Advances in Engineering Innovation

Advances in Engineering Innovation

Vol. 3, 23 October 2023


Open Access | Article

Exploring methods to make AI decisions more transparent and understandable for humans

Dawood Ali MoDastoni * 1
1 Saint Leo University

* Author to whom correspondence should be addressed.

Advances in Engineering Innovation, Vol. 3 Advances in Engineering Innovation,
Published 23 October 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Dawood Ali MoDastoni. Exploring methods to make AI decisions more transparent and understandable for humans. AEI (2023) Vol. 3. DOI: 10.54254/2977-3903/3/2023037.

Abstract

As Artificial Intelligence (AI) systems increasingly weave into the fabric of diverse sectors, their intricate and often opaque decision-making processes pose challenges to users and stakeholders alike. The 'black box' nature of AI, especially deep learning models, highlights a pressing need for transparency and interpretability. This paper delves into the significance of making AI decisions transparent and provides a comprehensive exploration of methods aimed at demystifying AI processes. Through the lens of Explainable AI (XAI) and advanced visualization tools, we underscore the importance of bridging the chasm between sophisticated AI operations and human-centric understanding. By fostering transparency, it is anticipated that AI systems can not only enhance efficacy but also fortify trust, ensuring that decisions are both informed and explicable.

Keywords

artificial intelligence, explainable AI (XAI), transparency, decision-making processes, visualization tools

References

1. Russell, S. J., & Norvig, P. (2010). Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited.

2. Castelvecchi, D. (2016). Can we open the black box of AI? Nature News, 538(7623), 20.

3. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.

4. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144).

5. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

6. Gunning, D. (2017). Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web.

7. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in neural information processing systems (pp. 4765-4774).

8. Wattenberg, M., Viégas, F. B., & Borning, A. (2016). How to use t-SNE effectively. Distill.

9. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining.

10. Wattenberg, M., Viégas, F., & Johnson, I. (2016). How to Use t-SNE Effectively. Distill.

11. Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., & Lipson, H. (2015). Understanding neural networks through deep visualization. arXiv preprint arXiv:1506.06579.

12. Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE international conference on computer vision.

13. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. *Adv

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this journal agree to the following terms:

1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).

Volume Title
ISBN (Print)
ISBN (Online)
Published Date
23 October 2023
Series
Advances in Engineering Innovation
ISSN (Print)
2977-3903
ISSN (Online)
2977-3911
DOI
10.54254/2977-3903/3/2023037
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated