Human vs. Algorithmic-Decision Making:

Bias, Fairness & Transparency

Seminar (7 CP)

Winter Semester 2015-16

Instructor: Krishna Gummadi

Teaching Assistants: Oana Goga, Juhi Kulshrestha, Muhammad Bilal Zafar



The concepts of bias, fairness and transparency have been studied previously in philosophical, social, cultural, economic and legal 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 investigate some of these questions. We will study how these concepts are defined and understood in humanities and social sciences and try to map them to algorithmic (data mining and machine learning) frameworks.


Prerequisites for the course include undergraduate courses on algorithms, programming languages, and information systems.Students are also expected to be familiar with material covered in advanced undergraduate / master-level courses in data mining or machine learning.

The course projects would require significant amount of programming effort towards analyzing data or towards implementing and deploying new system designs. Experience in one or more of the programming language such as Java, C/C++, Python is required.


Students interested in participating in the seminar should register by October 30, 2015 using the signup form.


When: regular meetings every Friday from 14:00 to 16:00.

The first meeting is on October 23.

Where: room 005, E1 5 (MPI-SWS building, UdS Campus).

Office hours: Tuesdays 14:00 to 16:00 in room 302, E 1 5.


Each week we will read one paper from the selected topics. Students do not have to present papers, instead one of us (TAs/instructors) will present the paper to initiate the discussion. The course will consist of three components:

Paper Reading List

List of covered papers and the lecture slides are posted here.

Course Project

Details of the course project can be found here.


Paper discussion


Paper reviewing


Research project


Imprint / Data Protection