Towards Model-informed Precision Dosing with Expert-in-the-loop Machine Learning

TL;DR

This paper presents a human-in-the-loop machine learning framework designed to enhance interpretability, reduce annotation costs, and mitigate bias, demonstrated through an application in precision dosing.

Contribution

It introduces a novel human-in-the-loop ML approach that incorporates expert feedback to improve interpretability and efficiency in high-cost data annotation scenarios.

Findings

Learns interpretable rules from data effectively.

Reduces experts' workload by replacing data annotation with rule editing.

Potentially decreases algorithmic bias through expert feedback.

Abstract

Machine Learning (ML) and its applications have been transforming our lives but it is also creating issues related to the development of fair, accountable, transparent, and ethical Artificial Intelligence. As the ML models are not fully comprehensible yet, it is obvious that we still need humans to be part of algorithmic decision-making processes. In this paper, we consider a ML framework that may accelerate model learning and improve its interpretability by incorporating human experts into the model learning loop. We propose a novel human-in-the-loop ML framework aimed at dealing with learning problems that the cost of data annotation is high and the lack of appropriate data to model the association between the target tasks and the input features. With an application to precision dosing, our experimental results show that the approach can learn interpretable rules from data and may potentially lower experts' workload by replacing data annotation with rule representation editing. The approach may also help remove algorithmic bias by introducing experts' feedback into the iterative model learning process.