Benjamin Nweke writes that traditional fraud detection relies on the assumption of a human actor, where deviations from established behavioral patterns serve as primary signals. While explainability tools like SHAP can effectively detail why specific transaction features (like amount or timing) trigger a risk score, they are insufficient for addressing "machine-to-machine mayhem" caused by autonomous agents. Because these agents lack human biological constraints and consistent life patterns, feature attribution on transactions fails to capture the underlying intent or decision-making trajectory of an agent that may be operating outside its delegated scope.
- Agentic AI fraud is characterized as a shift toward "machine-to-machine mayhem" where bots mimic legitimate shopping agents.
- Current explainability methods like SHAP focus on transaction features rather than the actor's underlying decision path or tool usage.
- 60% of industry professionals expect AI-mediated banking to diminish the effectiveness of traditional fraud defenses.
- Proposed regulatory responses include NIST's Agent Standards Initiative and Senator Mark Warner's proposed AI AGENT Act for establishing accountability through registries.
- Agentic AI fraud is characterized as a shift toward "machine-to-machine mayhem" where bots mimic legitimate shopping agents.
- Current explainability methods like SHAP focus on transaction features rather than the actor's underlying decision path or tool usage.
- 60% of industry professionals expect AI-mediated banking to diminish the effectiveness of traditional fraud defenses.
- Proposed regulatory responses include NIST's Agent Standards Initiative and Senator Mark Warner's proposed AI AGENT Act for establishing accountability through registries.
gSMILE is a model-agnostic framework designed to provide interpretability for large language models by explaining how specific parts of a prompt influence the generated output. The system functions by making minor variations to input prompts and measuring subsequent changes in responses to identify high-impact words, which are then presented as visual heat maps. This approach aims to demystify black-box systems like GPT, Llama, and Claude for use cases where trust and accountability are essential.
- Model-agnostic interpretability specifically for generative AI solutions.
- Identification of influential tokens through input perturbation.
- Visualization of prompt significance via heat maps.
- Empirical validation using accuracy, consistency, stability, and fidelity metrics.
- Model-agnostic interpretability specifically for generative AI solutions.
- Identification of influential tokens through input perturbation.
- Visualization of prompt significance via heat maps.
- Empirical validation using accuracy, consistency, stability, and fidelity metrics.
This article explains permutation feature importance (PFI), a popular method for understanding feature importance in explainable AI. The author walks through calculating PFI from scratch using Python and XGBoost, discussing the rationale behind the method and its limitations.
