AIセキュリティマップにマッピングされた外部作用的側面における負の影響「AIのユーザビリティが低下」のセキュリティ対象、それをもたらす攻撃・要因、および防御手法・対策を示しています。
セキュリティ対象
- 消費者
攻撃・要因
- 完全性の毀損
- 可用性の毀損
- 精度の毀損
- 制御可能性の毀損
- 出力の公平性の毀損
防御手法・対策
開発・活用における適用フェーズ
1. データ収集・前処理
2. モデルの選定・学習・検証
- 不確実性の定量化
3. システムの実装
- RAG
4. システムの提供・運用・保守
- 出力の有害度の算出
- XAI（説明可能なAI）
- 不確実性の定量化
5. システムの利用
参考文献
出力の有害度の算出
RAG
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, 2020.0
- REALM: Retrieval-Augmented Language Model Pre-Training, 2020.0
- In-Context Retrieval-Augmented Language Models, 2023.0
- Active Retrieval Augmented Generation, 2023.0
- Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection, 2023.0
- Query Rewriting for Retrieval-Augmented Large Language Models, 2023.0
- Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering, 2023.0
- Generate rather than Retrieve: Large Language Models are Strong Context Generators, 2023.0
- Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy, 2023.0
- From Local to Global: A Graph RAG Approach to Query-Focused Summarization, 2024.0
XAI（説明可能なAI）
- Visualizing and Understanding Convolutional Networks, 2014.0
- Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, 2014.0
- Understanding Deep Image Representations by Inverting Them, 2014.0
- “Why Should I Trust You?”: Explaining the Predictions of Any Classifier, 2016.0
- A Unified Approach to Interpreting Model Predictions, 2017.0
- Learning Important Features Through Propagating Activation Differences, 2017.0
- Understanding Black-box Predictions via Influence Functions, 2017.0
- Interpretable Explanations of Black Boxes by Meaningful Perturbation, 2017.0
- Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV), 2018.0
- Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization, 2019.0
不確実性の定量化
- Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding, 2015.0
- Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning, 2016.0
- Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles, 2017.0
- Predictive Uncertainty Estimation via Prior Networks, 2018.0
- Evidential Deep Learning to Quantify Classification Uncertainty, 2018.0
- Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift, 2019.0
- Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods, 2021.0