Name
Abstract | ||
|---|---|---|
Discrimination in decision making is prohibited on many attributes (religion, gender, etc驴), but often present in historical decisions. Use of such discriminatory historical decision making as training data can perpetuate discrimination, even if the protected attributes are not directly present in the data. This work focuses on discovering discrimination in instances and preventing discrimination in classification. First, we propose a discrimination discovery method based on modeling the probability distribution of a class using Bayesian networks. This measures the effect of a protected attribute (e.g., gender) in a subset of the dataset using the estimated probability distribution (via a Bayesian network). Second, we propose a classification method that corrects for the discovered discrimination without using protected attributes in the decision process. We evaluate the discrimination discovery and discrimination prevention approaches on two different datasets. The empirical results show that a substantial amount of discrimination identified in instances is prevented in future decisions. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s10506-014-9156-4 | Artif. Intell. Law |
Keywords | Field | DocType |
protected attribute,future decision,discrimination discovery,historical decision,discriminatory historical decision,Bayesian network,Combating discrimination,discrimination prevention approach,decision process,decision making,discrimination discovery method | Data science,Computer science,Knowledge management,Bayesian network | Journal |
Volume | Issue | ISSN |
22 | 2 | 1572-8382 |
Citations | PageRank | References |
9 | 0.55 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Koray Mancuhan | 1 | 14 | 3.02 |
| Chris Clifton | 2 | 3327 | 544.44 |