Overseeing Research Practice

Leahey, Erin

doi:10.1177/0162243907306702

Cited by 22 publications

(4 citation statements)

References 51 publications

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“…In sum, provenance requires more than a bias assessment that measures predictive accuracy across protected groups. In particular, far less attention has been paid to how complex social realities are transformed into algorithmic systems and the normative assumptions that drive these processes 125–127. For example, rather than define ‘fairness’ as a fixed attribute, the literature we reviewed emphasised that it is a value-laden social and political determination made by individuals or groups of people within specific contexts.…”

Section: Resultsmentioning

confidence: 99%

Conceptualising fairness: three pillars for medical algorithms and health equity

Sikstrom

Maslej

Hui

et al. 2022

BMJ Health Care Inform

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ObjectivesFairness is a core concept meant to grapple with different forms of discrimination and bias that emerge with advances in Artificial Intelligence (eg, machine learning, ML). Yet, claims to fairness in ML discourses are often vague and contradictory. The response to these issues within the scientific community has been technocratic. Studies either measure (mathematically) competing definitions of fairness, and/or recommend a range of governance tools (eg, fairness checklists or guiding principles). To advance efforts to operationalise fairness in medicine, we synthesised a broad range of literature.MethodsWe conducted an environmental scan of English language literature on fairness from 1960-July 31, 2021. Electronic databases Medline, PubMed and Google Scholar were searched, supplemented by additional hand searches. Data from 213 selected publications were analysed using rapid framework analysis. Search and analysis were completed in two rounds: to explore previously identified issues (a priori), as well as those emerging from the analysis (de novo).ResultsOur synthesis identified ‘Three Pillars for Fairness’: transparency, impartiality and inclusion. We draw on these insights to propose a multidimensional conceptual framework to guide empirical research on the operationalisation of fairness in healthcare.DiscussionWe apply the conceptual framework generated by our synthesis to risk assessment in psychiatry as a case study. We argue that any claim to fairness must reflect critical assessment and ongoing social and political deliberation around these three pillars with a range of stakeholders, including patients.ConclusionWe conclude by outlining areas for further research that would bolster ongoing commitments to fairness and health equity in healthcare.

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Section: Resultsmentioning

confidence: 99%

Conceptualising fairness: three pillars for medical algorithms and health equity

Sikstrom

Maslej

Hui

et al. 2022

BMJ Health Care Inform

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“…Some specific data preparation tasks with close affinities to statistical theory (especially missing data) have developed a substantial research literature, but the overall process of preparing data for analysis remains somewhat ad hoc and without a general methodological literature. Future empirical research might build on existing studies of researcher beliefs about data preparation (Leahey et al 2003;Leahey 2008) by studying regularities in the process of data preparation and quantifying the impacts that data preparation decisions have on estimates. Complementary theoretical research might enrich the connections between stages in the data pipeline and show how data preparation choices can be as important as data collection and modeling choices.…”

Section: The Future Of Metadata Systemsmentioning

confidence: 99%

Improving Metadata Infrastructure for Complex Surveys: Insights from the Fragile Families Challenge

Kindel

Bansal

Catena

et al. 2019

Socius: Sociological Research for a Dynamic World

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Researchers rely on metadata systems to prepare data for analysis. As the complexity of data sets increases and the breadth of data analysis practices grow, existing metadata systems can limit the efficiency and quality of data preparation. This article describes the redesign of a metadata system supporting the Fragile Families and Child Wellbeing Study on the basis of the experiences of participants in the Fragile Families Challenge. The authors demonstrate how treating metadata as data (i.e., releasing comprehensive information about variables in a format amenable to both automated and manual processing) can make the task of data preparation less arduous and less error prone for all types of data analysis. The authors hope that their work will facilitate new applications of machine-learning methods to longitudinal surveys and inspire research on data preparation in the social sciences. The authors have open-sourced the tools they created so that others can use and improve them.

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“…Scientific norms are still emerging for data management and data deposit. For example, Leahey (2008) found that social scientists lack shared norms for editing data. Granted, many centralized resources do exist to guide the process of managing and depositing qualitative data.…”

Section: Introductionmentioning

confidence: 99%

‘Routine Infrastructuring’: How Social Scientists Appropriate Resources to Deposit Qualitative Data to ICPSR and Implications for FAIR and CARE

Bratt

2023

Proceedings of the Association for Information Science and Tech

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This study develops a grounded theory of how social scientists facilitate qualitative data deposit and the impacts on making data FAIR and CARE. Drawing from 15 semi‐structured interviews with U.S. academic social science faculty who deposited data to ICPSR, I take a resource‐centric perspective to address the need for theorizing scientists' use of resources to bridge the gap between underspecified, heterogeneous data practices and repository requirements. The two primary contributions of the study are: First, the identification of three types of resources that social science faculty use to structure data deposit routines, namely: 1) bottom‐up, 2) top‐down, and 3) borrowed resources. Second, I import a theory from crisis informatics, ‘routine infrastructuring,’ to explain how social scientists deposit data to ICPSR. Results reveal that the resources social scientists use function as ostensive routines. I argue routine infrastructuring is not only a way to enact routines but also creates routines. Findings also show ‘in‐house’ resources have a mix of beneficial and negative impacts for data FAIR‐ and CARE‐ness. This study advances the small but growing body of literature that examines routine dynamics in research groups from a resource‐centric perspective to explain qualitative data deposit to research data repositories.

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Overseeing Research Practice

Cited by 22 publications

References 51 publications

Conceptualising fairness: three pillars for medical algorithms and health equity

Conceptualising fairness: three pillars for medical algorithms and health equity

Improving Metadata Infrastructure for Complex Surveys: Insights from the Fragile Families Challenge

‘Routine Infrastructuring’: How Social Scientists Appropriate Resources to Deposit Qualitative Data to ICPSR and Implications for FAIR and CARE

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