Mental health-related data generated by app users during the routine use of Consumer Mental Health Apps (CMHAs) are being increasingly leveraged for research and product improvement studies. However, it remains unclear which ethical safeguards and practices should be implemented by researchers and app developers to protect users during these studies, and concerns have been raised over their current implementation in CMHAs. To better understand which ethical safeguards and practices are implemented, why and how, 17 app developers and researchers were interviewed (...) who had been involved in using CMHA data for studies. Interviewees discussed the impact on stakeholder interests, sufficiency thresholds and procedural alterations of informed consent, data protection, gathering app user perspectives and representing users in app design and study conduct, and ensuring adequate support. Although the reasoning behind how and why these ethical safeguards and practices should be implemented showed considerable variability and several gaps, interviewees converged on various general lines of reasoning. This allowed for the development of a coherent and nuanced account that could prove useful for future CMHA studies and which could stimulate further normative investigation of the role of ethical safeguards and practices in these studies. (shrink)

This article identifies and examines a tension in mental health researchers’ growing enthusiasm for the use of computational tools powered by advances in artificial intelligence and machine learning (AI/ML). Although there is increasing recognition of the value of participatory methods in science generally and in mental health research specifically, many AI/ML approaches, fueled by an ever-growing number of sensors collecting multimodal data, risk further distancing participants from research processes and rendering them as mere vectors or collections of data points. The (...) imperatives of the “participatory turn” in mental health research may be at odds with the (often unquestioned) assumptions and data collection methods of AI/ML approaches. This article aims to show why this is a problem and how it might be addressed. (shrink)

As the artificial intelligence (AI) ethics field is currently working towards its operationalisation, ethics review as carried out by research ethics committees (RECs) constitutes a powerful, but so far underdeveloped, framework to make AI ethics effective in practice at the research level. This article contributes to the elaboration of research ethics frameworks for research projects developing and/or using AI. It highlights that these frameworks are still in their infancy and in need of a structure and criteria to ensure AI research (...) projects advance in a way that respects norms and principles. This article proposes to draw from the European Union’s AI Act currently in development to shape these frameworks. Although, in the current form of the draft (as of August 2023), the obligations of the AI Act do not apply to scientific research, it is most likely that they will still have a strong impact on AI research considering the need to anticipate market placement or to test new tools in real world conditions. This article investigates what the risk-based approach in the AI Act implies for research ethics and highlights some AI Act obligations of particular value to implement for ethics review processes. (shrink)

Harms and risks to groups and third-parties can be significant in the context of research, particularly in data-centric studies involving genomic, artificial intelligence, and/or machine learning technologies. This article explores whether and how United States federal regulations should be adapted to better align with current ethical thinking and protect group interests. Three aspects of the Common Rule deserve attention and reconsideration with respect to group interests: institutional review board (IRB) assessment of the risks/benefits of research; disclosure requirements in the informed (...) consent process; and criteria for waivers of informed consent. In accordance with respect for persons and communities, investigators and IRBs should systematically consider potential group harm when designing and reviewing protocols, respectively. Research participants should be informed about any potential group harm in the consent process. We call for additional public discussion, empirical research, and normative analysis on these issues to determine the right regulatory and policy path forward. (shrink)

While open science gains prominence in South Africa with the encouragement of open data sharing for research purposes, there are stricter laws and regulations around privacy – and specifically the use, management and transfer of personal information – to consider. The Protection of Personal Information Act No. 4 of 2013 (POPIA), which came into effect in 2021, established stringent requirements for the processing of personal information and has changed the regulatory landscape for the transfer of personal information across South African (...) borders. At the same time, draft national policies on open science encourage wide accessibility to data and open data sharing in line with international best practice. As a result, the operation of research ethics committees (RECs) in South Africa is affected by the conflicting demands of the shift towards open science on the one hand, and the stricter laws protecting participants’ personal information and the transfer thereof, on the other. This article explores the continuing evolving role of RECs in the era of open data and recommends the development of a data transfer agreement (DTA) for the ethical management of personal health information, considering the challenges that RECs encounter, which centres predominantly on privacy, data sharing and access concerns following advances in genetic and genomic research and biobanking. (shrink)

