This page provides the security targets of negative impacts “Economic loss caused by a decline in AI usability” in the external influence aspect in the AI Security Map, as well as the attacks and factors that cause them, and the corresponding defense methods and countermeasures.
Security target
- Consumer
Attack or cause
- Integrity violation
- Availability breach
- Explainability violation
- Degradation of output fairness
- Degradation of safety
- Degradation of accuracy
- Degradation of controllability
- Reliability violation
- Degradation of autonomy
Defensive method or countermeasure
- RAG
- XAI (Explainable AI)
- Uncertainty quantification
References
RAG
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, 2020
- REALM: Retrieval-Augmented Language Model Pre-Training, 2020
- In-Context Retrieval-Augmented Language Models, 2023
- Active Retrieval Augmented Generation, 2023
- Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection, 2023
- Query Rewriting for Retrieval-Augmented Large Language Models, 2023
- Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering, 2023
- Generate rather than Retrieve: Large Language Models are Strong Context Generators, 2023
- Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy, 2023
- From Local to Global: A Graph RAG Approach to Query-Focused Summarization, 2024
Search for other references related to “RAG” in the literature database
XAI (Explainable AI)
- Visualizing and Understanding Convolutional Networks, 2014
- Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, 2014
- Understanding Deep Image Representations by Inverting Them, 2014
- “Why Should I Trust You?”: Explaining the Predictions of Any Classifier, 2016
- A Unified Approach to Interpreting Model Predictions, 2017
- Learning Important Features Through Propagating Activation Differences, 2017
- Understanding Black-box Predictions via Influence Functions, 2017
- Interpretable Explanations of Black Boxes by Meaningful Perturbation, 2017
- Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV), 2018
- Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization, 2019