Key Points for an Ethical Evaluation of Healthcare Big Data

Science and Engineering Ethics, 2015

The capacity to collect and analyse data is growing exponentially. Referred to as ‘Big Data’, this scientific, social and technological trend has helped create destabilising amounts of information, which can challenge accepted social and ethical norms. Big Data remains a fuzzy idea, emerging across social, scientific, and business contexts sometimes seemingly related only by the gigantic size of the datasets being considered. As is often the case with the cutting edge of scientific and technological progress, understanding of the ethical implications of Big Data lags behind. In order to bridge such a gap, this article systematically and comprehensively analyses academic literature concerning the ethical implications of Big Data, providing a watershed for future ethical investigations and regulations. Particular attention is paid to biomedical Big Data due to the inherent sensitivity of medical information. By means of a meta-analysis of the literature, a thematic narrative is provided to guide ethicists, data scientists, regulators and other stakeholders through what is already known or hypothesised about the ethical risks of this emerging and innovative phenomenon. Five key areas of concern are identified: (1) informed consent, (2) privacy (including anonymisation and data protection), (3) ownership, (4) epistemology and objectivity, and (5) ‘Big Data Divides’ created between those who have or lack the necessary resources to analyse increasingly large datasets. Critical gaps in the treatment of these themes are identified with suggestions for future research. Six additional areas of concern are then suggested which, although related have not yet attracted extensive debate in the existing literature. It is argued that they will require much closer scrutiny in the immediate future: (6) the dangers of ignoring group-level ethical harms; (7) the importance of epistemology in assessing the ethics of Big Data; (8) the changing nature of fiduciary relationships that become increasingly data saturated; (9) the need to distinguish between ‘academic’ and ‘commercial’ Big Data practices in terms of potential harm to data subjects; (10) future problems with ownership of intellectual property generated from analysis of aggregated datasets; and (11) the difficulty of providing meaningful access rights to individual data subjects that lack necessary resources. Considered together, these eleven themes provide a thorough critical framework to guide ethical assessment and governance of emerging Big Data practices.

Ethical decision-making frameworks assist in identifying the issues at stake in a particular setting and thinking through, in a methodical manner, the ethical issues that require consideration as well as the values that need to be considered and promoted. Decisions made about the use, sharing, and re-use of big data are complex and laden with values. This paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by the Science, Health and Policy-relevant Ethics in Singapore (SHAPES) Initiative. It presents the aim and rationale for this framework supported by the underlying ethical concerns that relate to all health and research contexts. It also describes a set of substantive and procedural values that can be weighed up in addressing these concerns, and a step-by-step process for identifying, considering, and resolving the ethical issues arising from big data uses in health and research.

Biomedical research ethics has historically rested on cases of egregious harm and disrespect to subjects through direct experimentation on bodies. However, with the emergence of sophisticated health data and specimen analysis, a new type of research ethics case study has emerged to highlight the limitations of applying current research and privacy regulations to the study of Big Data. In this paper I challenge common myths about data protection and argue three points researchers must keep in mind: (1) De-identification does not always secure privacy in the manner intended, (2) Successful identification does not suffice to address all ethical concerns, and (3) any party that creates new health records should not presume that traditional regulatory restrictions have fully accounted for their own part in this vanguard of evolving responsibilities. To this last point, I argue that any researcher, including those operating in the largely unregulated domains of public data and citizen science, should seek ethics consultation to help them respect persons, avoid harms, and proceed justly beyond the legal minimums.

Asian Bioethics Review

As opposed to a 'one size fits all' approach, precision medicine uses relevant biological (including genetic), medical, behavioural and environmental information about a person to further personalize their healthcare. This could mean better prediction of someone's disease risk and more effective diagnosis and treatment if they have a condition. Big data allows for far more precision and tailoring than was ever before possible by linking together diverse datasets to reveal hitherto-unknown correlations and causal pathways. But it also raises ethical issues relating to the balancing of interests, viability of anonymization, familial and group implications, as well as genetic discrimination. This article analyses these issues in light of the values of public benefit, justice, harm minimization, transparency, engagement and reflexivity and applies the deliberative balancing approach found in the Ethical Framework for Big Data in Health and Research (Xafis et al. 2019) to a case study on clinical genomic data sharing. Please refer to that article for an explanation of how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. Our discussion is meant to be of use to those involved in the practice as well as governance and oversight of precision medicine to address ethical concerns that arise in a coherent and systematic manner.

