Name
Abstract | ||
|---|---|---|
Socially sensitive decisions about critical issues such as employment, credit scoring, or insurance premiums are increasingly automated based on big data mining. Although algorithms do not have personal preferences, they are not neutral, and the data itself can reflect various undesirable biases. The authors discuss how discrimination-aware data mining constitutes a crucial step to counter automated discrimination. They then explain why the complexity of legal and social norms requires a balanced interdisciplinary methodology and toolset comprising requirements relating to data accuracy, protection, and provenance, and legitimacy of targeted objectives. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/MIS.2016.96 | IEEE Intelligent Systems |
Keywords | Field | DocType |
Law,Decision making,Data analysis,Artificial intelligence,Data protection,Data mining,Big data,Consumer protection | Data science,Data accuracy,Data mining,Intelligent decision support system,Big data mining,Computer science,Norm (social),Legitimacy,Data Protection Act 1998,Big data | Journal |
Volume | Issue | ISSN |
31 | 6 | 1541-1672 |
Citations | PageRank | References |
6 | 0.64 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laura Carmichael | 1 | 6 | 1.32 |
| Sophie Stalla-Bourdillon | 2 | 18 | 10.03 |
| Steffen Staab | 3 | 6658 | 593.89 |