AI2030 Responsible AI Tools Marketplace

Key Takeaways

XAI serves as a critical trust-building tool in healthcare: The paper emphasizes that machine learning algorithms alone cannot generate the human trust necessary for medical applications. XAI methods provide an additional explanatory layer that helps clinicians understand why a model made a specific prediction, addressing the ethical concern of relying on "black box" AI systems to influence patient care decisions.
Different XAI methods reveal feature importance at multiple levels: The research demonstrates that XAI visualization techniques can identify which data features (patient characteristics, test results, etc.) most influenced a model's prediction both for individual cases (local level) and across all predictions (global level), helping medical experts spot identifiable trends in high-dimensional health records.
Transparent "glass-box" models can compete with complex "black-box" models: The paper provides performance comparisons showing that more interpretable model architectures achieve comparable results to complex black-box algorithms on tabular medical data, suggesting healthcare organizations don't necessarily have to sacrifice accuracy to gain explainability.
Human-expert validation of XAI is still needed: The authors note that future work must include user studies to evaluate whether medical professionals actually find these explanations useful and trustworthy in practice—highlighting that technical explainability doesn't automatically translate to practical usability for clinicians.

Abstract

Explainable Artificial Intelligence (XAI) is a rising field in AI. It aims to produce a demonstrative factor of trust, which for human subjects is achieved through communicative means, which Machine Learning (ML) algorithms cannot solely produce, illustrating the necessity of an extra layer producing support to the model output. When approaching the medical field, we can see challenges arise when dealing with the involvement of human-subjects, the ideology behind trusting a machine to tend towards the livelihood of a human poses an ethical conundrum - leaving trust as the basis of the human-expert in acceptance to the machines decision. The aim of this paper is to apply XAI methods to demonstrate the usability of explainable architectures as a tertiary layer for the medical domain supporting ML predictions and human-expert opinion, XAI methods produce visualization of the feature contribution towards a given models output on both a local and global level. The work in this paper uses XAI to determine feature importance towards high-dimensional data-driven questions to inform domain-experts of identifiable trends with a comparison of model-agnostic methods in application to ML algorithms. The performance metrics for a glass-box method is also provided as a comparison against black-box capability for tabular data. Future work will aim to produce a user-study using metrics to evaluate human-expert usability and opinion of the given models.

18 Pages, 11 Figures, Supplementary work supporting a proposal - the existing paper is open to future modification and further development post arXiv submission

Key Takeaways

XAI serves as a critical trust-building tool in healthcare: The paper emphasizes that machine learning algorithms alone cannot generate the human trust necessary for medical applications. XAI methods provide an additional explanatory layer that helps clinicians understand why a model made a specific prediction, addressing the ethical concern of relying on "black box" AI systems to influence patient care decisions.
Different XAI methods reveal feature importance at multiple levels: The research demonstrates that XAI visualization techniques can identify which data features (patient characteristics, test results, etc.) most influenced a model's prediction both for individual cases (local level) and across all predictions (global level), helping medical experts spot identifiable trends in high-dimensional health records.
Transparent "glass-box" models can compete with complex "black-box" models: The paper provides performance comparisons showing that more interpretable model architectures achieve comparable results to complex black-box algorithms on tabular medical data, suggesting healthcare organizations don't necessarily have to sacrifice accuracy to gain explainability.
Human-expert validation of XAI is still needed: The authors note that future work must include user studies to evaluate whether medical professionals actually find these explanations useful and trustworthy in practice—highlighting that technical explainability doesn't automatically translate to practical usability for clinicians.

Abstract

18 Pages, 11 Figures, Supplementary work supporting a proposal - the existing paper is open to future modification and further development post arXiv submission

A Comparative Approach to Explainable Artificial Intelligence Methods in Application to High-Dimensional Electronic Health Records: Examining the Usability of XAI

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links

A Comparative Approach to Explainable Artificial Intelligence Methods in Application to High-Dimensional Electronic Health Records: Examining the Usability of XAI

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links