rankaudit 0.1.1

Auditing and explainability library for ranking systems

RankAudit

Auditing and explainability toolkit for ranking systems.

Ranking systems decide what people see and in what order — jobs, loans, news, products. RankAudit makes them inspectable.

---

What is RankAudit?

Most ranking systems — search engines, recommendation feeds, LLM re-rankers are black boxes. They produce an ordered list with no explanation of why document A ranked above B, whether the ranking is fair across demographic groups, or what a lower-ranked item would need to change to move up.

RankAudit is a single Python library that closes this gap. It gives engineers and researchers four capabilities:

Capability	What it answers
Measure	How good is the ranking? (NDCG, MAP, MRR, Precision@k)
Explain	Why did this document rank here? (ranking-aware SHAP/LIME)
Interrogate	What would it take for this document to rank higher? (counterfactuals)
Detect	Is the ranking fair across groups? (exposure, parity, position-relevance)

---

Why not existing tools?

Tool	Gap
SHAP / LIME	Explain a single score in isolation — ignore that ranking is comparative
pytrec_eval / ranx	Compute quality metrics but give no insight into why or where unfairness lives
Fairlearn / AI Fairness 360	Designed for classifiers, not ranked lists
RankAudit	Ranking-native attribution + counterfactuals + fairness, unified API

---

Installation

pip install rankaudit                  # core only (numpy)
pip install "rankaudit[shap]"          # + SHAP attribution
pip install "rankaudit[lime]"          # + LIME attribution
pip install "rankaudit[llm]"           # + RankExplain (Anthropic / OpenAI)
pip install "rankaudit[pandas]"        # + DataFrame loader
pip install "rankaudit[all]"           # everything

---

Quick start

import rankaudit as ra

report = ra.audit(
    ranker=my_ranker,
    queries=["best laptop under $1000"],
    docs=doc_corpus,
    metrics=["ndcg", "fairness"],
)

print(report.summary())
print(report.explain(doc_id="doc_42"))

---

Inputs

RankAudit accepts four input formats. All normalise to the same internal QueryDocPair representation.

Option 1 — Raw queries + doc list (simplest)

queries = ["best laptop under $1000", "noise cancelling headphones"]

docs = [
    {
        "id": "doc_1",
        "text": "The MacBook Air M3 offers excellent value...",
        "relevance": 3,          # graded relevance label (0–3), optional
        "group": "apple",        # group label for bias analysis, optional
        "features": {            # numeric features your ranker uses
            "bm25": 0.82,
            "semantic_sim": 0.91,
            "recency": 0.74,
            "click_rate": 0.55,
        },
    },
    ...
]

report = ra.audit(ranker=my_ranker, queries=queries, docs=docs)

Option 2 — QueryDocPair objects (full control)

from rankaudit import QueryDocPair

pairs = [
    QueryDocPair(
        query_id="q1",
        query_text="best laptop under $1000",
        doc_id="doc_1",
        doc_text="The MacBook Air M3 offers excellent value...",
        features={"bm25": 0.82, "semantic_sim": 0.91, "recency": 0.74},
        relevance=3.0,
        metadata={"group": "apple"},
    ),
    ...
]

report = ra.audit(ranker=my_ranker, pairs=pairs)

Option 3 — pandas DataFrame

import pandas as pd
import rankaudit as ra

df = pd.DataFrame({
    "query_id":          ["q1",    "q1",    "q1"],
    "query_text":        ["best laptop"] * 3,
    "doc_id":            ["doc_1", "doc_2", "doc_3"],
    "doc_text":          ["MacBook Air...", "Dell XPS...", "Lenovo ThinkPad..."],
    "relevance":         [3.0,     2.0,     1.0],
    "feat_bm25":         [0.82,    0.61,    0.55],   # feat_ prefix → features dict
    "feat_semantic_sim": [0.91,    0.78,    0.70],
    "group":             ["apple", "dell",  "lenovo"],
})

pairs = ra.from_dataframe(df)
report = ra.audit(ranker=my_ranker, pairs=pairs)

Option 4 — TREC / LETOR benchmark files

pairs = ra.load_trec(qrel_path="robust04.qrel", run_path="bm25.run")
pairs = ra.load_letor(path="MQ2007/Fold1/train.txt")
report = ra.audit(ranker=my_ranker, pairs=pairs)

Implementing a ranker

Any object with a score method works — scikit-learn estimators, PyTorch modules, ONNX sessions, and plain callables all qualify.

class MyRanker:
    def score(self, pairs: list[QueryDocPair]) -> list[tuple[str, float]]:
        # Return (doc_id, score) for every pair — order doesn't matter
        return [(p.doc_id, sum(p.features.values())) for p in pairs]

What is required vs optional on each input

Field	Required?	Used by
query_id, query_text	Yes	All steps
doc_id, doc_text	Yes	All steps
features	No	Attribution (SHAP/LIME) and counterfactuals only
relevance	No	IR metrics (NDCG, MAP, MRR, Precision@k) — all return 0 if omitted
metadata["group"]	No	Bias detector — exposure and parity analysis only run when group labels are present