This paper presents an ethical framework designed to support the development of critical data literacy for research methods courses and data training programmes in higher education. The framework we present draws upon our reviews of literature, course syllabi and existing frameworks on data ethics. For this research we reviewed 250 research methods syllabi from across the disciplines, as well as 80 syllabi from data science programmes to understand how or if data ethics was taught. We also reviewed 12 data ethics (...) frameworks drawn from different sectors. Finally, we reviewed an extensive and diverse body of literature about data practices, research ethics, data ethics and critical data literacy, in order to develop a transversal model that can be adopted across higher education. To promote and support ethical approaches to the collection and use of data, ethics training must go beyond securing informed consent to enable a critical understanding of the techno-centric environment and the intersecting hierarchies of power embedded in technology and data. By fostering ethics as a method, educators can enable research that protects vulnerable groups and empower communities. (shrink)

This paper analyzes the ethics of social science research employing big data. We begin by highlighting the research gap found on the intersection between big data ethics, SSR and research ethics. We then discuss three aspects of big data SSR which make it warrant special attention from a research ethics angle: the interpretative character of both SSR and big data, complexities of anticipating and managing risks in publication and reuse of big data SSR, and the paucity of regulatory oversight and (...) ethical recommendations on protecting individual subjects as well as societies when conducting big data SSR. Against this backdrop, we propose using David Resnik’s research ethics framework to analyze some of the most pressing ethical issues of big data SSR. Focusing on the principles of honesty, carefulness, openness, efficiency, respect for subjects, and social responsibility, we discuss three clusters of ethical issues: those related to methodological biases and personal prejudices, those connected to risks arising from data availability and reuse, and those leading to individual and social harms. Finally, we advance considerations to observe in developing future ethical guidelines about big data SSR. (shrink)

This research examines the ethical dimensions of ethical thought aimed at reflecting fundamentals or leading principles of the production and reproduction of knowledge in science and tertiary education. To achieve research goals, the author of this article evaluates the key assumption that statements in the ethics of science and education are transcendental but do not require a reference to a transcendental or metaphysical subject. The author adheres to the stances by Wittgenstein and Moore and defines ethics in terms of the (...) general inquiry into what is good. The ways of forming ethical statements are compared with the main provisions of Merton’s theory of scientific ethos and its effects on the understanding patterns of the production and reproduction of knowledge. A comparison of general types of ethical inquiry and the theory of scientific ethos helps to present the theory of scientific ethos in terms of middle-range ethical theory. The comparison of transcendental statements in ethics and the points of the theory of scientific ethos is related to the issues of the philosophy of education. The relation is due to the exploring the forms and bases of reproduction of scientific society through tertiary education. The production of knowledge in science generates forms of judgments while education reproduces their ethically acceptable patterns of obtaining and applying. As a result, ethical transcendentalism without reference to a transcendental subject inevitably emphasizes the dialogical design of teaching. This design includes diversification of norms and values in science. Diversity as such ensures collective methods of decision-making and opposes any authoritarianism in education. (shrink)

This paper aims to provide a brief overview of the ethical challenges facing researchers engaging with web archival materials and demonstrates a framework and method for conducting research with historical web data created by young people.,This paper’s methodology is informed by the conceptual framing of data materials in research on the “right to be forgotten” (Crossen-White, 2015; GDPR, 2018; Tsesis, 2014), data afterlives (Agostinho, 2019; Stevenson and Gehl, 2019; Sutherland, 2017), indigenous data sovereignty and governance (Wemigwans, 2018) and feminist ethics (...) of care (Cifor et al., 2019; Cowan, 2020; Franzke et al., 2020; Luka and Millette, 2018). It demonstrates a new method called an archive promenade, which builds on the walkthrough and scroll-back methods (Light et al., 2018; Robards and Lincoln, 2017).,The archive promenades demonstrate how individual attachments to digital traces vary and are often unpredictable, which necessitates further steps to ensure that privacy and data sovereignty are maintained through research with web archives.,This paper demonstrates how the archive promenade methodological intervention can lead to better practices of care with sensitive web materials and brings together previous work on ethical fabrications (Markham, 2012), speculation (Luka and Millette, 2018) and thick context (Marzullo et al., 2018), to yield new insights for research on the experiences of growing up online. (shrink)

