Know Your Model (KYM): Increasing Trust in AI and Machine Learning
arXiv:2106.11036
Abstract
The widespread utilization of AI systems has drawn attention to the potential impacts of such systems on society. Of particular concern are the consequences that prediction errors may have on real-world scenarios, and the trust humanity places in AI systems. It is necessary to understand how we can evaluate trustworthiness in AI and how individuals and entities alike can develop trustworthy AI systems. In this paper, we analyze each element of trustworthiness and provide a set of 20 guidelines that can be leveraged to ensure optimal AI functionality while taking into account the greater ethical, technical, and practical impacts to humanity. Moreover, the guidelines help ensure that trustworthiness is provable and can be demonstrated, they are implementation agnostic, and they can be applied to any AI system in any sector.
arXiv comment10 pages
References in corpus (6)
- Towards A Rigorous Science of Interpretable Machine Learning
- AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
- Differential Privacy and Machine Learning: a Survey and Review
- The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards
- Tutorial: Safe and Reliable Machine Learning
- The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey