09:00:00 - 18:30:00,
Monday 30 November, 2015
Data science provides huge opportunities to improve private and public life. However, such a potentially highly positive impact is coupled to significant ethical challenges. The extensive use of increasingly more data (Big Data), the growing reliance on algorithms to analyse them and to reach decisions (machine learning), as well as the gradual reduction of human oversight over many automatic processes pose pressing issues of fairness, responsibility, and respect of human rights.
These issues can be addressed successfully. However, if they are overlooked, underestimated or left unresolved, they risk hindering the innovation and the progress that data science can bring to society at large and to future generations. Furthermore, data science projects may face a double bottleneck: ethical mistakes or misunderstandings may lead to social rejection and/or distorted legislation and policies, which in turn may cripple the acceptance and advancement of data science.
Clearly, ethical analysis should be incorporated at all stages of any data science project and since the beginning, in order to understand impact, anticipate risks of unethical consequences, suggest early interventions to avoid or mitigate them, foster resilience, reinforce ethical goals and outcomes, and ensure that ethical best practices are developed, implemented, and appreciated.
In order to pursue these goals, the workshop will:
- map the range of ethical issues that may challenge data science projects;
- outline the agenda for the development of the conceptual framework needed to address them successfully;
- identify potential data science projects that may benchmark such a framework as pilot studies;
- start to build the ethico-methodological capacity for data science at the ATI across the five universities in the consortium; and
- deliver a landscape document.
Welcome & Opening Remarks
What does the recent English attempt to share health data (care.data) tell us about data ethics?
Hetan Shah, The Royal Statistical Society
IP for resilience: data spills and the ethics of information ownership
Burkhard Schafer, Law School, University of Edinburgh
Data science in government
Cat Drew, UK Policy Lab & Data Science, Cabinet Office
The ethics of data as used by algorithms
Jonathan Cave, Department of Economics, University of Warwick
How data teach machines to discriminate
Solon Barocas, Center for Information Technology Policy, Princeton University
Managing distributed accountabilities: on locating ethics in data science
Sabina Leonelli, Department of Sociology, Philosophy and Anthropology, University of Exeter
Different understandings of privacy relevant to data science
Deirdre Mulligan, School of Information, Berkeley Center for Law & Technology, University of California Berkeley
Ethics of data-driven, networked urbanism
Rob Kitchin, National Institute of Regional and Spatial Analysis, National University of Ireland Maynooth
Designing better ethical futures: a rhetorical challenge
Annette Markham, School of Communication and Culture, Centre for Science-Technology-Society Studies, Aarhus University
Big Data implications for citizens, governments and business
Barry O’Sullivan, Insight Centre for Data Analytics, University College Cork, Ireland
Measuring and predicting human behaviour with Internet data
Suzy Moat, Business School, University of Warwick
The value of respect: reclaiming the philosophical and political foundations of informed consent
Anna Lauren Hoffmann, School of Information, University of California Berkeley
Strict and distributed responsibility
Luciano Floridi, Oxford Internet Institute, University of Oxford
Chair: Sofia Charlotta Olhede, Department of Statistical Science, UCL
Delegates: Marina Jirotka, Computer Science Department, University of Oxford; Richard Pinch, GCHQ; Ben Wagner, Centre for Internet & Human Rights, European University Viadrina.
Data Dump to delete
- Name: Hetan Shah|Burkhard Schafer|Cat Drew|Jonathan Cave|Solon Barocas|Sabina Leonelli|Deirdre Mulligan|Rob Kitchin|Annette Markham|Barry O’Sullivan|Suzy Moat|Anna Lauren Hoffmann|Sofia Charlotta Olhede|Marina Jirotka|Richard Pinch|Ben Wagner|Luciano Floridi
- Affiliation: The Royal Statistical Society|Law School, University of Edinburgh|Policy Lab & Data Science, Cabinet Office|Economics Department, University of Warwick|Center for Information Technology Policy, Princeton University|Sociology, Philosophy and Anthropology, University of Exeter|School of Information – Berkeley Center for Law & Technology, University of California Berkeley|National University of Ireland Maynooth|School of Communication and Culture – Centre for Science-Technology-Society Studies, Aharus University|Insight Centre for Data Analytics, University College Cork, Ireland|Business School, University of Warwick|School of Information, University of California Berkeley|Department of Statistical Science, UCL|Computer Science Department, University of Oxford|GCHQ|Europa Universität Viadrina|Oxford Internet Institute, University of Oxford
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