Dates
Location
Register
- Abstract
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.
Programme
| Start | End | Schedule |
|---|---|---|
| 08:45:00 | 09:00:00 |
Registration |
| 09:00:00 | 09:10:00 |
Welcome & Opening Remarks |
| 09:10:00 | 09:30:00 |
What does the recent English attempt to share health data (care.data) tell us about data ethics? Hetan Shah, The Royal Statistical Society |
| 09:30:00 | 09:50:00 |
IP for resilience: data spills and the ethics of information ownership Burkhard Schafer, Law School, University of Edinburgh |
| 09:50:00 | 10:10:00 |
Data science in government Cat Drew, UK Policy Lab & Data Science, Cabinet Office |
| 10:10:00 | 10:30:00 |
The ethics of data as used by algorithms Jonathan Cave, Department of Economics, University of Warwick |
| 10:30:00 | 11:10:00 |
Discussion |
| 11:10:00 | 11:40:00 |
Coffee Break |
| 11:40:00 | 12:00:00 |
How data teach machines to discriminate Solon Barocas, Center for Information Technology Policy, Princeton University |
| 12:00:00 | 12:20:00 |
Managing distributed accountabilities: on locating ethics in data science Sabina Leonelli, Department of Sociology, Philosophy and Anthropology, University of Exeter |
| 12:20:00 | 12:40:00 |
Different understandings of privacy relevant to data science Deirdre Mulligan, School of Information, Berkeley Center for Law & Technology, University of California Berkeley |
| 12:40:00 | 13:20:00 |
Discussion |
| 13:20:00 | 14:30:00 |
Lunch |
| 14:30:00 | 14:50:00 |
Ethics of data-driven, networked urbanism Rob Kitchin, National Institute of Regional and Spatial Analysis, National University of Ireland Maynooth |
| 14:50:00 | 15:10:00 |
Designing better ethical futures: a rhetorical challenge Annette Markham, School of Communication and Culture, Centre for Science-Technology-Society Studies, Aarhus University |
| 15:10:00 | 15:30:00 |
Big Data implications for citizens, governments and business Barry O’Sullivan, Insight Centre for Data Analytics, University College Cork, Ireland |
| 15:30:00 | 16:10:00 |
Discussion |
| 16:10:00 | 16:40:00 |
Coffee Break |
| 16:40:00 | 17:00:00 |
Measuring and predicting human behaviour with Internet data Suzy Moat, Business School, University of Warwick |
| 17:00:00 | 17:20:00 |
The value of respect: reclaiming the philosophical and political foundations of informed consent Anna Lauren Hoffmann, School of Information, University of California Berkeley |
| 17:20:00 | 17:40:00 |
Strict and distributed responsibility Luciano Floridi, Oxford Internet Institute, University of Oxford |
| 17:40:00 | 18:30:00 |
Round Table 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. |
| 18:30:00 | 19:30:00 |
Wine Reception |
About the speakers
-
Hetan Shah
Affiliation: The Royal Statistical Society -
Burkhard Schafer
Affiliation: Law School, University of Edinburgh -
Cat Drew
Affiliation: Policy Lab & Data Science, Cabinet Office -
Jonathan Cave
Affiliation: Economics Department, University of Warwick -
Solon Barocas
Affiliation: Center for Information Technology Policy, Princeton University -
Sabina Leonelli
Affiliation: Sociology, Philosophy and Anthropology, University of Exeter -
Deirdre Mulligan
Affiliation: School of Information - Berkeley Center for Law & Technology, University of California Berkeley -
Rob Kitchin
Affiliation: National University of Ireland Maynooth -
Annette Markham
Affiliation: School of Communication and Culture - Centre for Science-Technology-Society Studies, Aharus University -
Barry O’Sullivan
Affiliation: Insight Centre for Data Analytics, University College Cork, Ireland -
Suzy Moat
Affiliation: Business School, University of Warwick -
Anna Lauren Hoffmann
Affiliation: School of Information, University of California Berkeley -
Sofia Charlotta Olhede
Affiliation: Department of Statistical Science, UCL -
Marina Jirotka
Affiliation: Computer Science Department, University of Oxford -
Richard Pinch
Affiliation: GCHQ -
Ben Wagner
Affiliation: Europa Universität Viadrina -
Luciano Floridi
Affiliation: Oxford Internet Institute, University of Oxford