alibi-explainability

alibi-explainability

Alibi Explain provides explanation algorithms answering: "How do predictions change with feature inputs? Which features matter for a prediction? What minimal changes would alter a prediction? How does each feature contribute to predictions?" per the Alibi Explain docs.

When to use

• Compliance / regulator inquiry: "explain this denied loan prediction" - generate counterfactual + per-feature attribution.
• Model debugging: a single instance got the wrong answer; explain why.
• High-risk system audit (EU AI Act Annex III): every prediction ships with a stored explanation record.

Step 1 - Install

pip install alibi

Per the Alibi Explain docs.

Step 2 - Pick the right explainer category

Per the Alibi Explain docs:

Category	Explainers	When
Global feature attribution	Accumulated Local Effects (ALE), Partial Dependence	"Across the whole input space, how does feature X drive output?"
Local necessary features	Anchors, Pertinent Positives	"What minimal feature subset locks in this prediction?"
Local feature attribution	Integrated Gradients, Kernel SHAP, Tree SHAP	"What did each feature contribute to this prediction?"
Counterfactual	Counterfactual Instances, CEM, CFProto, CounterfactualRL	"What minimal change flips this prediction?"

Step 3 - The two-method interface

Every Alibi explainer follows the same pattern:

explainer.fit(X_train)        # Some explainers — preparation phase
explanation = explainer.explain(instance)
print(explanation.data)       # Per-explainer schema

Per the Alibi Explain docs Explainer Interface section.

Step 4 - Anchors example (tabular)

from alibi.explainers import AnchorTabular

predict_fn = lambda x: classifier.predict(x)
explainer = AnchorTabular(
    predict_fn,
    feature_names=FEATURE_NAMES,
    categorical_names=CATEGORICAL_INDEX,
)
explainer.fit(X_train)

explanation = explainer.explain(X_test[0])
print("Anchor: %s" % (" AND ".join(explanation.anchor)))
print("Precision: %.2f" % explanation.precision)
print("Coverage: %.2f" % explanation.coverage)

Anchors return a minimal feature subset such that the prediction holds with precision confidence over coverage of the input space.

Step 5 - Counterfactual example

from alibi.explainers import CounterfactualProto

cf = CounterfactualProto(
    predict_fn,
    shape=X_train[0:1].shape,
    use_kdtree=True,
    theta=10.,
)
cf.fit(X_train)

explanation = cf.explain(X_test[0:1])
print("Counterfactual: %s" % explanation.cf["X"])
print("Original class: %d, CF class: %d" % (
    explanation.orig_class, explanation.cf["class"]
))

Counterfactual = "the closest input that flips the prediction" - auditor-friendly format.

Step 6 - Persist explanations as audit records

import json
from pathlib import Path

def explain_and_log(instance_id, instance, explainer, log_dir="explanations"):
    explanation = explainer.explain(instance)
    record = {
        "instance_id": instance_id,
        "timestamp": "...",
        "explainer": type(explainer).__name__,
        "data": explanation.data,
        "meta": explanation.meta,
    }
    Path(log_dir, f"{instance_id}.json").write_text(json.dumps(record))

For high-risk systems, store every explanation alongside the prediction (immutable audit log). Pair with qa-compliance/audit-trail-test-author in the testland-qa marketplace for storage assertions.

Step 7 - Don't confuse with alibi-detect

Alibi Explain is the explanation library. Drift detection uses the sister package alibi-detect (pip install alibi-detect) - that covers concept drift, adversarial detection, outlier detection. They share governance but are separate packages.

Anti-patterns

Anti-pattern	Why it fails	Fix
Use Kernel SHAP on every prediction in real time	O(n) model calls per explanation; latency-killer	Tree SHAP for tree models; cache for repeated instances
Show feature attributions to non-technical stakeholders	"0.3 contribution from 'income'" is jargon	Use Counterfactuals (Step 5) - natural-language friendly
Skip fit() step	Some explainers (Anchors) need training data summary	Always fit on representative data (Step 3)
Treat explanation as ground-truth causality	Attributions are model-relative, not causal	Document this in audit trail metadata
Mix alibi and alibi-detect packages	Different scope; same install string causes confusion	Install both explicitly when needed (Step 7)

Limitations

• Counterfactual explainers can produce out-of-distribution instances; constrain via prototypes or domain rules.
• Integrated Gradients requires gradient access (TF/PyTorch native); no support for opaque APIs (SaaS LLMs).

References

• Alibi Explain docs - explainer categories, interface, install, per-method documentation