A Comparative Assessment and Synthesis of Twenty Ethics Codes on AI and Big Data
| Autor: | Markus Christen, Christoph Heitz, Michele Loi |
|---|---|
| Přispěvatelé: | University of Zurich |
| Rok vydání: | 2020 |
| Předmět: |
Artificial intelligence
business.industry Computer science Management science media_common.quotation_subject Control (management) Big data 170: Ethik 610 Medicine & health Bioethics 006: Spezielle Computerverfahren Ethical values Transparency (behavior) Ethical guideline Promotion (rank) Action (philosophy) Accountability Data ethic 10222 Institute of Biomedical Ethics and History of Medicine business media_common |
| Zdroj: | SDS |
| DOI: | 10.1109/sds49233.2020.00015 |
| Popis: | Up to date, more than 80 codes exist for handling ethical risks of artificial intelligence and big data. In this paper, we analyse where those codes converge and where they differ. Based on an in-depth analysis of 20 guidelines, we identify three procedural action types (1. control and document, 2. inform, 3. assign responsibility) as well as four clusters of ethical values whose promotion or protection is supported by the procedural activities. We achieve a synthesis of previous approaches with a framework of seven principles, combining the four principles of biomedical ethics with three distinct procedural principles: control, transparency and accountability. |
| Databáze: | OpenAIRE |
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