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  1. The new ethical responsibilities of internet service providers.Luciano Floridi - 2011 - Philosophy and Technology 24 (4):369-370.
    The exponential developments of internet services and resources have brought enormous benefits, but also enormous moral and ethical challenges. This paper introduces the contributions from a research workshop, tasked with defining new ethical responsibilities for Internet Service Providers (ISPs).
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  • “But the data is already public”: on the ethics of research in Facebook.Michael Zimmer - 2010 - Ethics and Information Technology 12 (4):313-325.
    In 2008, a group of researchers publicly released profile data collected from the Facebook accounts of an entire cohort of college students from a US university. While good-faith attempts were made to hide the identity of the institution and protect the privacy of the data subjects, the source of the data was quickly identified, placing the privacy of the students at risk. Using this incident as a case study, this paper articulates a set of ethical concerns that must be addressed (...)
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  • Epistemic injustice: power and the ethics of knowing.Miranda Fricker - 2007 - New York: Oxford University Press.
    Fricker shows that virtue epistemology provides a general epistemological idiom in which these issues can be forcefully discussed.
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  • Archiving information from geotagged tweets to promote reproducibility and comparability in social media research.Fred Morstatter, Jürgen Pfeffer, Wolfgang Zenk-Möltgen, Katrin Weller & Katharina Kinder-Kurlanda - 2017 - Big Data and Society 4 (2).
    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 (...)
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  • Mining social media data: How are research sponsors and researchers addressing the ethical challenges?Joanna Taylor & Claudia Pagliari - 2017 - Research Ethics 14 (2):1-39.
    Background:Data representing people’s behaviour, attitudes, feelings and relationships are increasingly being harvested from social media platforms and re-used for research purposes. This can be ethically problematic, even where such data exist in the public domain. We set out to explore how the academic community is addressing these challenges by analysing a national corpus of research ethics guidelines and published studies in one interdisciplinary research area.Methods:Ethics guidelines published by Research Councils UK, its seven-member councils and guidelines cited within these were reviewed. (...)
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  • Terms and Conditions May Apply.Kyle L. Galbraith - 2017 - American Journal of Bioethics 17 (3):21-22.
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  • Using Social Media as a Research Recruitment Tool: Ethical Issues and Recommendations.Luke Gelinas, Robin Pierce, Sabune Winkler, I. Glenn Cohen, Holly Fernandez Lynch & Barbara E. Bierer - 2017 - American Journal of Bioethics 17 (3):3-14.
    The use of social media as a recruitment tool for research with humans is increasing, and likely to continue to grow. Despite this, to date there has been no specific regulatory guidance and there has been little in the bioethics literature to guide investigators and institutional review boards faced with navigating the ethical issues such use raises. We begin to fill this gap by first defending a nonexceptionalist methodology for assessing social media recruitment; second, examining respect for privacy and investigator (...)
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  • What is data ethics?Luciano Floridi & Mariarosaria Taddeo - 2016 - Philosophical Transactions of the Royal Society A 374 (2083):20160360.
    This theme issue has the founding ambition of landscaping Data Ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing, and use), algorithms (including AI, artificial agents, machine learning, and robots), and corresponding practices (including responsible innovation, programming, hacking, and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values). Data Ethics builds on the foundation provided by Computer and Information (...)
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  • Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data.Reuben Binns & Michael Veale - 2017 - Big Data and Society 4 (2):205395171774353.
    Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining and fairness, accountability and transparency machine learning, their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data on sensitive attributes such as gender, ethnicity, sexuality or disability needed to diagnose and mitigate emergent indirect discrimination-by-proxy, such (...)
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  • Privacy as Protection of the Incomputable Self: From Agnostic to Agonistic Machine Learning.Mireille Hildebrandt - 2019 - Theoretical Inquiries in Law 20 (1):83-121.
    This Article takes the perspective of law and philosophy, integrating insights from computer science. First, I will argue that in the era of big data analytics we need an understanding of privacy that is capable of protecting what is uncountable, incalculable or incomputable about individual persons. To instigate this new dimension of the right to privacy, I expand previous work on the relational nature of privacy, and the productive indeterminacy of human identity it implies, into an ecological understanding of privacy, (...)
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  • The debate on the moral responsibilities of online service providers.Mariarosaria Taddeo & Luciano Floridi - 2016 - Science and Engineering Ethics 22 (6):1575-1603.
    Online service providers —such as AOL, Facebook, Google, Microsoft, and Twitter—significantly shape the informational environment and influence users’ experiences and interactions within it. There is a general agreement on the centrality of OSPs in information societies, but little consensus about what principles should shape their moral responsibilities and practices. In this article, we analyse the main contributions to the debate on the moral responsibilities of OSPs. By endorsing the method of the levels of abstract, we first analyse the moral responsibilities (...)
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  • Data feminism.Catherine D'Ignazio - 2020 - Cambridge, Massachusetts: The MIT Press. Edited by Lauren F. Klein.
