DeCoDe: Defer-and-Complement Decision-Making via Decoupled Concept Bottleneck Models

• URL: http://arxiv.org/abs/2505.19220v1
• Date: Sun, 25 May 2025 16:34:45 GMT
• Title: DeCoDe: Defer-and-Complement Decision-Making via Decoupled Concept Bottleneck Models
• Authors: Chengbo He, Bochao Zou, Junliang Xing, Jiansheng Chen, Yuanchun Shi, Huimin Ma,
• Abstract summary: We propose a concept-driven framework for human-AI collaboration.<n>DeCoDe makes strategy decisions based on human-interpretable concept representations.<n>It supports three modes: autonomous AI prediction, deferral to humans, and human-AI collaborative complementarity.
• Score: 37.118479480792416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In human-AI collaboration, a central challenge is deciding whether the AI should handle a task, be deferred to a human expert, or be addressed through collaborative effort. Existing Learning to Defer approaches typically make binary choices between AI and humans, neglecting their complementary strengths. They also lack interpretability, a critical property in high-stakes scenarios where users must understand and, if necessary, correct the model's reasoning. To overcome these limitations, we propose Defer-and-Complement Decision-Making via Decoupled Concept Bottleneck Models (DeCoDe), a concept-driven framework for human-AI collaboration. DeCoDe makes strategy decisions based on human-interpretable concept representations, enhancing transparency throughout the decision process. It supports three flexible modes: autonomous AI prediction, deferral to humans, and human-AI collaborative complementarity, selected via a gating network that takes concept-level inputs and is trained using a novel surrogate loss that balances accuracy and human effort. This approach enables instance-specific, interpretable, and adaptive human-AI collaboration. Experiments on real-world datasets demonstrate that DeCoDe significantly outperforms AI-only, human-only, and traditional deferral baselines, while maintaining strong robustness and interpretability even under noisy expert annotations.

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