Model Review: A PROMISEing Opportunity

TL;DR

This paper advocates for a shift in the PROMISE community towards model review to enhance understandability and correctness of models, leveraging prior successes in simplifying complex data mining models.

Contribution

It proposes a new focus on model review within the PROMISE community to improve human understanding and trust in AI models, building on previous work in model simplification.

Findings

Simple models can perform well, challenging complexity assumptions.

Data mining techniques can effectively summarize large models and datasets.

A community shift could improve model interpretability and reliability.

Abstract

To make models more understandable and correctable, I propose that the PROMISE community pivots to the problem of model review. Over the years, there have been many reports that very simple models can perform exceptionally well. Yet, where are the researchers asking "say, does that mean that we could make software analytics simpler and more comprehensible?" This is an important question, since humans often have difficulty accurately assessing complex models (leading to unreliable and sometimes dangerous results). Prior PROMISE results have shown that data mining can effectively summarizing large models/ data sets into simpler and smaller ones. Therefore, the PROMISE community has the skills and experience needed to redefine, simplify, and improve the relationship between humans and AI.