The Ethics of Data Science: the Landscape for the Alan Turing Institute

Date and Venue 30th November Oxford University
Main organisers: Luciano Floridi, Ian Brown, Sadie Creese, Marina Jirotka, Victoria Nash, Ilina Singh, Burkhard Schafer, David Erdos, Graham Cormode, Richard Moorhead

Summary:
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 (a) map the range of ethical issues that may challenge data science projects; (b) outline the agenda for the development of the conceptual framework needed to address them successfully; (c) identify potential data science projects that may benchmark such a framework as pilot studies; (d) start to build the ethico-methodological capacity for data science at the ATI across the five universities in the consortium; and (e) deliver a landscape document.