Work

A Comparative Assessment and Synthesis of Twenty Ethics Codes on AI and Big Data

Year: 2020

Type: conference-paper

Abstract: 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.

Source: IEEE Swiss Conference on Data Science (SDS)

DOI: 10.1109/sds49233.2020.00015

Authors: Michele Loi, Christoph Heitz, Markus Christen

Institutions: University of Zurich, ZHAW Zurich University of Applied Sciences

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

Open Access status: green

Funders: Hans Böckler Stiftung, Innosuisse - Schweizerische Agentur für Innovationsförderung

OpenAlex ID: W3045012088