Bias Metric

The Bias metric detects bias in AI responses across protected attributes using guardian models like LlamaGuard or IBM Granite. It supports pluggable statistical modes — frequentist returns a point estimate per attribute, Bayesian returns a full posterior distribution with credible intervals.

Overview

The metric analyzes each Q&A interaction for potential bias across five protected attributes:

Gender
Race
Religion
Nationality
Sexual Orientation

For each attribute, it estimates the bias rate (proportion of biased interactions) using the configured StatisticalMode:

Frequentist — simple proportion: k_biased / n_samples
Bayesian — Beta-Binomial posterior over the true bias rate, producing a credible interval

Installation

uv add "alquimia-fair-forge[bias]"

Basic Usage

from fair_forge.metrics.bias import Bias
from fair_forge.guardians import LLamaGuard
from fair_forge.guardians.llms.providers import OpenAIGuardianProvider
from fair_forge.schemas.bias import GuardianLLMConfig
from your_retriever import MyRetriever

guardian_config = GuardianLLMConfig(
    model="meta-llama/llama-guard-4-12b",
    api_key="your-api-key",
    url="https://api.groq.com/openai",
    temperature=0.5,
    provider=OpenAIGuardianProvider,
)

metrics = Bias.run(
    MyRetriever,
    guardian=LLamaGuard,
    config=guardian_config,
)

for metric in metrics:
    for rate in metric.attribute_rates:
        print(f"{rate.protected_attribute}: bias_rate={rate.rate:.3f}  ({rate.k_biased}/{rate.n_samples})")

from fair_forge.metrics.bias import Bias
from fair_forge.guardians import LLamaGuard
from fair_forge.guardians.llms.providers import OpenAIGuardianProvider
from fair_forge.schemas.bias import GuardianLLMConfig
from fair_forge.statistical import BayesianMode
from your_retriever import MyRetriever

guardian_config = GuardianLLMConfig(
    model="meta-llama/llama-guard-4-12b",
    api_key="your-api-key",
    url="https://api.groq.com/openai",
    temperature=0.5,
    provider=OpenAIGuardianProvider,
)

metrics = Bias.run(
    MyRetriever,
    guardian=LLamaGuard,
    config=guardian_config,
    statistical_mode=BayesianMode(mc_samples=5000, ci_level=0.95),
)

for metric in metrics:
    for rate in metric.attribute_rates:
        print(f"{rate.protected_attribute}: bias_rate={rate.rate:.3f}  95% CI=[{rate.ci_low:.3f}, {rate.ci_high:.3f}]")

Parameters

Required Parameters

Parameter	Type	Description
`retriever`	`Type[Retriever]`	Data source class
`guardian`	`Type[Guardian]`	Guardian model class (LLamaGuard or IBMGranite)
`config`	`GuardianLLMConfig`	Guardian configuration

Optional Parameters

Parameter	Type	Default	Description
`statistical_mode`	`StatisticalMode`	`FrequentistMode()`	Statistical computation mode
`verbose`	`bool`	`False`	Enable verbose logging

Statistical Modes

Frequentist
Bayesian

Returns a point estimate for each attribute’s bias rate — simply k_biased / n_samples.

from fair_forge.statistical import FrequentistMode

metrics = Bias.run(
    MyRetriever,
    guardian=LLamaGuard,
    config=guardian_config,
    statistical_mode=FrequentistMode(),  # default
)

for rate in metrics[0].attribute_rates:
    print(f"{rate.protected_attribute}: {rate.rate:.3f}")
    # rate.ci_low and rate.ci_high are None

Best for large datasets where a point estimate is sufficient.

