Responsible Data Science: Avoiding pitfalls and ensuring fairness
Data-driven decision making is widely employed nowadays by businesses, governments and other organizations in order to optimize efficiency and effectiveness of their operations. Decisions once undertaken by humans are increasingly conducted by algorithms, derived through Machine Learning (ML) and Artificial Intelligence (AI) powered by big data. The technology has already penetrated into almost all spheres of human life, from content recommendation and healthcare to predictive policing and autonomous driving, deeply affecting everyone, anywhere, anytime. While data-driven decision making allows previously unthinkable optimizations in the automation of expensive human decision making, the risks that the technology can pose are also high, leading to an ever increasing public concern about the impact of the technology in our lives. In this talk I will argue that many of these risks are the result of misconceptions and violated assumptions and therefore, an accurate understanding of the technology is essential for benefiting from its huge potential while ensuring human values and social good.
In the context of this talk, I will focus on two particular issues:
For each aspect, I will discuss the challenges for data-driven learning and I will present novel solutions to overcome their limitations.
04.12.2019 | 13:00 - 14:15