Purpose This study advances a reconceptualization of data and information which overcomes normative understandings often contained in data policies at national and international levels. This study aims to propose a conceptual framework that moves beyond subject- and collective-centric normative understandings. Design/methodology/approach To do so, this study discusses the European Union (EU) and China’s approaches to data-driven technologies highlighting their similarities and differences when it comes to the vision underpinning how tech innovation is shaped. Findings Regardless of the different attention to (...) the subject (the EU) and the collective (China), the normative understandings of technology by both actors remain trapped into a positivist approach that overlooks all that is not and cannot be turned into data, thus hindering the elaboration of a more holistic ecological thinking merging humans and technologies. Originality/value Revising the philosophical and political debate on data and data-driven technologies, a third way is elaborated, i.e. federated data as commons. This third way puts the subject as part by default of a collective at the centre of discussion. This framing can serve as the basis for elaborating sociotechnical alternatives when it comes to define and regulate the mash-up of humans and technology. (shrink)

The paper explores the potential and limitations of big data for researching racism on social media. Informed by critical data studies and critical race studies, the paper discusses challenges of doing big data research and the problems of the so called ‘white method’. The paper introduces the following three types of approach, each with a different epistemological basis for researching racism in digital context: 1) using big data analytics to point out the dominant power relations and the dynamics of racist (...) discourse, 2) complementing big data with qualitative research and 3) revealing new logics of racism in datafied context. The paper contributes to critical data and critical race studies by enhancing the understanding of the possibilities and limitations of big data research. This study also highlights the importance of contextualisation and mixed methods for achieving a more nuanced comprehension of racism and discrimination on social media and in large datasets. (shrink)

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. (shrink)

Over the last decade, ‘data scientists’ have burst into society as a novel expert role. They hold increasing responsibility for generating and analysing digitally captured human experiences. The article considers their professionalization not as a functionally necessary development but as the outcome of classification practices and struggles. The rise of data scientists is examined across their discursive classification in the academic and economic fields in both the USA and Germany. Despite notable differences across these fields and nations, the article identifies (...) two common subjectivation patterns. Firstly, data scientists are constructed as hybrids, who combine generally conflictive roles as both generalists and specialists; technicians and communicators; data exploiters and data ethicists. This finding is interpreted as demonstrating a discursive distinction between data scientists and other competing and supposedly more one-dimensional professionals, such as statisticians or computer scientists. Secondly, the article uncovers a discursive construction that interpellates data scientists as discoverers of needs. They are imagined as explorative work subjects who can establish growth for digital capitalism by generating behavioural patterns that allow for personalization, customization and optimization practices. (shrink)

Consumer-based datasets are the products of data brokerage firms that agglomerate millions of personal records on the adult US population. This big data commodity is purchased by both companies and individual clients for purposes such as marketing, risk prevention, and identity searches. The sheer magnitude and population coverage of available consumer-based datasets and the opacity of the business practices that create these datasets pose emergent ethical challenges within the computational social sciences that have begun to incorporate consumer-based datasets into empirical (...) research. To directly engage with the core ethical debates around the use of consumer-based datasets within social science research, I first consider two case study applications of consumer-based dataset-based scholarship. I then focus on three primary ethical dilemmas within consumer-based datasets regarding human subject research, participant privacy, and informed consent in conversation with the principles of the seminal Belmont Report. (shrink)

The application of artificial intelligence and machine learning technologies in healthcare have immense potential to improve the care of patients. While there are some emerging practices surro...

