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.
Iván Palomares Carrascosa writes about methods for interpreting the dense numerical vector representations, or embeddings, generated by large language models (LLMs). By using a combination of probing classifiers like logistic regression, UMAP dimensionality reduction for visualization, and SHAP values to identify influential latent dimensions, one can analyze the quality and semantic structure captured within LLM-generated embedding spaces.
- Probing classifiers help determine if embeddings are rich enough to distinguish between classes by testing them with simpler models.
- UMAP is used to project high-dimensional embeddings into 2D space for visual inspection of natural groupings.
- SHAP values can pinpoint which specific dimensions in an embedding most significantly influence a classifier's decisions.
- The article demonstrates using Scikit-LLM alongside local Ollama models to generate embeddings cost-effectively.
- Probing classifiers help determine if embeddings are rich enough to distinguish between classes by testing them with simpler models.
- UMAP is used to project high-dimensional embeddings into 2D space for visual inspection of natural groupings.
- SHAP values can pinpoint which specific dimensions in an embedding most significantly influence a classifier's decisions.
- The article demonstrates using Scikit-LLM alongside local Ollama models to generate embeddings cost-effectively.
MIT researchers developed a system that uses large language models to convert AI explanations into narrative text that can be more easily understood by users, aiming to help with better decision-making about model trustworthiness.
The system, called EXPLINGO, leverages large language models (LLMs) to convert machine-learning explanations, such as SHAP plots, into easily comprehensible narrative text. The system consists of two parts: NARRATOR, which generates natural language explanations based on user preferences, and GRADER, which evaluates the quality of these narratives. This approach aims to help users understand and trust machine learning predictions more effectively by providing clear and concise explanations.
The researchers hope to further develop the system to enable interactive follow-up questions from users to the AI model.
The system, called EXPLINGO, leverages large language models (LLMs) to convert machine-learning explanations, such as SHAP plots, into easily comprehensible narrative text. The system consists of two parts: NARRATOR, which generates natural language explanations based on user preferences, and GRADER, which evaluates the quality of these narratives. This approach aims to help users understand and trust machine learning predictions more effectively by providing clear and concise explanations.
The researchers hope to further develop the system to enable interactive follow-up questions from users to the AI model.
An article detailing how to build a flexible, explainable, and algorithm-agnostic ML pipeline with MLflow, focusing on preprocessing, model training, and SHAP-based explanations.
This article provides a non-technical guide to interpreting SHAP analyses, useful for explaining machine learning models to non-technical stakeholders, with a focus on both local and global interpretability using various visualization methods.
This article explores the use of Isolation Forest for anomaly detection and how SHAP (KernelSHAP and TreeSHAP) can be applied to explain the anomalies detected, providing insights into which features contribute to anomaly scores.
This article explores how stochastic regularization in neural networks can improve performance on unseen categorical data, especially high-cardinality categorical features. It uses visualizations and SHAP values to understand how entity embeddings respond to this regularization technique.
Generating counterfactual explanations got a lot easier with CFNOW, but what are counterfactual explanations, and how can I use them?
