2.1 Feature Attribution Methods

Feature attribution techniques identify which input features most influence model predictions. LIME (Local Interpretable Model-agnostic Explanations) approximates complex models locally with interpretable surrogates. SHAP (SHapley Additive exPlanations) applies game-theoretic Shapley values to attribute prediction contributions across features. Integrated Gradients computes gradients along paths from baseline inputs to actual inputs, attributing importance through cumulative gradient magnitude.

These methods enable stakeholders to understand which factors drive specific predictions—crucial for validation, debugging, and trust. However, feature attribution faces limitations: attributions may be unstable across similar inputs, methods can produce conflicting attributions for the same prediction, and attributions don't necessarily reflect causal relationships.

2.2 Attention Visualization

Transformer-based models employ attention mechanisms that can be visualized to reveal which input tokens the model "focuses on" when generating outputs. Attention visualization provides intuitive insights into model reasoning—showing, for example, which words in a document a summarization model emphasizes or which image regions a vision model examines for classification.

However, attention weights don't always correspond to importance in the sense humans expect. Recent research demonstrates that attention patterns can be manipulated without changing outputs, suggesting attention may not reliably indicate true feature importance. This highlights the gap between intuitive interpretability proxies and genuine mechanistic understanding.

2.3 Concept-Based Explanations

Concept activation vectors identify high-level concepts learned by neural networks and measure their influence on predictions. Rather than attributing importance to individual input features, concept-based methods explain predictions in terms of human-understandable concepts—enabling explanations like "this diagnosis was influenced by the presence of inflammation and tissue irregularity" rather than pixel-level attributions.

This approach aligns better with human reasoning, as experts think in terms of domain concepts rather than raw features. However, defining meaningful concepts requires domain expertise, and discovered concepts may not align with human intuitions about important decision factors.

2.4 Mechanistic Interpretability

Mechanistic interpretability aims to reverse-engineer the algorithms implemented by neural networks—identifying specific circuits, neurons, or layer functions responsible for particular capabilities. This research investigates questions like: How do models perform in-context learning? Which circuits implement specific reasoning patterns? How do models represent and manipulate abstract concepts internally?

Mechanistic understanding promises deeper insights than surface-level attribution methods, potentially enabling verification that models reason in intended ways and detection of potentially harmful learned algorithms. However, the complexity of modern networks makes comprehensive mechanistic understanding extremely challenging—current successes focus on specific capabilities in relatively small models.

3.1 Model Cards and Documentation

Comprehensive documentation provides essential transparency beyond technical explainability. Model cards systematically document training data, evaluation metrics, intended use cases, known limitations, and potential biases. This standardized documentation enables stakeholders to make informed decisions about model deployment and interpret model behavior in context.

Effective documentation includes: training data provenance and characteristics, evaluation methodology and results across diverse populations, performance degradation under distributional shift, known failure modes, and guidance for responsible deployment. We publish comprehensive documentation for all deployed systems, updated as new limitations or capabilities are discovered.

3.2 Uncertainty Quantification

Transparent systems communicate uncertainty alongside predictions. Calibrated confidence scores help users appropriately weight AI recommendations. Uncertainty decomposition distinguishes aleatoric uncertainty (inherent randomness in data) from epistemic uncertainty (model knowledge gaps), enabling users to understand whether uncertainty stems from fundamental unpredictability or insufficient training data.

Ensemble methods, Bayesian approaches, and conformal prediction provide principled uncertainty quantification. However, neural networks often produce overconfident predictions, necessitating calibration techniques. We implement temperature scaling and ensemble methods to ensure confidence scores accurately reflect true prediction reliability.

3.3 Audit Trails and Logging

Comprehensive logging enables post-hoc analysis and accountability. We maintain detailed records of model inputs, outputs, intermediate reasoning steps (where applicable), model versions used, and contextual metadata. This audit trail supports incident investigation, bias detection, performance monitoring, and regulatory compliance.

Privacy-preserving logging techniques enable transparency while protecting sensitive information. Differential privacy, secure multi-party computation, and aggregation methods allow analysis of system behavior without exposing individual user data.

3.4 Interactive Explanation Interfaces

Static explanations often fail to address users' specific questions. Interactive interfaces enable stakeholders to query model behavior, explore counterfactual scenarios, and examine decision boundaries. These tools support: "what-if" analysis (exploring how changing inputs affects outputs), example-based explanations (finding similar cases with different outcomes), and sensitivity analysis (identifying which features most impact predictions).

We develop explanation interfaces tailored to different stakeholder needs—simplified visualizations for end users, detailed technical analysis for domain experts, and comprehensive audit tools for regulators. This multi-level transparency ensures appropriate explanation depth for each audience.

4.1 Explanation Fidelity

Post-hoc explanations may not accurately represent true model reasoning. Research demonstrates that plausible explanations can be generated for models that actually rely on entirely different features—a phenomenon called "explanation gaming." This creates risks: stakeholders might trust systems based on reassuring but inaccurate explanations, missing actual sources of unreliability or bias.

Addressing this requires validating explanations against ground truth where possible, comparing multiple explanation methods for consistency, and developing techniques to detect when explanations misrepresent actual model behavior. We employ multiple complementary explanation approaches and flag cases where methods produce conflicting attributions.

4.2 Scalability

Many interpretability methods scale poorly to large models and datasets. Computing exact Shapley values requires exponentially many model evaluations. Mechanistic interpretability faces combinatorial explosion analyzing billions of parameters. This scalability challenge necessitates approximations, sampling strategies, and focus on specific model components—introducing tradeoffs between explanation completeness and computational feasibility.

4.3 User Comprehension

Technical explanations may overwhelm or confuse non-expert users. Research on human-AI interaction reveals that users often misinterpret feature importance visualizations, overweight salient but irrelevant features, and fail to appropriately calibrate trust based on explanation complexity. Effective explainability requires not just technical accuracy but also psychological validity—explanations must align with how humans actually reason and make decisions.

5.1 Open Documentation

We publish comprehensive documentation for all deployed systems, including training data characteristics, evaluation methodology, known limitations, and performance across demographic groups. This documentation is continuously updated as we discover new capabilities or failure modes. We openly share evaluation methods, allowing external researchers to independently verify our claims and identify potential issues we may have missed.

5.2 Multi-Method Explanations

We employ multiple complementary explanation techniques—feature attribution, attention visualization, concept-based explanations, and uncertainty quantification—providing stakeholders with diverse perspectives on model reasoning. When methods agree, this increases confidence in explanations. When methods disagree, we flag these cases for additional scrutiny, as disagreement often indicates edge cases or unreliable predictions.

5.3 External Auditing

We engage independent third-party auditors to evaluate our systems, providing access to model internals, training data (where privacy-preserving), and comprehensive logging. This external validation offers accountability beyond self-assessment, helping identify blind spots in our internal evaluation and building public trust through demonstrable transparency.

5.4 Ongoing Research

We invest in advancing interpretability research—exploring mechanistic interpretability, developing more faithful explanation methods, and creating better tools for communicating AI reasoning to diverse stakeholders. This research commitment recognizes that current explanation techniques remain imperfect and that transparency will require continued innovation as AI systems grow in complexity.