in business. Today data scientists are the hires firms are competing to make. As companies wrestle with unprecedented volumes and types of information, demand for these experts has raced well ahead of supply. Indeed, Greylock Partners, the VC firm that backed Facebook and LinkedIn, is so worried about the shortage of data scientists that it has a recruiting team dedicated to channeling them to the businesses in its portfolio. Data scientists are the key to realizing the opportunities presented by big data. They bring structure to it, find compelling patterns in it, and advise executives on the implications for products, processes, and decisions. They find the story buried in the data and communicate it. And they don’t just deliver reports: They get at the questions at the heart of problems and devise creative approaches to them. One data scientist who was studying a fraud problem, for example, realized it was analogous to a type of DNA sequencing problem. Bringing those disparate worlds together, he crafted a solution that dramatically reduced fraud losses. In this article, Harvard Business School’s Davenport and Greylock’s Patil take a deep dive on what organizations need to know about data scientists: where to look for them, how to attract and develop them, and how to spot a great one.
Professionalising Community Management Roles in Interdisciplinary Research Projects
In this article we discuss community management in interdisciplinary research teams, focusing on recognising and professionalising roles referred to here as the Research Community Managers (RCM). Drawing insights and examples from research and data science projects, we discuss how RCM roles address some of the researchâs most pressing challenges, from promoting best practices for open research and reproducibility to engaging diverse stakeholders in community-led research and ensuring fair recognition for their contributions. We offer a Community Maturation Indicator and share examples of projects from The Alan Turing Institute, the UK's national institute for data science and Artificial Intelligence (AI), where institutionally supported RCM roles were established. With the aim to integrate RCM expertise in teams involved in data science and AI research, we provide an RCM Skills and Competencies Framework. We also propose a roadmap for professionalising RCM roles by improving recognition and rewards, potential career paths and organisational support structures. To systematically sustain and progress these roles, we recommend institutional investment in establishing RCM teams that are empowered to prioritise collaboration, transparency and community-based approaches in interdisciplinary projects, such as in data science and AI. As a team, RCMs are well placed to connect disparate teams, initiatives and resources across the organisation, building more resilient research communities that can achieve greater innovation, improved project outcomes and a strongly connected ecosystem, with impacts extending beyond their narrow contexts.
We Need A New Kind Of Data Rights
Associational data rights would empower communities to reclaim power from Big Tech
Doing Data Science on the Shoulders of Giants: The Value of Open Source Software for the Data Science Community · Issue 2.2, Spring 2020
Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.
Doing Data Science on the Shoulders of Giants: The Value of Open Source Software for the Data Science Community
Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.
Data Refusal from Below: A Framework for Understanding, Evaluating, and Envisioning Refusal as Design
Amidst calls for public accountability over large data-driven systems, feminist and indigenous scholars have developed refusal as a practice that challenges the authority of data collectors. However, because data affect so many aspects of daily life, it can be hard to see seemingly different refusal strategies as part of the same repertoire. Furthermore, conversations about refusal often happen from the standpoint of designers and policymakers rather than the people and communities most affected by data collection. In this article, we introduce a framework for data refusal from below —writing from the standpoint of people who refuse, rather than the institutions that seek their compliance. Because refusers work to reshape socio-technical systems, we argue that refusal is an act of design and that design-based frameworks and methods can contribute to refusal. We characterize refusal strategies across four constituent facets common to all refusal, whatever strategies are used: autonomy , or how refusal accounts for individual and collective interests; time , or whether refusal reacts to past harm or proactively prevents future harm; power , or the extent to which refusal makes change possible; and cost , or whether or not refusal can reduce or redistribute penalties experienced by refusers. We illustrate each facet by drawing on cases of people and collectives that have refused data systems. Together, the four facets of our framework are designed to help scholars and activists describe, evaluate, and imagine new forms of refusal.
Civic, Citizen and Grassroots Science: Towards a Transformative Scientific Research Model | Request PDF
Request PDF | On Jan 1, 2013, Jessica McCallum Breen and others published Civic, Citizen and Grassroots Science: Towards a Transformative Scientific Research Model | Find, read and cite all the research you need on ResearchGate
Can “Conscious Data Contribution” Help Users to Exert “Data Leverage” Against Technology Companies?
Tech users currently have limited ability to act on concerns regarding the negative societal impacts of large tech companies. However, recent work suggests that users can exert leverage using their role in the generation of valuable data, for instance by withholding their data contributions to intelligent technologies. We propose and evaluate a new means to exert this type of leverage against tech companies: "conscious data contribution" (CDC).