A Maturity Model for Trustworthy AI Software Development-Reference-Cited by-同舟云学术

A Maturity Model for Trustworthy AI Software Development

Published:2023-04-10 Issue:8 Volume:13 Page:4771
ISSN:2076-3417
Container-title:Applied Sciences
language:en
Short-container-title:Applied Sciences

Author:

Cho Seunghwan¹,Kim Ingyu²,Kim Jinhan²,Woo Honguk³^ORCID,Shin Wanseon¹

Affiliation:

1. Graduate School of Technology Management, Sungkyunkwan University, Suwon 16419, Republic of Korea

2. Samsung Research, Seoul 06765, Republic of Korea

3. Department of Computer Science and Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea

Abstract

Recently, AI software has been rapidly growing and is widely used in various industrial domains, such as finance, medicine, robotics, and autonomous driving. Unlike traditional software, in which developers need to define and implement specific functions and rules according to requirements, AI software learns these requirements by collecting and training relevant data. For this reason, if unintended biases exist in the training data, AI software can create fairness and safety issues. To address this challenge, we propose a maturity model for ensuring trustworthy and reliable AI software, known as AI-MM, by considering common AI processes and fairness-specific processes within a traditional maturity model, SPICE (ISO/IEC 15504). To verify the effectiveness of AI-MM, we applied this model to 13 real-world AI projects and provide a statistical assessment on them. The results show that AI-MM not only effectively measures the maturity levels of AI projects but also provides practical guidelines for enhancing maturity levels.

Funder

Institute for Information & communications Technology Planning & Evaluation

ICT Creative Consilience program supervised by the IITP

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Link

https://www.mdpi.com/2076-3417/13/8/4771/pdf

Reference36 articles.

1. Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., Hirschberg, J., Kalyanakrishnan, S., Kamar, E., and Kraus, S. One Hundred Year Study on Artificial Intelligence: Report of the 2015–2016 Study Panel, Stanford University. Available online: http://ai100.stanford.edu/2016-report.

2. Balakrishnan, T., Chui, M., Hall, B., and Henke, N. (2023, February 20). The state of AI in 2020. Mckinsey Global Institute. Available online: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/global-survey-the-state-of-ai-in-2020.

3. Needhan, M. (2023, February 20). IDC Forecasts Improved Growth for Global AI Market in 2021. Available online: https://www.businesswire.com/news/home/20210223005277/en/IDC-Forecasts-Improved-Growth-for-Global-AI-Market-in-2021.

4. Babic, B., Cohen, I.G., Evgeniou, T., and Gerke, S. (2021). When Machine Learning Goes off the Rails. Harv. Bus. Rev., Available online: https://hbr.org/2021/01/when-machine-learning-goes-off-the-rails.

5. Schwartz, O. (2023, February 20). 2016 Microsoft’s Racist Chatbot Revealed the Dangers of Online Conversation. IEEE Spectrum. Available online: https://spectrum.ieee.org/in-2016-microsofts-racist-chatbot-revealed-the-dangers-of-online-conversation.

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Development and Evaluation of a Maturity Model for AI Deployment Capability of Manufacturing Companies;Information Systems Management;2024-02-29