What you get without optional fields:

# No features, no relevance, no group labels — still works
pairs = [
    QueryDocPair(query_id="q1", query_text="query", doc_id="d1", doc_text="doc text"),
    ...
]

# Skip the steps that need features
report = ra.audit(
    ranker=my_ranker,
    pairs=pairs,
    attribution=None,       # requires features
    counterfactuals=False,  # requires features
)
# → report.metrics will have NDCG/MAP of 0 (no relevance labels)
# → report.bias will have position_bias only (no group labels)

Full audit requires:

• features — for attribution and counterfactuals
• relevance — for meaningful NDCG, MAP, MRR, Precision@k values
• metadata["group"] — for exposure and demographic parity bias analysis

---

Outputs

ra.audit(...) returns an AuditReport object with four output surfaces.

report.summary()

=== RankAudit Report ===
Queries audited : 2
Documents ranked: 20
Avg NDCG@10     : 0.7841
Attributions    : 20 documents explained
Counterfactuals : 4 flip examples
Bias checks     : 2 queries analysed

report.metrics — ranking quality

All four metrics require relevance labels on your input pairs. They return 0 if no relevance labels are provided.

Metric	What it measures	metrics= value
NDCG@k	Quality of the top-k ranking, weighted by position	"ndcg"
Precision@k	Fraction of relevant docs in the top-k	"precision"
MAP	Average precision across all relevant docs	"map"
MRR	How high the first relevant doc appears	"mrr"
Fairness / bias	Exposure and parity across groups	"fairness"

Pass any combination to ra.audit(metrics=[...]). Default is ["ndcg", "fairness"].

for m in report.metrics:
    print(m.query_id, m.ndcg_at_k, m.map_score, m.mrr_score)

# q1  {1: 1.0, 3: 0.92, 5: 0.87, 10: 0.78}  0.81  1.0
# q2  {1: 0.5, 3: 0.71, 5: 0.74, 10: 0.69}  0.67  0.5

report.explain() — feature attribution

Ranking-aware attribution showing which features drove a document's position:

print(report.explain(doc_id="doc_2"))

Explanation for doc 'doc_2' (query 'q1') — method: shap
  Base score   : 0.5312
  Final score  : 0.7841
  Top feature contributions:
    semantic_sim    +0.1820
    bm25            +0.0934
    recency         -0.0381
    click_rate      -0.0144

report.counterfactuals — rank flip analysis

Minimal feature change that would flip the order of two documents:

for cf in report.counterfactuals:
    print(f"Query {cf.query_id}: '{cf.doc_b_id}' (rank {cf.original_rank_b}) "
          f"would overtake '{cf.doc_a_id}' (rank {cf.original_rank_a}) if:")
    for feat, (current, needed) in cf.flipping_changes.items():
        print(f"  {feat}: {current:.3f} → {needed:.3f}")

# Query q1: 'doc_2' (rank 2) would overtake 'doc_1' (rank 1) if:
#   recency: 0.450 → 0.720

report.bias — fairness metrics

Exposure and demographic parity across group labels:

for b in report.bias:
    print(b.query_id, b.exposure_bias, b.demographic_parity)
    for note in b.notes:
        print(" [!]", note)

# q1  {'apple': 0.4821, 'dell': 0.2103, 'lenovo': 0.1874}  ...
# [!] Exposure ratio between groups is 2.57x — significant disparity detected.
# [!] Demographic parity gap is 0.50 — one group dominates top-k results.

RankExplain — plain-English explanations via LLM

from anthropic import Anthropic
from rankaudit import RankExplain

explainer = RankExplain(client=Anthropic(), model="claude-sonnet-4-6")

print(explainer.explain_report(report))
print(explainer.explain_doc(report, doc_id="doc_2"))
print(explainer.explain_counterfactuals(report))
print(explainer.explain_bias(report))

The ranking system performs well overall (NDCG@10: 0.78) but shows a notable
recency penalty — newer documents are discounted by ~30% relative to older ones
with comparable semantic scores. The 'apple' group receives 2.5x more exposure
than other groups, which may warrant investigation if group identity should not
influence ranking position...

Serialisation

report.to_json()   # full report as a JSON string
report.to_dict()   # plain Python dict

---

Citation

If you use RankAudit in your research, please cite:

@software{parab2026rankaudit,
  author  = {Parab, Mandar Narendra},
  title   = {{RankAudit}: Auditing and Explainability for Ranking Systems},
  year    = {2026},
  url     = {https://github.com/m-np/rankaudit},
  license = {Apache-2.0}
}

---

Contributing

Contributions are welcome. Please open an issue before submitting a large PR so we can align on direction. See CONTRIBUTING.md for guidelines.

---

License

Copyright 2026 Mandar Narendra Parab. Licensed under the Apache License 2.0.