AN APPROACH FOR DISCRIMINATION PREVENTION IN DATA MINING

By Rupanjali Dive, Anagha P. Khedkar. 2014

4 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Bryce Goodman
2. Seth R. Flaxman
3. Daniel S. Weld
4. Gagan Bansal
5. Eva Thelisson
6. Zachary Chase Lipton
7. Been Kim
8. Wojciech Samek

In Computational Theory and Mathematics

1. Bryce Goodman
2. Seth R. Flaxman

In Information Systems

1. Bryce Goodman
2. Seth R. Flaxman

Citations in the corpus

• European Union Regulations on Algorithmic Decision-Making and a "Right to Explanation" (2016, methodology): “Furthermore, a number of recent studies have attempted to tackle the issue of discrimination within algorithms by introducing tools to both identify [5, 29] and rectify [9, 16, 32, 6, 12, 14] cases of unwanted bias.”
• EU regulations on algorithmic decision-making and a "right to explanation" (2016, methodology): “…to tackle the issue of discrimination within algorithms by introducing tools to both identify (Berendt & Preibusch, 2012; Sandvig et al., 2014) and rectify (Calders & Verwer, 2010; Hajian et al., 2011; Zliobaite et al., 2011; Berendt & Preibusch, 2014; Dive & Khedkar, 2014) cases of unwanted bias.”