Toward Accountable Discrimination-Aware Data Mining: The Importance of Keeping the Human in the Loop—and Under the Looking Glass
| Autor: | Bettina Berendt, Sören Preibusch |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Engineering
Information Systems and Management Decision Making Big data 02 engineering and technology computer.software_genre user studies 020204 information systems 0202 electrical engineering electronic engineering information engineering Data Mining Humans Data Protection Act 1998 media_common.cataloged_instance Human-in-the-loop European union Implementation media_common data protection evaluation business.industry Information technology Data science Computer Science Applications discrimination-aware and fairness-aware data mining accountability General Data Protection Regulation discrimination discovery and prevention 020201 artificial intelligence & image processing Data mining User interface business computer Information Systems |
| Zdroj: | Big Data. 5:135-152 |
| ISSN: | 2167-647X 2167-6461 |
| DOI: | 10.1089/big.2016.0055 |
| Popis: | "Big Data" and data-mined inferences are affecting more and more of our lives, and concerns about their possible discriminatory effects are growing. Methods for discrimination-aware data mining and fairness-aware data mining aim at keeping decision processes supported by information technology free from unjust grounds. However, these formal approaches alone are not sufficient to solve the problem. In the present article, we describe reasons why discrimination with data can and typically does arise through the combined effects of human and machine-based reasoning, and argue that this requires a deeper understanding of the human side of decision-making with data mining. We describe results from a large-scale human-subjects experiment that investigated such decision-making, analyzing the reasoning that participants reported during their task to assess whether a loan request should or would be granted. We derive data protection by design strategies for making decision-making discrimination-aware in an accountable way, grounding these requirements in the accountability principle of the European Union General Data Protection Regulation, and outline how their implementations can integrate algorithmic, behavioral, and user interface factors. ispartof: Big Data vol:5 issue:2 pages:135-152 ispartof: location:United States status: published |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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