Though rapid collection of big data is ubiquitous across domains, from industry settings to academic contexts, the ethics of big data collection and research are contested. A nexus of data ethics issues is the concept of creep, or repurposing of data for other applications or research beyond the conditions of original collection. Data creep has proven controversial and has prompted concerns about the scope of ethical oversight. Institutional review boards offer little guidance regarding big data, and problematic research can still (...) meet ethical standards. While ethics seem concrete through institutional deployment, I frame ethics as produced. Informed by my ethnographic research at a large public university in the U.S., I explore ethics through two models: ethics as institutional procedures and ethics as acts and intentions. The university where I conducted fieldwork is the development grounds for a predictive model that uses student data to anticipate academic success. While students consent to data collection, the circumstances of consent and the degree to which they are informed are not so apparent, as many data are a product of creep. Drawing from interviews and participant observation with administrators, data scientists, developers, and students, I examine data ethics, from a larger institutional model to everyday enactments related to data creep. After demonstrating the limits of such models, I propose a remodeling of ethics that draws on recent works on data, justice, and refusal to pose generative questions for rethinking ethics in institutional contexts. (shrink)

This article adapts the ethnographic medium of the diary to develop a method for studying data and related data practices. The article focuses on the creation of one data diary, developed iteratively over three years in the context of a national centre for monitoring disasters and natural hazards in Brazil. We describe four points of focus involved in the creation of a data diary – spaces, interfaces, types and situations – before reflecting on the value of this method. We suggest (...) data diaries are able to capture the informal dimension of data-intensive organisations; enable empirical analysis of the specific ways that data intervene in the unfolding of situations; and as a document, data diaries can foster interdisciplinary and inter-expert dialogue by bridging different ways of knowing data. (shrink)

The paper problematises the reliability and ethics of using social media data, such as sourced from Twitter or Instagram, to carry out health-related research. As in many other domains, the opportunity to mine social media for information has been hailed as transformative for research on well-being and disease. Considerations around the fairness, responsibilities and accountabilities relating to using such data have often been set aside, on the understanding that as long as data were anonymised, no real ethical or scientific issue (...) would arise. We first counter this perception by emphasising that the use of social media data in health research can yield problematic and unethical results. We then provide a conceptualisation of methodological data fairness that can complement data management principles such as FAIR by enhancing the actionability of social media data for future research. We highlight the forms that methodological data fairness can take at different stages of the research process and identify practical steps through which researchers can ensure that their practices and outcomes are scientifically sound as well as fair to society at large. We conclude that making research data fair as well as FAIR is inextricably linked to concerns around the adequacy of data practices. The failure to act on those concerns raises serious ethical, methodological and epistemic issues with the knowledge and evidence that are being produced. (shrink)

There has been considerable debate around the ethical issues raised by data-driven technologies such as artificial intelligence. Ethical principles for the field have focused on the need to ensure...

BackgroundEthics review is the process of assessing the ethics of research involving humans. The Ethics Review Committee (ERC) is the key oversight mechanism designated to ensure ethics review. Whether or not this governance mechanism is still fit for purpose in the data-driven research context remains a debated issue among research ethics experts.Main textIn this article, we seek to address this issue in a twofold manner. First, we review the strengths and weaknesses of ERCs in ensuring ethical oversight. Second, we map (...) these strengths and weaknesses onto specific challenges raised by big data research. We distinguish two categories of potential weakness. The first category concerns persistent weaknesses, i.e., those which are not specific to big data research, but may be exacerbated by it. The second category concerns novel weaknesses, i.e., those which are created by and inherent to big data projects. Within this second category, we further distinguish between purview weaknesses related to the ERC’s scope (e.g., how big data projects may evade ERC review) and functional weaknesses, related to the ERC’s way of operating. Based on this analysis, we propose reforms aimed at improving the oversight capacity of ERCs in the era of big data science.ConclusionsWe believe the oversight mechanism could benefit from these reforms because they will help to overcome data-intensive research challenges and consequently benefit research at large. (shrink)

