Interactive Explanations

Enables users to test hypothetical scenarios by modifying input features and observing the resulting prediction changes in real-time. This 'what-if' analysis helps identify the minimal conditions required to alter an outcome.

• Feature Perturbation: Drag sliders to adjust continuous values like income or age
• Threshold Discovery: Automatically find the exact boundary where a loan application flips from denied to approved
• Recourse Generation: Provides actionable steps for users to achieve a desired outcome

For example, a credit analyst can interactively lower an applicant's debt-to-income ratio to see exactly when the model would approve the loan.

Allows users to test how sensitive a model is to high-level, human-defined concepts rather than raw input features. Users can dial the intensity of a concept like 'stripes' or 'redness' to see how it shifts classification probabilities.

• Concept Activation Vectors (CAVs): Define a direction in the model's activation space corresponding to a concept
• Interactive Sliders: Increase or decrease the presence of abstract concepts like 'sentiment' or 'formality'
• Sensitivity Scoring: Quantifies how much each concept influences the final output

This bridges the gap between low-level feature attribution and high-level human reasoning.

Breaks down a model's chain-of-thought reasoning into discrete, collapsible steps that a user can inspect, validate, or challenge. Each step in the logical chain is presented with its supporting evidence and intermediate conclusions.

• Step-by-Step Validation: Approve or reject individual reasoning steps
• Evidence Highlighting: Click on a claim to see the source data that supports it
• Branching Exploration: Explore alternative reasoning paths the model considered but rejected

This transforms a monolithic explanation into an auditable, interactive logic tree suitable for compliance review.

Provides a 2D or 3D interactive map of the model's latent representation space, allowing users to explore how the model organizes knowledge. Users can zoom into clusters of similar cases and understand decision boundaries.

• Dimensionality Reduction: UMAP or t-SNE projections of high-dimensional embeddings
• Nearest Neighbor Inspection: Click any point to see the most semantically similar training examples
• Decision Boundary Overlay: Visualize the hyperplane separating classification regions

This spatial exploration helps identify clusters of model errors or regions of high uncertainty.

Enables side-by-side comparison of two or more predictions to isolate the differentiating factors. Users can select a correctly classified instance and a misclassified one to understand the critical feature divergences.

• Diff View: Highlights the exact feature differences between two cases
• Contrastive Rationale Generation: Automatically generates text explaining why case A was classified differently from case B
• Cohort Selection: Group instances by outcome and compare aggregate feature distributions

This comparative approach is essential for debugging systematic model biases and edge cases.

A credit officer uses an interactive dashboard to test the boundaries of a loan denial. The interface allows them to adjust specific input features—such as debt-to-income ratio or years of employment—in real-time. The system instantly recalculates the decision and highlights the minimal sufficient change required for approval. This transforms a black-box rejection into an actionable, counterfactual exploration session, enabling the officer to provide concrete guidance to the applicant.

A radiologist interacts with a diagnostic AI by submitting follow-up questions about a flagged anomaly. The system generates natural language rationales that ground its reasoning in specific pixel regions of the CT scan. The clinician can ask, 'Why did you ignore the adjacent tissue?' prompting the model to re-evaluate its attention map and provide a contrastive explanation comparing the current case against a library of similar historical cases with known outcomes.

During contract review, a corporate lawyer highlights a specific liability clause and queries the AI assistant. The system provides a rationale for why it flagged the clause as high-risk, citing precedent from a database of case law. The lawyer can then adjust the clause text directly in the interface, and the model interactively re-evaluates the risk score, offering a playground to negotiate terms while maintaining compliance with jurisdictional regulations.

A data scientist debugs a product recommendation model that is underperforming for a specific user segment. Using an interactive explanation tool, they input a synthetic user profile and observe the feature attribution scores in real-time. By toggling features like 'recent search history' or 'price sensitivity,' they identify that the model is overweighting seasonal trends and ignoring long-term preferences. This allows for rapid hypothesis testing before retraining.

After a near-miss event, safety engineers replay the scenario in an interactive simulator. The autonomous driving system provides a temporal rationale, explaining why it chose to swerve right instead of braking. Engineers can inject hypothetical obstacles or alter pedestrian trajectories to probe the model's decision boundaries. The system visualizes its attention over the LiDAR point cloud at each time step, making the split-second logic auditable.

A fraud analyst uses an interactive interface to adjust the classification threshold of a transaction monitoring model. As they move the slider, the interface dynamically updates the projected false positive rate and highlights specific transactions that would flip from 'approved' to 'blocked.' The system generates concept-based explanations for each flipped case, allowing the analyst to balance security against customer friction without needing to understand the underlying neural network architecture.