(F)airness, (A)ccountability, (T)ransparency and (E)xplainability in Data-driven Decision Systems

* Overview * Publications * Software * People



The concepts of fairness, accountability, transparency and explainability 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 (as well as human) decision-making frameworks. While these concepts are somewhat intuitive, quantifying and incorporating 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 make sure that an algorithm does not (un)intentionally discriminate against people from certain social backgrounds? Our work tries to address these issues by proposing novel techniques to detect, quantify and remove such biases from decision making systems.






Faculty   Student / Post doc  
* Manuel Gomez Rodriguez
* Krishna P. Gummadi
  * Nina Grgić-Hlača
* Juhi Kulshrestha
* Till Speicher
* Muhammad Bilal Zafar
* Abhijnan Chakraborty


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