The main problems faced by scientists in working with Big Data sets, highlighting the main ethical issues, taking into account the legislation of the European Union. After a brief Introduction to Big Data, the Technology section presents specific research applications. There is an approach to the main philosophical issues in Philosophical Aspects, and Legal Aspects with specific ethical issues in the EU Regulation on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (Data Protection Directive - General Data Protection Regulation, "GDPR"). The Ethics Issues section details the specific aspects of Big Data. After a brief section of Big Data Research, I finalize my work with the presentation of Conclusions on research ethics in working with Big Data. CONTENTS: Abstract 1. Introduction - 1.1 Definitions - 1.2 Big Data dimensions 2. Technology - 2.1 Applications - - 2.1.1 In research 3. Philosophical aspects 4. Legal aspects - 4.1 GDPR - - Stages of processing of personal data - - Principles of data processing - - Privacy policy and transparency - - Purposes of data processing - - Design and implicit confidentiality - - The (legal) paradox of Big Data 5. Ethical issues - Ethics in research - Awareness - Consent - Control - Transparency - Trust - Ownership - Surveillance and security - Digital identity - Tailored reality - De-identification - Digital inequality - Privacy 6. Big Data research Conclusions Bibliography DOI: 10.13140/RG.2.2.11054.46401

2021

Τhis paper explores the legal issues that arise from the collection and processing of Big Health Data in the light of the EU law on Data Protection, placing particular emphasis on the General Data Protection Regulation. Whether Big Health Data can be characterised as "personal data" or not is really the crux of the matter. The legal ambiguity is compounded by the fact that, even though the processing of Big Health Data is premised on the de-identification of the data subject, the possibility of a combination of Big Health Data with other data circulating freely on the web or from other data files cannot be excluded. Moreover, data subject's rights, e.g., the right not to be subject to a decision based solely on automated processing, are heavily impacted by the use of AI, algorithms and technologies that reclaim health data for further use, resulting in sometimes ambiguous results that have substantial impact on individuals. On the other hand, as the COVID-19 pandemic has revealed, Big Data analytics can offer crucial source of information. In this respect, this paper identifies and systematises the legal provisions concerned, offering interpretative solutions that tackle dangers concerning data subjects' rights while embracing the opportunities that Big Health Data have to offer.

Annals of epidemiology, 2017

This article reflects on the activities of the Ethics Committee of the American College of Epidemiology (ACE). Members of the Ethics Committee identified an opportunity to elaborate on knowledge gained since the inception of the original Ethics Guidelines published by the ACE Ethics and Standards of Practice Committee in 2000. The ACE Ethics Committee presented a symposium session at the 2016 Epidemiology Congress of the Americas in Miami on the evolving complexities of ethics and epidemiology as it pertains to "big data." This article presents a summary and further discussion of that symposium session. Three topic areas were presented: the policy implications of big data and computing, the fallacy of "secondary" data sources, and the duty of citizens to contribute to big data. A balanced perspective is needed that provides safeguards for individuals but also furthers research to improve population health. Our in-depth review offers next steps for teaching of eth...