Worker well-being is a hot topic in organizations, consultancy and academia. However, too often, the buzz about worker well-being, enthusiasm for new programs to promote it and interest to research it, have not been accompanied by universal enthusiasm for scientific measurement. Aim to bridge this gap, we address three questions. To address the question ‘What is worker well-being?’, we explain that worker well-being is a multi-facetted concept and that it can be operationalized in a variety of constructs. We propose a (...) four-dimensional taxonomy of worker well-being constructs to illustrate the concept’s complexity and classify ten constructs within this taxonomy. To answer the question ‘How can worker well-being constructs be measured?’, we present two aspects of measures: measure obtrusiveness (i.e., the extent to which obtaining a measure interferes with workers’ experiences) and measure type (i.e., closed question survey, word, behavioral and physiological). We illustrate the diversity of measures across our taxonomy and uncover some hitherto under-appreciated avenues for measuring worker well-being. Finally, we address the question ‘How should a worker well-being measure be selected?’ by discussing conceptual, methodological, practical and ethical considerations when selecting a measure. We summarize these considerations in a short checklist. It is our hope that with this study researchers – working in organizations, in academia or both – will feel more competent to find effective strategies for the measurement worker well-being and eventually make policies and choices with a better understanding of what drives worker well-being. (shrink)

Data platforms represent a new paradigm for carrying out health research. In the platform model, datasets are pooled for remote access and analysis, so novel insights for developing better stratified and/or personalised medicine approaches can be derived from their integration. If the integration of diverse datasets enables development of more accurate risk indicators, prognostic factors, or better treatments and interventions, this obviates the need for the sharing and reuse of data; and a platform-based approach is an appropriate model for facilitating (...) this. Platform-based approaches thus require new thinking about consent. Here we defend an approach to meeting this challenge within the data platform model, grounded in: the notion of ‘reasonable expectations’ for the reuse of data; Waldron’s account of ‘integrity’ as a heuristic for managing disagreement about the ethical permissibility of the approach; and the element of the social contract that emphasises the importance of public engagement in embedding new norms of research consistent with changing technological realities. While a social contract approach may sound appealing, however, it is incoherent in the context at hand. We defend a way forward guided by that part of the social contract which requires public approval for the proposal and argue that we have moral reasons to endorse a wider presumption of data reuse. However, we show that the relationship in question is not recognisably contractual and that the social contract approach is therefore misleading in this context. We conclude stating four requirements on which the legitimacy of our proposal rests. (shrink)

Sharing social media research datasets allows for reproducibility and peer-review, but it is very often difficult or even impossible to achieve due to legal restrictions and can also be ethically questionable. What is more, research data repositories and other research infrastructure and research support institutions are only starting to target social media researchers. In this paper, we present a practical solution to sharing social media data with the help of a social science data archive. Our aim is to contribute to (...) the effort of enhancing comparability and reproducibility in social media research by taking some first steps towards setting standards for sustainable data archiving. We present a showcase for sharing social media data with the example of a big dataset containing geotagged tweets through a research data archive. We provide a general background to the process of long-term archiving of research data. After some consideration of the current obstacles for sharing and archiving social media data, we present our solution of archiving the specific dataset of geotagged tweets at the GESIS Data Archive for the Social Sciences, a publicly funded German data archive for secure and long-term archiving of social science data. We archived and documented tweet IDs and additional information to improve reproducibility of the initial research while also attending to ethical and legal considerations, and taking into account Twitter’s terms of service in particular. (shrink)

Advancements in technology are shifting the ways that biomedical data are collected, managed, and used. The pervasiveness of connected devices is expanding the types of information that are defined as ‘health data.’ Additionally, cloud-based mechanisms for data collection and distribution are shifting biomedical research away from traditional infrastructure towards a more distributed and interconnected ecosystem. This shift provides an opportunity for us to reimagine the roles of scientists and participants in health research, with the potential to more meaningfully engage in (...) partnership across the research process. At the same time, these emerging practices present a potential to expose research participants to unanticipated and unintended consequences. Social norms and policy can help to mitigate these risks, but their development is often slow relative to the pace of technological advances and, as such, they can become reactive rather than prospective. As an alternative, the integrated development of data governance structures within technological advancements, supports their effective implementation, evaluation and evolution in a manner that can balance the benefits and risks of biomedical researcher in a decentralized ecosystem. (shrink)

