Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning

TL;DR

Zeno is a versatile interactive framework that helps practitioners visualize and test machine learning models for systematic failures across various real-world use cases, addressing a gap in existing tools.

Contribution

The paper introduces Zeno, a general-purpose framework for behavioral evaluation of ML models, supported by insights from practitioner interviews and multiple case studies.

Findings

Practitioners can reproduce manual failure analyses using Zeno.

Zeno enables discovery of new systematic failures in real-world models.

The framework supports diverse use cases beyond domain-specific tools.

Abstract

Machine learning models with high accuracy on test data can still produce systematic failures, such as harmful biases and safety issues, when deployed in the real world. To detect and mitigate such failures, practitioners run behavioral evaluation of their models, checking model outputs for specific types of inputs. Behavioral evaluation is important but challenging, requiring that practitioners discover real-world patterns and validate systematic failures. We conducted 18 semi-structured interviews with ML practitioners to better understand the challenges of behavioral evaluation and found that it is a collaborative, use-case-first process that is not adequately supported by existing task- and domain-specific tools. Using these findings, we designed Zeno, a general-purpose framework for visualizing and testing AI systems across diverse use cases. In four case studies with participants using Zeno on real-world models, we found that practitioners were able to reproduce previous manual analyses and discover new systematic failures.