Summary of Fostering Trust and Quantifying Value Of Ai and Ml, by Dalmo Cirne and Veena Calambur
Fostering Trust and Quantifying Value of AI and ML
by Dalmo Cirne, Veena Calambur
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the challenge of developing trustworthy artificial intelligence (AI) and machine learning (ML) systems. The authors highlight that while much has been discussed about trusting AI and ML inferences, there is a lack of frameworks to quantify and measure transparency, explainability, safety, bias, and other key aspects. To increase the value of ML-based products and engage users, the authors propose a framework for computing trust scores based on metrics such as model performance, data quality, and user feedback. This framework aims to provide an objective understanding of how trustworthy a machine learning system can claim to be over time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about making sure that AI and ML systems are honest and reliable. Right now, there’s no way to measure if these systems are trustworthy or not. The authors want to change this by creating a framework that shows how good or bad an AI/ML system is based on things like how well it works, the quality of the data it uses, and what people think about it. This will help make people trust these systems more and improve them over time. |
Keywords
» Artificial intelligence » Machine learning
