|10 Jun 2016||10:00am - 6:00pm||Seminar room SG2, Alison Richard Building.|
Registration for this event has now closed.
The workshop will support an interdisciplinary conversation at the University of Cambridge about the ethics of big data research. Its aims are both to raise awareness of ethical issues associated with big data and to contribute to the development of material for the Research Group’s digital reader – a publicly accessible, interactive online resource on the ethics of big data research.
Jake Metcalf (Data & Society Institute)
Bendert Zevenbergen (Oxford Internet Institute)
Madeleine Greenhalgh (Cabinet Office)
Respondent: Julia Powles (University of Cambridge)
Jake Metcalf (Data and Society Institute)
Part of the Ethics of Big Data Research Group, series
Administrative assistance: firstname.lastname@example.org
Speaker: Bendert Zevenbergen
Institution: Oxford Internet Institute
Title: The Networked System Ethics guidelines
The Networked System Ethics guidelines aim to underpin a meaningful cross-disciplinary conversation between gatekeepers of ethics standards and researchers about the ethical and social impact of technical Internet research projects. The iterative reflexivity methodology guides stakeholders to identify and minimize risks and other burdens, which must be mitigated to the largest extent possible by adjusting the design of the project before data collection takes place. The aim is thus to improve the ethical considerations of individual projects, but also to streamline the proceedings of ethical discussions in Internet research generally.
The primary audience for these guidelines are technical researchers (e.g.
computer science, network engineering, as well as social science) and gatekeepers of ethics standards at institutions, academic journals, conferences, and funding agencies. It is certainly possible to use these guidelines beyond in academic research in civil society, product development, or otherwise, but these are not the primary audience. Some sections point the reader to other groups – such as the data subjects, lawyers, local peers, etc. – who can also use (parts of) the guidelines to help assess the impact of a project from their expertise or point of view.
The guidelines have been created through a lengthy multidisciplinary and collaborative effort. Computer scientists, network engineers, lawyers, philosophers, and social scientists contributed valuable input in specifically organized workshops and meetings.
Speaker: Madeleine Greenhalgh
Institution: Data Group, Government Digital Service
Title: The Government Data Science Ethical Framework
Data science provides huge opportunities for government. Harnessing new forms of data with increasingly powerful computer techniques increases operational efficiency, improves public services and provides insight for better policymaking.
We want people in government to feel confident using data science techniques to innovate, but currently the laws and guidance surrounding the many aspects of data science are disparate and complex. Through an open policy making process we have worked with data scientists and experts in ethics, law and privacy amongst others, from government, academia, industry and civil society to develop the Data Science Ethical Framework for government. The guidance is intended to bring together relevant laws and best practice to give teams robust principles to work with.
The public view of government use of data science is vital to understanding how the framework should be presented and the questions it should ask. This is why we ran a public dialogue before drafting the first framework and the findings will be used to improve future iterations.
We have published as a first version of the Framework that we are asking the public, experts, civil servants and other interested parties to help us perfect and iterate.
Speaker: Jake Metcalf
Institution: Data and Society Institute and Founding Partner, Ethical Resolve
Title: Data subjectivity: responding to emerging forms of research and research subjects
Abstract: There are significant disjunctions between the established norms and practices of human- subjects research protections and the emerging research methods and infrastructures at the heart of data science and the internet economy. For example, long-standing research ethics regulations typically exempt from further review research projects that utilize pre-existing and/or public datasets, such as most data science research. This was once a sound assumption because such research does not require additional intervention into a person’s life or body, and the ‘publicness’ of the data meant all informational or privacy harms had already occurred. However, because big data enables datasets to be (at least in theory) widely networked, continually updated, infinitely repurposable and indefinitely stored, this assumption is no longer sound—big data allows potential harms to become networked, distributed and temporally stretched such that potential harms can take place far outside of the parameters of the research. Familiar protections for research subjects need rethinking in light of these changes to scientific practices. In this talk I will discuss how a historicization of ‘human subjects’ in research enables us to critically interrogate an emerging form of research subjectivity in response to the changing conditions of data-driven research. I will ask how data scientists, practitioners, policy-makers and ethicists might account for the emerging interests and concerns of ‘data subjects,’ particularly in light of proposed changes to research ethics regulations in the U.S.
Registration and coffee
Welcome and introduction
Ethics of Big Data in practice in the academic context (EOBD team)
We will present and discuss our conclusions from the 2015-16 seminar series, and outline the plans for continuation of the seminar series in 2016-17
How do we engage people with thinking ethically about big data in other contexts?
The Networked System Ethics guidelines
The Government Data Science Ethical Framework
Respondent: Julia Powles, University of Cambridge
Presentations from 2 speakers on processes and guidelines from big data research and the challenges in engaging different communities
Data subjectivity: responding to emerging forms of research and research subjects