What could a social-justice oriented, feminist data studies look like? The current datalogical turn foregrounds the digital datafication of everyday life, increasing algorithmic processing and data as an emergent regime of power/knowledge. Scholars celebrate the politics of big data knowledge production for its omnipotent objectivity or dismiss it outright as data fundamentalism that may lead to methodological genocide. In this feminist and postcolonial intervention into gender-, race- and geography-blind ‘big data’ ideologies, I call for ethical, anti-oppressive digital data-driven research in (...) the social sciences and humanities. I argue that a reflexive data scholarship can emerge from the reintegration of feminist and postcolonial science studies and ethics of care ideals. Although it is not a panacea for all ails of data mining, I offer a road map for an alternative data-analysis practice that is more power-sensitive and accountable. By incorporating a people-centric and context-aware perspective that acknowledges relationships of dependency, reflects on temptations, and scrutinises benefits and harm, an ‘asymmetrically reciprocal’ (Young, 1997) research encounter may be achieved. I bring this perspective to bear on experiences of a two-year research project with eighty-four young Londoners on digital identities and living in a highly diverse city. I align awareness of uneven relations of power and knowledge with the messy relation of dependency between human and non-human actors in data analysis. This framework productively recognises that digital data cannot be expected to speak for itself, that data do not emerge from a vacuum, and that isolated data patterns cannot be the end-goal of a situated and reflexive research endeavor. Data-driven research, in turn, shows the urgency for renewed feminist ethical reflection on how digital mediation impacts upon responsibility, intersectional power relations, human subjectivity and the autonomy of research participants over their own data. (shrink)

The emergence of Big Data has added a new aspect to conceptualizing the use of digital technologies in the delivery of public services and for realizing digital governance. This article explores, via the ‘value-chain’ approach, the evolution of digital governance research, and aligns it with current developments associated with data analytics, often referred to as ‘Big Data’. In many ways, the current discourse around Big Data reiterates and repeats established commentaries within the eGovernment research community. This body of knowledge provides (...) an opportunity to reflect on the ‘promise’ of Big Data, both in relation to service delivery and policy formulation. This includes, issues associated with the quality and reliability of data, from mixing public and private sector data, issues associated with the ownership of raw and manipulated data, and ethical issues concerning surveillance and privacy. These insights and the issues raised help assess the value of Big Data in government and smart city environments. (shrink)

Social media offers an attractive site for Big Data research. Access to big social media data, however, is controlled by companies that privilege corporate, governmental, and private research firms. Additionally, Institutional Review Boards’ regulative practices and slow adaptation to emerging ethical dilemmas in online contexts creates challenges for Big Data researchers. We examine these challenges in the context of a feminist qualitative Big Data analysis of the hashtag event #WhyIStayed. We argue power, context, and subjugated knowledges must each be central (...) considerations in conducting Big Data social media research. In doing so, this paper offers a feminist practice of holistic reflexivity in order to help social media researchers navigate and negotiate this terrain. (shrink)

Critical Data Studies explore the unique cultural, ethical, and critical challenges posed by Big Data. Rather than treat Big Data as only scientifically empirical and therefore largely neutral phenomena, CDS advocates the view that Big Data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact with society, its organization and functioning, and the resulting impact on individuals’ daily lives. CDS questions the (...) many assumptions about Big Data that permeate contemporary literature on information and society by locating instances where Big Data may be naively taken to denote objective and transparent informational entities. In this introduction to the Big Data & Society CDS special theme, we briefly describe CDS work, its orientations, and principles. (shrink)

