Challenge AI Mind: A Crowd System for Proactive AI Testing

TL;DR

This paper introduces Challenge.AI, a crowd-powered system that combines crowdsourcing and machine learning to proactively generate and evaluate testing data, improving AI testing coverage and error detection.

Contribution

The paper presents a novel crowd system integrating machine learning for proactive AI testing, enabling dynamic data generation and comprehensive error analysis.

Findings

Crowd workflow is more effective with machine learning assistance.

AI developers can discover previously unknown errors.

The system enhances proactive testing engagement.

Abstract

Artificial Intelligence (AI) has burrowed into our lives in various aspects; however, without appropriate testing, deployed AI systems are often being criticized to fail in critical and embarrassing cases. Existing testing approaches mainly depend on fixed and pre-defined datasets, providing a limited testing coverage. In this paper, we propose the concept of proactive testing to dynamically generate testing data and evaluate the performance of AI systems. We further introduce Challenge.AI, a new crowd system that features the integration of crowdsourcing and machine learning techniques in the process of error generation, error validation, error categorization, and error analysis. We present experiences and insights into a participatory design with AI developers. The evaluation shows that the crowd workflow is more effective with the help of machine learning techniques. AI developers found that our system can help them discover unknown errors made by the AI models, and engage in the process of proactive testing.