From FAIR data to fair data use: Methodological data fairness in health-related social media research

Leonelli, Sabina; Lovell, Rebecca; Wheeler, Benedict W.; Fleming, Lora E.; Williams, Hywel T. P.

doi:10.1177/20539517211010310

Cited by 28 publications

(32 citation statements)

References 74 publications

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“…Related problems, such as using other "open" data sources to carry out research, e.g., from social media, [70] are also beyond the scope of this article. The same goes for the subversion of data for many reasons, such as hiding poor research integrity or avoiding competing research groups.…”

Section: Discussionmentioning

confidence: 99%

Open Science Knowledge Production: Addressing Epistemological Challenges and Ethical Implications

Hofmann

2022

Publications

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Open Science (OS) is envisioned to have a wide range of benefits including being more transparent, shared, accessible, and collaboratively developed than traditional science. Despite great enthusiasm, there are also several challenges with OS. In order to ensure that OS obtains its benefits, these challenges need to be addressed. Accordingly, the objective of this study is to provide an overview of one type of challenge, i.e., epistemological challenges with OS knowledge production, and their ethical implications. Based on a literature review, it (a) reveals factors undermining the envisioned benefits of OS, (b) identifies negative effects on knowledge production, and (c) exposes epistemological challenges with the various phases of the OS process. The main epistemic challenges are related to governance, framing, looping effects, proper data procurement, validation, replication, bias, and polarization. The ethical implications are injustice, reduced benefit (efficiency), increased harm (as a consequence of poor-quality science), deception and manipulation (reduced autonomy), and lack of trustworthiness. Accordingly, to obtain the envisioned benefits of OS, we need to address these epistemological challenges and their ethical implications.

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Section: Discussionmentioning

confidence: 99%

Open Science Knowledge Production: Addressing Epistemological Challenges and Ethical Implications

Hofmann

2022

Publications

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“…principles (Findable, Accessible, Interoperable, Reproducible) ( Wilkinson et al ., 2016 ), achieving F.A.I.R. data, does not necessarily result in fairness in data use ( Leonelli et al ., 2021 ). Indeed, enabling greater data use for global genomics science may cause inequities and inequalities where data use or sharing is undertaken without fully accounting for diversity, inclusion and equality.…”

Section: Developing Engagement Through Deliberative Reflectionmentioning

confidence: 99%

Engaged genomic science produces better and fairer outcomes: an engagement framework for engaging and involving participants, patients and publics in genomics research and healthcare implementation

Murtagh

Machirori²,

Gaff

et al. 2021

Wellcome Open Res

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Genomic science is increasingly central to the provision of health care. Producing and applying robust genomics knowledge is a complex endeavour in which no single individual, profession, discipline or community holds all the answers. Engagement and involvement of diverse stakeholders can support alignment of societal and scientific interests, understandings and perspectives and promises better science and fairer outcomes. In this context we argue for F.A.I.R.E.R. data and data use that is Findable, Accessible, Interoperable, Reproducible, Equitable and Responsible. Yet there is a paucity of international guidance on how to engage publics, patients and participants in genomics. To support meaningful and effective engagement and involvement we developed an Engagement Framework for involving and engaging participants, patients and publics in genomics research and health implementation. The Engagement Framework is intended to support all those working in genomics research, medicine, and healthcare to deliberatively consider approaches to participant, patient and public engagement and involvement in their work. Through a series of questions, the Engagement Framework prompts new ways of thinking about the aims and purposes of engagement, and support reflection on the strengths, limitations, likely outcomes and impacts of choosing different approaches to engagement. To guide genomics activities, we describe four themes and associated questions for deliberative reflection: (i) fairness; (ii) context; (iii) heterogeneity, and (iv) recognising tensions and conflict. The four key components in the Engagement provide a framework to assist those involved in genomics to reflect on decisions they make for their initiatives, including the strategies selected, the participant, patient and public stakeholders engaged, and the anticipated goals. The Engagement Framework is one step in an actively evolving process of building genomics research and implementation cultures which foster responsible leadership and are attentive to objectives which increase equality, diversity and inclusion in participation and outcomes.

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“…scraped off the public web or pulled from a database), the similar basic principles should be considered (7,22). Even without any explicit data collection, a data scientist tasked with developing an algorithm has ethical considerations to make, for example with regard to bias, fairness, and accountability (16,36,53). Recently, there has also been discussion of data science oaths, similar to the Hippocratic oath taken by doctors, that would give data scientists explicit priniciples to which they would pledge to adhere (37,41).…”

Section: Data Science Ethicsmentioning

confidence: 99%

Perspective on Data Science

Peng¹,

Parker²

2021

Preprint

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The field of data science currently enjoys a broad definition that includes a wide array of activities which borrow from many other established fields of study. Having such a vague characterization of a field in the early stages might be natural, but over time maintaining such a broad definition becomes unwieldy and impedes progress.In particular, the teaching of data science is hampered by the seeming need to cover many different points of interest. Data scientists must ultimately identify the core of the field by determining what makes the field unique and what it means to develop new knowledge in data science. In this review we attempt to distill some core ideas from data science by focusing on the iterative process of data analysis and develop some generalizations from past experience. Generalizations of this nature could form the basis of a theory of data science and would serve to unify and scale the teaching of data science to large audiences.

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From FAIR data to fair data use: Methodological data fairness in health-related social media research

Cited by 28 publications

References 74 publications

Open Science Knowledge Production: Addressing Epistemological Challenges and Ethical Implications

Open Science Knowledge Production: Addressing Epistemological Challenges and Ethical Implications

Engaged genomic science produces better and fairer outcomes: an engagement framework for engaging and involving participants, patients and publics in genomics research and healthcare implementation

Perspective on Data Science

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