This study identifies and explores evolving concepts of trust and privacy in the context of user-generated health data. We define “user-generated health data” as data captured through devices or software and used outside of traditional clinical settings for tracking personal health data. The investigators conducted qualitative research through semistructured interviews with researchers, health technology start-up companies, and members of the general public to inquire why and how they interact with and understand the value of user-generated health data. We found significant (...) results concerning new attitudes toward trust, privacy, and sharing of health data outside of clinical settings that conflict with regulations governing health data within clinical settings. Members of the general public expressed little concern about sharing health data with the companies that sold the devices or apps they used, and indicated that they rarely read the “terms and conditions” detailing how their data may be exploited by the company or third-party affiliates before consenting to them. In contrast, interviews with researchers revealed significant resistance among potential research participants to sharing their user-generated health data for purposes of scientific study. The widespread rhetoric of personalization and social sharing in “user-generated culture” appears to facilitate an understanding of user-generated health data that deemphasizes the risk of exploitation in favor of loosely defined benefits to individual and social well-being. We recommend clarification and greater transparency of regulations governing data sharing related to health. (shrink)

In their article, Char et al. have created a model intended to tidy up the messy landscape of ethical concerns arising from machine-learning health care applications. The novel con...

Peer commentary on Char, Abràmoff & Feudtner (2020) target article: "Identifying Ethical Considerations for Machine Learning Healthcare Applications" .

Recent calls for retraction of a large body of Chinese transplant research and of Dr Jiankui He’s gene editing research has led to renewed interest in the question of publication, retraction and use of unethical biomedical research. In Part 1 of this paper, we briefly review the now well-established consequentialist and deontological arguments for and against the use of unethical research. We argue that, while there are potentially compelling justifications for use under some circumstances, these justifications fail when unethical practices (...) are ongoing—as in the case of research involving transplantations in which organs have been procured unethically from executed prisoners. Use of such research displays a lack of respect and concern for the victims and undermines efforts to deter unethical practices. Such use also creates moral taint and renders those who use the research complicit in continuing harm. In Part 2, we distinguish three dimensions of ‘non-use’ of unethical research: non-use of published unethical research, non-publication, and retraction and argue that all three types of non-use should be upheld in the case of Chinese transplant research. Publishers have responsibilities to not publish contemporary unethical biomedical research, and where this has occurred, to retract publications. Failure to retract the papers implicitly condones the research, while uptake of the research through citations rewards researchers and ongoing circulation of the data in the literature facilitates subsequent use by researchers, policymakers and clinicians. (shrink)

A recent systematic review of Machine Learning (ML) approaches to health data, containing over 100 studies, found that the most investigated problem was mental health (Yin et al., 2019). Relatedly, recent estimates suggest that between 165,000 and 325,000 health and wellness apps are now commercially available, with over 10,000 of those designed specifically for mental health (Carlo et al., 2019). In light of these trends, the present chapter has three aims: (1) provide an informative overview of some of the recent (...) work taking place at the intersection of text mining and mental health so that we can (2) highlight and analyze several pressing ethical issues that are arising in this rapidly growing field and (3) suggest productive directions for how these issues might be better addressed within future interdisciplinary work to ensure the responsible development of text mining approaches in psychology generally, and in mental health fields, specifically. In Section 1, we review some of the recent literature on text-mining and mental health in the contexts of traditional experimental settings, social media, and research involving electronic health records. Then, in Section 2, we introduce and discuss ethical concerns that arise before, during, and after research is conducted. Finally, in Section 3, we offer several suggestions about how ethical oversight of text-mining research might be improved to be more responsive to the concerns mapped out in Section 2. (shrink)

The article covers electronic informed consent from different dimensions so that practitioners might understand the history, regulation, and current status of eIC. It covers the transition of informed consent to electronic screens and the implications of that transition in terms of design, costs, and data analysis. The article explores the limits of regulation mandating eIC for mobile application research, and addresses some of the broader social context around eIC.

Central to ethical debates in contemporary mental health research are the rhetoric of parity of esteem, challenges underpinned by the social construct of vulnerability and the tendency to homogenis...

