A great number of academic and industry initiatives seek to mitigate what are increasingly understood as ethical concerns in the development, implementation, and evaluation of algorithms in social contexts. Often, when confronted with questions of ethics within these socio-technical systems, engineers ,data scientists, and others turn to the field of Philosophy for help in trying to decide the balance between the right thing to do and what we can do. In this talk, Dr. Rodriguez presents her arguments for why most strands of moral philosophy are ill equipped to help us deal with the realities of algorithmic bias. Her arguments use evidence gleaned from years of professional experience in human services, her study of moral philosophy, as well as arguments put forth by a school of Black Philosophers. The aim of the talk is to introduce the audience to more contextual forms of philosophizing, ones grounded in the realities of marginalized existence.

As practising Data Science researchers and practitioners, the COVID-19 pandemic has highlighed both the need for data driven decision making and the reality of what it really takes to get to that point. It is not only about throwing data + model at a problem. It is about understanding the environment that one is in and then strategising on what might best work for that environment. In this talk I look back at some of the work we have done within responding to different challenges within both Data Science and Natural Language Processing. I place at the center people and how they are the important piece in our practice.

We study the effects of a reform that increased the stakes of the national exam at the end of high school for university enrolment in Spain. We find a negative effect of the reform on female test scores, driven by a worse performance in the exams for which the stakes increase the most, and driven by students expected to be top performers. The effect on test scores translates into significant changes in students' allocation to college: female enrolment in the most selective degrees declines, together with expected earnings.

We will discuss the following questions. What is democratic backsliding? What makes a democracy stable? Which processes potentially lead to instability of a democratic system? How can complexity science help us understand and mitigate democratic backsliding? The talk is based on this paper and this paper.

Colleges and universities are among those that have been most affected by the COVID-19 pandemic. Even with a potential end to the ongoing global restrictions in sight, the manner in which these institutions operate is likely to be permanently altered as a result of the significant impact of the virus. Most notably, a more traditional, siloed approach to research will play a key role in effectively meeting the needs of local communities. Additionally, the existing teaching-learning paradigm will need to be reimagined, keeping in mind the transformation of the educational landscape. This session will address key global trends in the education sector, as well as propose some ideas on how universities can adopt a more proactive approach to cope with these changes.