    We have seen through many examples that data science and artificial intelligence can reinforce structural inequalities like sexism and racism. Data is power, and that power is distributed unequally. This book offers a vision for a feminist data science that can challenge power and work towards justice. This book takes a stand against a world that benefits some (including the authors, two white women) at the expense of others. It seeks to provide concrete steps for data scientists seeking to learn (...)
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  • What is data justice? The case for connecting digital rights and freedoms globally.Linnet Taylor - 2017 - Big Data and Society 4 (2).
    The increasing availability of digital data reflecting economic and human development, and in particular the availability of data emitted as a by-product of people’s use of technological devices and services, has both political and practical implications for the way people are seen and treated by the state and by the private sector. Yet the data revolution is so far primarily a technical one: the power of data to sort, categorise and intervene has not yet been explicitly connected to a social (...)
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  • Fairness, Respect and the Egalitarian Ethos Revisited.Jonathan Wolff - 2010 - The Journal of Ethics 14 (3-4):335-350.
    This paper reconsiders some themes and arguments from my earlier paper “Fairness, Respect and the Egalitarian Ethos.” That work is often considered to be part of a cluster of papers attacking “luck egalitarianism” on the grounds that insisting on luck egalitarianism's standards of fairness undermines relations of mutual respect among citizens. While this is an accurate reading, the earlier paper did not make its motivations clear, and the current paper attempts to explain the reasons that led me to write the (...)
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  • Fairness and Philosophy.Alan Ryan - 2006 - Social Research: An International Quarterly 73 (2):597-606.
    The paper puts forward a pluralistic account of fairness within which concepts of equality of sacrifice and outcome, desert, and randomized outcomes within a fair framework all have their place. The distinction between efficiency and fairness is set out early on, and it is later argued that only efficient social arrangements can withstand the questioning about the fairness of the way they distribute their benefits to their beneficiaries and impose demands on those whose taxes pay for them that the modern (...)
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  • Ethical issues in using Twitter for population-level depression monitoring: a qualitative study.Jude Mikal, Samantha Hurst & Mike Conway - 2016 - BMC Medical Ethics 17 (1):1.
    Recently, significant research effort has focused on using Twitter to investigate mental health at the population-level. While there has been influential work in developing ethical guidelines for Internet discussion forum-based research in public health, there is currently limited work focused on addressing ethical problems in Twitter-based public health research, and less still that considers these issues from users’ own perspectives. In this work, we aim to investigate public attitudes towards utilizing public domain Twitter data for population-level mental health monitoring using (...)
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  • The ethics of big data: current and foreseeable issues in biomedical contexts.Brent Daniel Mittelstadt & Luciano Floridi - 2016 - Science and Engineering Ethics 22 (2):303–341.
    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 (...)
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  • Behaving as Expected: Public Information and Fairness Norms.Cristina Bicchieri & Alex Chavez - unknown
    What is considered to be fair depends on context-dependent expectations. Using a modified version of the Ultimatum Game, we demonstrate that both fair behavior and perceptions of fairness depend upon beliefs about what one ought to do in a situation—that is, upon normative expectations. We manipulate such expectations by creating informational asymmetries about the offer choices available to the Proposer, and find that behavior varies accordingly. Proposers and Responders show a remarkable degree of agreement in their beliefs about which choices (...)
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  • Public goods and fairness.Garrett Cullity - 2008 - Australasian Journal of Philosophy 86 (1):1 – 21.
    To what extent can we as a community legitimately require individuals to contribute to producing public goods? Most of us think that, at least sometimes, refusing to pay for a public good that you have enjoyed can involve a kind of 'free riding' that makes it wrong. But what is less clear is under exactly which circumstances this is wrong. To work out the answer to that, we need to know why it is wrong. I argue that when free riding (...)
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  • Where are human subjects in Big Data research? The emerging ethics divide.Kate Crawford & Jacob Metcalf - 2016 - Big Data and Society 3 (1).
    There are growing discontinuities between the research practices of data science and established tools of research ethics regulation. Some of the core commitments of existing research ethics regulations, such as the distinction between research and practice, cannot be cleanly exported from biomedical research to data science research. Such discontinuities have led some data science practitioners and researchers to move toward rejecting ethics regulations outright. These shifts occur at the same time as a proposal for major revisions to the Common Rule—the (...)
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  • Framing Big Data: The discursive construction of a radio cell query in Germany.Charlotte Fischer & Christian Pentzold - 2017 - Big Data and Society 4 (2).
    The article examines the construction of “Big Data” in media discourse. Rather than asking what Big Data really is or is not, it deals with the discursive work that goes into making Big Data a socially relevant phenomenon and problem in the first place. It starts from the idea that in modern societies the public understanding of technology is largely driven by a media-based discourse, which is a key arena for circulating collectively shared meanings. This largely ignored dimension invites us (...)
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