The large-scale and intensive collection and analysis of digital data (commonly called “Big Data”) has become a common, popular, and consensual research method for the social sciences, as the automation of data collection, mathematization of analysis, and digital objectification reinforce both its efficiency and truth-value. This article opens with a critical review of the literature on data collection and analysis, and summarizes current ethical discussions focusing on these technologies. A semiotic model of data production and circulation is then introduced to (...) problematize the view that digital data has ceased to stand for a formalization method (a possible kind of representation among others), and effectively “becomes the world itself” (a direct presentation of the world outperforming all other modes of representation). Following Charles Sanders Peirce’s semiotics and pragmaticist philosophy, we characterize digitalization as a hypersymbolic semiotic process, and we highlight the naturalization of meaning, the illusion of iconicity, and rhetorical efficiency on which data’s truth value relies within the context of its large-scale, profit-driven, and results-oriented research uses. This outlines some epistemological and ethical implications of data’s visualization, use, and authority, and indicates avenues for critical semiotics of contemporary data science and analysis. (shrink)

Sensor-based data are becoming increasingly widespread in social, behavioral and organizational sciences. Far from providing a neutral window on 'reality', sensor-based big-data are highly complex, constructed data sources. Nevertheless, a more systematic approach to the validation of sensors as a method of data collection is lacking, as their use and conceptualization have been spread out across different strands of social-, behavioral- and computer science literature. Further debunking the myth of raw data, the present article argues that, in order to validate (...) sensor-based data, researchers need to take into account the mutual interdependence between types of sensors available on the market, the conceptual (construct) choices made in the research process, and the contextual cues. Sensor-based data in research are usually combined with additional quantitative and qualitative data sources. However, the incompatibility between the highly granular nature of sensor data and the static, a-temporal character of traditional quantitative and qualitative data has not been sufficiently emphasized as a key limiting factor of sensor-based research. It is likely that the failure to consider the basic quality criteria of social science measurement indicators more explicitly may lead to the production of insignificant results, despite the availability of high volume and high-resolution data. The paper concludes with recommendations for designing and conducting mixed methods studies using sensors. (shrink)

The proliferation of digital data and internet-based research technologies is transforming the research landscape, and researchers and research ethics communities are struggling to respond to the ethical issues being raised. This paper discusses the findings from a collaborative project that explored emerging ethical issues associated with the expanding use of digital data for research. The project involved consulting with researchers from a broad range of disciplinary fields. These discussions identified five key sets of issues and informed the development of guidelines (...) orientated to meet the needs of researchers and ethics committee members. We argue that establishing common approaches to assessing ethical risks of research involving digital data will promote consistency in the ethical standards for research, enable the smooth functioning of ethics committees, and sustain public confidence in research. We conclude with recommendations for the development of educational resources for ethics committees, data management guidelines and further public education. (shrink)

This article reflects on the relevance and applicability of the Belmont Report nearly four decades after its original publication. In an exploration of criticisms that have been raised in response to the report and of significant changes that have occurred within the context of biomedical research, five primary themes arise. These themes include the increasingly vague boundary between research and practice, unique harms to communities that are not addressed by the principle of respect for persons, and how growing complexity and (...) commodification in research have shed light on the importance of transparency. The repercussions of Belmont's emphasis on the protection of vulnerable populations is also explored, as is the relationship between the report's ethical principles and their applications. It is concluded that while the Belmont Report was an impressive response to the ethical issues of its day, the field of research ethics involving human subjects may have outgrown it. (shrink)

This paper describes how the ethical problems raised by scientific data obtained through harmful and immoral conduct may also emerge in cases where data is collected from the Internet. It describes the major arguments for and against using ill-gotten information in research, and shows how they may be applied to research that either collects information about the Internet itself or which uses data from questionable or unknown sources on the Internet. Three examples demonstrate how researchers address the ethical issues raised (...) by the sources of data that they use and how the existing arguments concerning the use of ill-gotten information apply to Internet research. The problems faced by researchers who collect or use data from the Internet are shown to be the same problems faced by researchers in other fields who may obtain or use ill-gotten information. (shrink)