L Floridi & B Mittelstadt (Eds)., Ethics of Biomedical Big Data

Two scientific domains that are crucial in “Biomedical Big Data”, computing and statistics, do not typically require “training in the responsible conduct of research” or research ethics. While “responsible conduct of research” (RCR) comprises interactions with subjects (human and non-human), it also involves interactions with other scientists, the scientific community, the public, and in some contexts, research funders. Historically, the development or emergence of disciplines and professions tend to involve a semi-simultaneous emergence of professional norms and/or codes of conduct. However, Biomedical Big Data is not emerging as a single discipline or profession, and engages practitioners from many diverse backgrounds. Moreover, the place of the data analyst or the computer scientist developing analytic algorithms seems to be too granular to be considered specifically within the activities that comprise “responsible research and innovation” (RRI). Current legal and policy-level considerations of Biomedical Big Data and RRI are implicitly assuming that scientists carrying out the research and achieving the innovations are exercising their scientific freedom – i.e., conducting research – responsibly. The assumption is that all scientists are trained to conduct research responsibly. In the United States, federal agencies funding research require that training in RCR be included – some of the time. Because the vast majority of research that was federally funded has not included Biomedical Big Data, RCR training paradigms have emerged over the past 20 years in US institutions that are not particularly relevant for Big Data. While it would be efficient to utilize such established, well-known, easily-documented RCR training programs, this chapter discusses how and why this is less likely to support the development of professional norms that are relevant for Biomedical Big Data. This chapter will describe an alternative approach that can support ongoing reflection on professional obligations, which can be used in a wide range of ethical, legal, and social implications (ELSI), including those that have not yet been identified. This may be the greatest strength of this alternative approach for preparing practitioners for Biomedical Big Data, because the ability to apply prior learning in ethics to previously unseen problems is especially critical in the current era of dynamic and massive data accumulation. To support the development of normative ethical practices among practitioners in Biomedical Big Data, this chapter reviews the guidelines for professional practice from three statistical associations (American Statistical Association; Royal Statistics Society; International Statistics Institute) and from the Association of Computing Machinery. These can be leveraged to ensure that, in their work with Biomedical Big Data, participants know and understand the ethical, legal, and social implications of that work. Formal integration of these (or other relevant) guidelines into the preparation for practice with data (big and small) can help in dealing with ethical challenges currently arising with Big Data in biomedical research; moreover, this integration can also help deal with challenges that have not yet arisen. These outcomes, which are consistent with recent calls for the institutionalization of reflection and reasoning around ELSI across scientific disciplines, in Europe, are only possible as long as the integration effort does not follow a currently-dominant paradigm for training in RCR. Preparing scientists to engage competently in conversations around ethical issues in Biomedical Big Data requires purposeful, discipline-relevant, and developmental training that can come from, and support, a culture of ethical biomedical research and practice with Big Data.

Yearbook of Medical Informatics, 2020

SummaryContemporary bioethics was fledged and is sustained by challenges posed by new technologies. These technologies have affected many lives. Yet health informatics affects more lives than any of them. The challenges include the development and the appropriate uses and users of machine learning software, the balancing of privacy rights against the needs of public health and clinical practice in a time of Big Data analytics, whether and how to use this technology, and the role of ethics and standards in health policy. Historical antecedents in statistics and evidence-based practice foreshadow some of the difficulties now faced, but the scope and scale of these challenges requires that ethics, too, be brought to scale in parallel, especially given the size of contemporary data sets and the processing power of new computers. Fortunately, applied ethics affords a variety of tools to help identify and rank applicable values, support best practices, and contribute to standards. The bio...

Journal of Medical Systems, 2021

Personalized medicine (PM) operates with biological data to optimize therapy or prevention and to achieve cost reduction. Associated data may consist of large variations of informational subtypes e.g. genetic characteristics and their epigenetic modifications, biomarkers or even individual lifestyle factors. Present innovations in the field of information technology have already enabled the procession of increasingly large amounts of such data (‘volume’) from various sources (‘variety’) and varying quality in terms of data accuracy (‘veracity’) to facilitate the generation and analyzation of messy data sets within a short and highly efficient time period (‘velocity’) to provide insights into previously unknown connections and correlations between different items (‘value’). As such developments are characteristics of Big Data approaches, Big Data itself has become an important catchphrase that is closely linked to the emerging foundations and approaches of PM. However, as ethical con...