XAI belongs in production,
not just in papers
Explainability has been a research topic for years. xaitalk makes it a practical tool — so you can actually verify, debug, and trust your AI before it reaches users.
Shadow models for closed-source APIs
Train an open model to replicate a closed-source API on your specific workflow — email classification, content moderation, credit scoring. Then explain every prediction. Replace black-box costs with transparent decisions.
xaitalk.explain(shadow_model, email, method="lrp_epsilon")shadow modelscost reductiontransparency
Verify learning during training
Run attributions at checkpoints to see what your model focuses on at each epoch. Catch Clever Hans moments early — is it learning the feature or the artifact? Compare methods to build confidence before deployment.
xaitalk.compare_methods(model_v3, x, ["gradient", "lrp_epsilon"])training loopClever Hans detectionepoch comparison
Fairness with counterfactual testing
Would the credit decision change if the applicant's gender were different? xaitalk's counterfactual fairness method flips protected attributes and compares attributions — concrete evidence for auditors and regulators.
xaitalk.explain(model, x, method="fairness_counterfactual")counterfactualbias detectionGDPREU AI Act
Give stakeholders real instruments
Doctors reviewing AI-flagged scans, loan officers explaining rejections, compliance teams auditing automated decisions — xaitalk turns model outputs into visual evidence that non-technical people can evaluate.
result = xaitalk.explain(model, x, method="integrated_gradients")human-in-the-loopnon-technical usersaudit trail
Any input dimension. Any architecture.
CNNs, Transformers, LLMs, GNNs, RNNs, diffusion models, protein folders, game engines.
0D
Tabular
Credit scoring, features
1D
Sequences
ECG, audio, text
2D
Images
X-rays, photos, scans
3D
Video
Surveillance, sports
4D
Spatiotemporal
Chess, V-JEPA