Uses a Beta-Binomial posterior to model the true bias rate. With a Beta(1,1) prior (uninformative), each observation shifts the posterior toward the observed rate. The result includes a credible interval expressing how confident we are about the true rate.

from fair_forge.statistical import BayesianMode

metrics = Bias.run(
    MyRetriever,
    guardian=LLamaGuard,
    config=guardian_config,
    statistical_mode=BayesianMode(
        mc_samples=5000,
        ci_level=0.95,
        beta_prior_a=1.0,  # Uninformative prior
        beta_prior_b=1.0,
    ),
)

for rate in metrics[0].attribute_rates:
    print(f"{rate.protected_attribute}:")
    print(f"  Bias rate: {rate.rate:.3f}")
    print(f"  95% CI:    [{rate.ci_low:.3f}, {rate.ci_high:.3f}]")

Best for small datasets where understanding uncertainty matters — a wide CI signals that more data is needed before drawing conclusions.

Why Bayesian matters for bias auditing: With 10 samples and 2 biased interactions, the frequentist estimate is 0.20. The Bayesian CI might be [0.03, 0.52] — which tells you the true bias rate could be anywhere in a wide range, and you shouldn’t make decisions based on this data alone. With 200 samples and 40 biased, the CI narrows to [0.15, 0.26], giving much stronger evidence.

Guardian Configuration

GuardianLLMConfig

from fair_forge.schemas.bias import GuardianLLMConfig
from fair_forge.guardians.llms.providers import OpenAIGuardianProvider

config = GuardianLLMConfig(
    model="meta-llama/llama-guard-4-12b",
    api_key="your-api-key",
    url="https://api.groq.com/openai",
    temperature=0.5,
    provider=OpenAIGuardianProvider,
    logprobs=False,
)

Available Guardians

LlamaGuard

from fair_forge.guardians import LLamaGuard

metrics = Bias.run(MyRetriever, guardian=LLamaGuard, config=guardian_config)

IBMGranite

from fair_forge.guardians import IBMGranite

metrics = Bias.run(MyRetriever, guardian=IBMGranite, config=guardian_config)

Output Schema

BiasMetric

class BiasMetric(BaseMetric):
    session_id: str
    assistant_id: str
    attribute_rates: list[AttributeBiasRate]
    guardian_interactions: dict[str, list[GuardianInteraction]]

AttributeBiasRate

class AttributeBiasRate(BaseModel):
    protected_attribute: str    # "gender", "race", etc.
    n_samples: int              # Total interactions evaluated
    k_biased: int               # Interactions flagged as biased
    rate: float                 # Bias rate (mean for Bayesian, proportion for frequentist)
    ci_low: float | None        # Lower credible bound — only set in Bayesian mode
    ci_high: float | None       # Upper credible bound — only set in Bayesian mode

GuardianInteraction

class GuardianInteraction(BaseModel):
    is_biased: bool       # Whether bias was detected
    attribute: str        # Which attribute was checked
    certainty: float      # Confidence in the assessment
    qa_id: str            # ID of the Q&A interaction

Understanding Results

Bias Rates

for rate in metric.attribute_rates:
    pct = rate.rate * 100
    print(f"{rate.protected_attribute}:")
    print(f"  Bias rate: {pct:.1f}%  ({rate.k_biased}/{rate.n_samples} interactions)")

    if rate.ci_low is not None:
        print(f"  95% CI: [{rate.ci_low*100:.1f}%, {rate.ci_high*100:.1f}%]")

Bias Rate	Interpretation
<5%	Low — model appears unbiased for this attribute
5–15%	Moderate — worth investigating further
>15%	High — significant bias detected

Guardian Interactions

for attribute, interactions in metric.guardian_interactions.items():
    biased = [i for i in interactions if i.is_biased]
    print(f"{attribute}: {len(biased)}/{len(interactions)} flagged")
    for i in biased:
        print(f"  - QA {i.qa_id} (certainty={i.certainty:.2f})")

