| Kurzkommentar |
Abstract:
The concepts of bias, fairness and transparency have been studied
thoroughly in philosophical, social and cultural frameworks. However, as
automated data analysis replaces human supervision and intuition in
decision making, and the scale of the data analyzed becomes “big”, there
is a growing need for incorporating these concepts into algorithmic
decision-making frameworks [1, 2,3]. While these concepts are somewhat
intuitive, quantifying them in algorithmic frameworks is a non-trivial
task. For example, what does it mean for an algorithm to be fair or
unbiased? Or, how do we quantify the extent to which an algorithm is
discriminating against subjects of a protected class, and so on.
In this seminar, we will try to answer some of these questions. We will
start off by studying the definitions of these concepts and try to map
them to algorithmic frameworks. Algorithmic way of quantifying these
concepts exposes their underlying complexity. We will see that the
philosophical definitions of these concepts allow for a large number of
subjective solutions. For example, different humans can interpret the
definition of impartiality (or lack of bias) in different ways. On the
other hand, the algorithmic quantification of bias has to be very precise.
In order to solve these problems, we will take a computational approach
towards understanding these concepts. |
