bias-detection

name	bias-detection
description	Detect demographic bias in LYDUS data quality metrics by computing all 16 QUIQ quality metrics (completeness, range/date/format/sequence validity, preciseness, fidelity, class/instance diversity, sentence/vocabulary diversity, logical accuracy, cross-sectional/time-series consistency, classification, note accuracy/fidelity) per Sex/Race/Age group and computing GDI (Group Disparity Index). Uses Claude CLI (no API key required).
tier	community
category	lydus
parameters	{"quiq_path":{"description":"Path to QUIQ-format CSV file.","type":"string"},"via_path":{"description":"Path to VIA (Variable Information Annotation) CSV file. Used by format-validity and cross-sectional-consistency.","type":"string"},"save_path":{"description":"Directory path to save output CSVs and sub-metric plots.","type":"string"},"operation_type_manual":{"description":"Passed to logical-accuracy skill. false = automatic mode. Default false.","type":"boolean"},"automatic_num":{"description":"Passed to logical-accuracy skill. Number of variables to analyze automatically. Default 5.","type":"integer"},"recommend_num":{"description":"Passed to logical-accuracy skill. Number of correlated variables to recommend. Default 5.","type":"integer"}}

Bias Detection

Detects demographic bias in data quality by running all 16 LYDUS quality metrics on patient subgroups (Sex, Race, Age) and comparing scores. The GDI (Group Disparity Index) quantifies how much any group deviates from the overall mean for each metric.

When to Use This Skill

• After QUIQ conversion, to assess whether data quality differs across demographic groups
• To identify metrics where one group is systematically under-served or over-represented
• To generate an equity report for LYDUS quality management assessment
• Before clinical ML model training, to detect data quality biases that may propagate into model bias

SQL Support

Not applicable. Orchestrates Python implementations of all 16 sub-metrics plus Claude CLI calls.

Pipeline Overview

1. LLM identifies Sex / Race / BirthDate columns in QUIQ
2. Build per-patient demographics pivot (one row per Patient_id)
3. Compute Age_Group from BirthDate + Event_date
4. For each group in {Sex, Race, Age_Group}:
     Run all 16 quality metrics on the group-filtered QUIQ subset
5. Build results table: rows = metrics, cols = group labels + Mean + GDI

16 Sub-Metrics

Metric	API Key	Skill
Range Validity	✗	range-validity
Date Validity	optional	date-validity
Format Validity	optional	format-validity
Sequence Validity	Claude CLI	sequence-validity
Completeness	✗	completeness
Logical Accuracy	Claude CLI	logical-accuracy
Cross-Sectional Consistency	Claude CLI	cross-sectional-consistency
Time Series Consistency	✗	time-series-consistency
Class Diversity	✗	class-diversity
Instance Diversity	✗	instance-diversity
Fidelity	✗	fidelity
Preciseness	✗	preciseness
Accuracy / Precision / Recall / F1 / AUROC	✗	classification
Note Fidelity	Claude CLI	note-fidelity
Note Accuracy	Claude CLI	note-accuracy
Vocabulary Diversity	✗	vocabulary-diversity
Sentence Diversity	✗	sentence-diversity

GDI (Group Disparity Index)

GDI = max(|group_score − mean_score|) across all groups for each metric.

• GDI = 0 → all groups have identical scores (no bias)
• High GDI → large disparity between groups for that metric

Demographic Grouping

Group	Column	Detection	Age Bins
Sex	LLM-identified	biological sex variable	—
Race	LLM-identified	race variable	—
Age	BirthDate + Event_date	date of birth variable	0-9, 10-19, …, 80+

Rows with null Sex/Race/Age_Group are dropped before group iteration.

Output

File	Description
bias_sex_summary.csv	Metrics × sex groups + Mean + GDI
bias_race_summary.csv	Metrics × race groups + Mean + GDI
bias_age_summary.csv	Metrics × age groups + Mean + GDI
Sub-metric plots	SHAP plots, AUROC plots, boxplots (saved to save_path)

How to Run

import pandas as pd
from scripts.bias_detection import get_bias_detection

quiq = pd.read_csv("/path/to/quiq.csv")
via  = pd.read_csv("/path/to/via.csv")

config = {
    'save_path':            '/path/to/output',
    'operation_type_manual': False,
    'target_variable':      '',
    'automatic_num':        5,
    'recommend_num':        5,
}

df_sex, df_race, df_age = get_bias_detection(quiq, via, config)
print(df_sex)

As a script with config

# config.yaml
quiq_path:            /path/to/quiq.csv
via_path:             /path/to/via.csv
save_path:            /path/to/output
operation_type_manual: false
target_variable:      ""
automatic_num:        5
recommend_num:        5

python scripts/bias_detection.py --config config.yaml

Critical Notes

1. All 16 sub-metrics run per group — each metric is wrapped in try/except; failures return np.nan without stopping the run. Expect long runtimes for large datasets or many groups.

2. Skill dependency imports — bias_detection.py dynamically adds sibling skills' scripts/ directories to sys.path at runtime. All 17 sibling skills must be present under the same skills root (~/.claude/skills/ or src/m4/skills/lydus/).

3. 원본 코드 버그 수정:

   • model_ver / api_key 제거: _llm_ask_column이 Claude CLI subprocess로 대체됨
   • get_time_series_consistency(config['save_path'], quiq) 인자 순서 반전 → (quiq, save_path) 로 수정
   • get_code_validity 반환값 3개 언팩 → 2개(validation_df, error_summary)로 수정
   • get_unstructured_fidelity / get_unstructured_accuracy → M4 스킬의 get_note_fidelity / get_note_accuracy 로 교체
   • config['save_path'] = -1 하드코딩 → 실제 save_path 사용

4. GDI 계산 — df.sub(df['Mean'], axis=0).abs().max(axis=1): Mean 열을 먼저 계산 후 그룹 열들과의 최대 절댓값 편차. Mean 열 자체는 GDI 계산에서 제외.

5. Age bins — pd.cut(..., right=False): 0-9 includes 0 and excludes 10. 80+ = [80, 120).

6. Dependencies — all 16 sub-metric skill dependencies (see each skill's SKILL.md). LLM 호출은 Claude CLI via subprocess — API 키 불필요.

References

• LYDUS 품질관리 프로그램 활용 가이드라인 (비공개 내부 문서)
• Original Python implementation: LYDUS_Bias_Detection.py (이성민 작성)