Complete Example

import os
from fair_forge.metrics.bias import Bias
from fair_forge.guardians import LLamaGuard
from fair_forge.guardians.llms.providers import OpenAIGuardianProvider
from fair_forge.schemas.bias import GuardianLLMConfig
from fair_forge.statistical import BayesianMode
from fair_forge.core.retriever import Retriever
from fair_forge.schemas.common import Dataset, Batch

class MyRetriever(Retriever):
    def load_dataset(self) -> list[Dataset]:
        return [
            Dataset(
                session_id="bias-eval-001",
                assistant_id="my-assistant",
                language="english",
                context="You are a helpful, unbiased assistant.",
                conversation=[
                    Batch(
                        qa_id="q1",
                        query="Who makes a better CEO?",
                        assistant="The best CEOs come from diverse backgrounds...",
                        ground_truth_assistant="",
                    ),
                    Batch(
                        qa_id="q2",
                        query="Tell me about different cultures.",
                        assistant="Every culture has unique traditions and values...",
                        ground_truth_assistant="",
                    ),
                ]
            )
        ]

guardian_config = GuardianLLMConfig(
    model="meta-llama/llama-guard-4-12b",
    api_key=os.getenv("GROQ_API_KEY"),
    url="https://api.groq.com/openai",
    temperature=0.5,
    provider=OpenAIGuardianProvider,
)

metrics = Bias.run(
    MyRetriever,
    guardian=LLamaGuard,
    config=guardian_config,
    statistical_mode=BayesianMode(mc_samples=5000, ci_level=0.95),
    verbose=True,
)

print("Bias Detection Results")
print("=" * 50)

for metric in metrics:
    print(f"\nSession: {metric.session_id} | Assistant: {metric.assistant_id}")
    print("\nBias Rates by Protected Attribute:")

    for rate in metric.attribute_rates:
        status = "⚠ WARNING" if rate.rate > 0.10 else "✅ OK"
        line = f"  [{status}] {rate.protected_attribute}: {rate.rate:.1%}  ({rate.k_biased}/{rate.n_samples})"
        if rate.ci_low is not None:
            line += f"  CI=[{rate.ci_low:.1%}, {rate.ci_high:.1%}]"
        print(line)

Custom Guardian

from fair_forge.core.guardian import Guardian
from fair_forge.schemas.bias import GuardianBias

class MyGuardian(Guardian):
    def __init__(self, config, **kwargs):
        self.config = config

    def is_biased(self, question, answer, attribute, context=None) -> GuardianBias:
        is_biased = self._detect_bias(question, answer, attribute)
        return GuardianBias(
            is_biased=is_biased,
            attribute=attribute.attribute.value,
            certainty=0.9,
        )

metrics = Bias.run(MyRetriever, guardian=MyGuardian, config=my_config)

Next Steps

Statistical Modes

Deep dive into Frequentist vs Bayesian approaches

Context Metric

Evaluate context alignment

Toxicity Metric

Detect toxic content with group profiling

Getting Started

Core Concepts

Metrics

Generators

Runners

Prompt Optimizer

Explainability

Bias

Bias Metric

Overview

Installation

Basic Usage

Parameters

Required Parameters

Optional Parameters

Statistical Modes

Guardian Configuration

GuardianLLMConfig

Available Guardians

LlamaGuard

IBMGranite

Output Schema

BiasMetric

AttributeBiasRate

GuardianInteraction

Understanding Results

Bias Rates

Guardian Interactions

Complete Example

Custom Guardian

Next Steps

Statistical Modes

Context Metric

Toxicity Metric

​Bias Metric

​Overview

​Installation

​Basic Usage

​Parameters

​Required Parameters

​Optional Parameters

​Statistical Modes

​Guardian Configuration

​GuardianLLMConfig

​Available Guardians

​LlamaGuard

​IBMGranite

​Output Schema

​BiasMetric

​AttributeBiasRate

​GuardianInteraction

​Understanding Results

​Bias Rates

​Guardian Interactions

​Complete Example

​Custom Guardian

​Next Steps

Statistical Modes

Context Metric

Toxicity Metric

Bias Metric

Overview

Installation

Basic Usage

Parameters

Required Parameters

Optional Parameters

Statistical Modes

Guardian Configuration

GuardianLLMConfig

Available Guardians

LlamaGuard

IBMGranite

Output Schema

BiasMetric

AttributeBiasRate

GuardianInteraction

Understanding Results

Bias Rates

Guardian Interactions

Complete Example

Custom Guardian

Next Steps