🤖 AI Summary
Conventional evaluation metrics (e.g., accuracy, F1) fail to reflect the practical value of AI models in human-AI collaborative settings—particularly when low-confidence predictions require human intervention—leading to suboptimal model selection. Method: We propose a task-aware value-oriented evaluation paradigm grounded in cost-sensitive decision theory, jointly modeling costs of correct predictions, misclassifications, and human review. Crucially, we emphasize probability calibration—not model complexity—as the primary determinant of real-world value, enhancing reliability via Platt scaling or temperature scaling. Contribution/Results: Empirical validation across diverse operational scenarios demonstrates that standard metrics frequently select inferior models, whereas simple yet well-calibrated models substantially increase end-to-end workflow value. Our framework provides an interpretable, optimization-friendly, value-driven assessment methodology for enterprise-scale AI deployment.
📝 Abstract
In this paper, we argue that the prevailing approach to training and evaluating machine learning models often fails to consider their real-world application within organizational or societal contexts, where they are intended to create beneficial value for people. We propose a shift in perspective, redefining model assessment and selection to emphasize integration into workflows that combine machine predictions with human expertise, particularly in scenarios requiring human intervention for low-confidence predictions. Traditional metrics like accuracy and f-score fail to capture the beneficial value of models in such hybrid settings. To address this, we introduce a simple yet theoretically sound"value"metric that incorporates task-specific costs for correct predictions, errors, and rejections, offering a practical framework for real-world evaluation. Through extensive experiments, we show that existing metrics fail to capture real-world needs, often leading to suboptimal choices in terms of value when used to rank classifiers. Furthermore, we emphasize the critical role of calibration in determining model value, showing that simple, well-calibrated models can often outperform more complex models that are challenging to calibrate.
