How Do Software Companies Deal with Artificial Intelligence Ethics? : A Gap Analysis
Abstract
The public and academic discussion on Artificial Intelligence (AI) ethics is accelerating and the general public is becoming more aware AI ethics issues such as data privacy in these systems. To guide ethical development of AI systems, governmental and institutional actors, as well as companies, have drafted various guidelines for ethical AI. Though these guidelines are becoming increasingly common, they have been criticized for a lack of impact on industrial practice. There seems to be a gap between research and practice in the area, though its exact nature remains unknown. In this paper, we present a gap analysis of the current state of the art by comparing practices of 39 companies that work with AI systems to the seven key requirements for trustworthy AI presented in the “The Ethics Guidelines for Trustworthy Artificial Intelligence”. The key finding of this paper is that there is indeed notable gap between AI ethics guidelines and practice. Especially practices considering the novel requirements for software development, requirements of societal and environmental well-being and diversity, nondiscrimination and fairness were not tackled by companies.
Main Authors
Format
Conferences
Conference paper
Published
2022
Subjects
Publication in research information system
Publisher
ACM
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202303292297Käytä tätä linkitykseen.
Parent publication ISBN
978-1-4503-9613-4
Review status
Peer reviewed
DOI
https://doi.org/10.1145/3530019.3530030
Conference
International Conference on Evaluation and Assessment in Software Engineering
Language
English
Is part of publication
EASE 2022 : The International Conference on Evaluation and Assessment in Software Engineering
Citation
- Vakkuri, V., Kemell, K.-K., Tolvanen, J., Jantunen, M., Halme, E., & Abrahamsson, P. (2022). How Do Software Companies Deal with Artificial Intelligence Ethics? : A Gap Analysis. In M. Staron, C. Berger, J. Simmonds, & R. Prikladnicki (Eds.), EASE 2022 : The International Conference on Evaluation and Assessment in Software Engineering (pp. 100-109). ACM. https://doi.org/10.1145/3530019.3530030
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