隨著人工智慧逐漸應用於醫療、金融及公共決策等重要領域，模型在不同族群間的效能差異與偏差已成為人工智慧的重要議題。然而，現有研究多將公平性、統計推論與自動化學習視為獨立問題，缺乏整合性的系統架構。故本研究提出一個可信多任務學習框架，整合公平性學習、代理式人工智慧機制、統計不確定性分析與視覺化模組，去提升在不同族群間的公平性與穩健性，並強化人工智慧系統的可信度與臨床應用價值。
在方法上，本研究提出一種公平性導向的對比式表示學習策略，結合監督式對比學習與非歧視性正則化，使模型能在保留任務相關資訊的同時，降低對敏感族群特徵的依賴。再來透過自助法、DeLong檢定與多重比較校正，提供具統計支持的公平性與效能推論評估不同族群間的效能差異。此外，本研究導入學習策略代理進行自動化超參數搜尋，以及決策整合代理進行模型集成，去提升模型穩定性與泛化能力，同時維持公平性評估與模型訓練的分離。
本框架於計算病理學中進行具體實現，透過病理切片影像進行多任務預測，包含癌症斷、基因標記與存活分析，並在多個內外部資料集上進行驗證。實驗結果顯示，在訓練資料集中緩解74%顯著偏差、而外部驗證資料集改善44%，在維持整體預測效能情況下，達到統計顯著差異，顯示本方法具備良好的穩健性與泛化能力。未來研究可進一步延伸至多模態學習、交互式公平分析、因果推論與臨床實際部署，以建構更具社會責任與臨床價值的人工智慧系統。

As artificial intelligence (AI) is applied in critical domains such as healthcare, finance, and public decision-making, performance disparities across demographic groups have become a major concern. However, previous research often treats fairness, statistical inference, and automated learning as separate problems. This dissertation proposes TrustMi, a trustworthy multi-task learning framework, that integrates fairness-aware learning, agentic AI mechanisms, statistical uncertainty analysis, and visualization to improve fairness, robustness, and reliability in real-world AI systems.
TrustMi introduces a fairness-aware contrastive representation learning strategy, enabling models to preserve task-relevant information while reducing reliance on demographic-related features. We further incorporate Bootstrapping, DeLong testing, and Benjamini-Hochberg procedure to provide statistically rigorous evaluation of predictive performance and subgroup disparities. In addition, a Learning Policy Agent is designed for adaptive hyperparameter optimization, and a Decision Aggregation Agent enhances ensemble prediction, improving model stability and generalization.
TrustMi is instantiated in whole-slide histopathology images for multi-task prediction, including cancer diagnosis, biomarker prediction, and survival analysis. Across multiple internal and external cohorts, the proposed method mitigated approximately 74% of statistically significant disparities in training datasets and 44% in external validation, while preserving overall predictive performance.
Overall, TrustMi provides a scalable and statistically grounded framework for trustworthy AI. Future research will extend this approach to multimodal learning, causal modeling, and real-world clinical deployment to further enhance fairness and clinical impact.

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