Tracking Bias and Accuracy Across Cohorts

Overview

GenAI applications span a wide variety of tasks, from summarization and information extraction to Q&A systems and autonomous agents. Each task type presents unique challenges for accuracy measurement and bias detection. This cookbook provides a framework for selecting the right Fiddler evaluators (both out-of-the-box and custom LLM-as-a-Judge) to ensure your models deliver reliable, fair, and high-quality outputs across different use cases.

Understanding Bias in GenAI

Bias in GenAI primarily refers to unfair treatment of people by an LLM based on protected characteristics such as:

Nationality
Gender
Age
Socioeconomic status
Sexual orientation
Race/ethnicity

This is the most critical concern when monitoring for bias in production systems. A model exhibits bias when it performs differently for users or content associated with different demographic groups. Beyond these social considerations, models can also exhibit inconsistent behavior across different input types—such as performing differently on technical documents vs. narrative content. While less critical than bias related to protected characteristics, tracking these patterns can still improve overall model quality.

Measuring Bias with Fiddler

A great way to detect bias is to compare accuracy metrics across protected cohorts. For example:

Does your Q&A system achieve lower Answer Relevance scores for questions about certain cultural topics?
Does your summarization model produce less faithful summaries for content written by authors from specific demographic backgrounds?
Does your chatbot provide less helpful responses when discussing topics related to certain protected characteristics?

Fiddler provides two complementary approaches for bias detection:

LLM-as-a-Judge for individual assessment: Custom evaluators that assess each request for potential bias indicators
Segment analysis for comparative measurement: Compare accuracy metrics across protected cohorts to identify disparities

Both approaches are detailed in the Deep Dive sections below.

Out-of-the-Box Evaluators

These pre-built metrics provide immediate, generalized assessments of LLM performance. They offer a quick, low-effort way to establish a baseline for quality and compliance. Learn more: Enrichments

Custom LLM-as-a-Judge Evaluators

These evaluators allow you to inject specific business rules and task-specific quality standards directly into the evaluation process. By using a structured prompt template to define the criteria, the LLM acts as an automated, scalable subject matter expert, turning nuanced judgments into objective, trackable data. Learn more: Prompt Specs Quick Start

Recommended Evaluators by GenAI Task

Summarization

Use Case Description: Summarization models condense long-form content (documents, articles, meeting transcripts, research papers) into concise summaries while preserving key information and maintaining factual accuracy. Accuracy Evaluators:

Evaluator	What does it measure?	What value does it provide?
Faithfulness (optimized for summarization)	Assesses whether the summary accurately represents the source material without introducing hallucinations or distortions.	Accuracy Baseline: Ensures the summary doesn’t fabricate or misrepresent information from the original text.
Conciseness	Evaluates whether the summary is appropriately brief while retaining essential information.	Efficiency: Confirms the model is truly summarizing, not just excerpting or paraphrasing at length.

Bias Detection Strategy:

Tag summaries with metadata about source content (author demographics, topic categories, document type)
Compare Faithfulness and Conciseness scores across segments
You can also monitor token count differences between summaries of similar-length documents from different segments
Example: Do summaries of technical papers by women researchers average 20% fewer tokens than those by men, suggesting less thorough coverage?

Information Extraction

Use Case Description: Information extraction models parse unstructured or semi-structured text (invoices, receipts, contracts, emails, forms) to identify and extract specific data fields like names, dates, amounts, addresses, or custom entities relevant to your business. Accuracy Evaluators:

Evaluator	What does it measure?	What value does it provide?
Per-Field Accuracy LLM-as-a-Judge	Evaluates extraction accuracy for each specific field (e.g., “name,” “date,” “amount”).	Granular Quality Control: Pinpoints which fields the model extracts reliably vs. which require improvement.
Overall Accuracy LLM-as-a-Judge	Assesses whether all required information was extracted correctly in aggregate.	Completeness Check: Ensures no critical data is missed during extraction.

Bias Detection Strategy:

Tag extractions with metadata about the source (document format, language, content domain, demographic context)
Compare per-field accuracy rates across segments
Example: Does the model correctly extract names from resumes with non-Western names at the same rate as Western names?

Question & Answer (RAG Systems)

Use Case Description: RAG (Retrieval-Augmented Generation) systems answer user questions by first retrieving relevant information from a knowledge base, then generating responses grounded in that context. These systems power customer support chatbots, internal knowledge assistants, and documentation search tools. Note: Evaluation strategies differ for closed-ended questions (verifiable facts) vs. open-ended questions (requiring interpretation). Accuracy Evaluators:

Evaluator	What does it measure?	What value does it provide?
Answer Relevance	Assesses whether the response accurately addresses the user’s question.	Intent Matching: Especially critical for open-ended questions—confirms the response addresses the spirit of the question, not just surface-level keywords.
RAG Faithfulness	Evaluates whether the answer is grounded in the retrieved context without hallucination.	Factual Accuracy: For closed-ended questions, ensures the model said the “right thing” based on retrieved documents. For open-ended questions, confirms claims are supported by context.
Context Relevance	Measures whether the retrieved documents are actually relevant to the question.	Retrieval Quality: Identifies when the retrieval subsystem is returning poor-quality or off-topic context.
Conciseness	Evaluates whether answers are appropriately brief.	User Experience: Prevents over-explaining simple questions or burying key information in verbose responses.

Closed-Ended vs. Open-Ended Questions:

Closed-Ended: “What is the capital of France?” → Focus on RAG Faithfulness (Did it say “Paris”?)
Open-Ended: “How can I improve team productivity?” → Focus on Answer Relevance (Does it address the intent?) and RAG Faithfulness (Are suggestions grounded in retrieved best practices?)

Bias Detection Strategy:

Tag questions with metadata about topic category, user demographics (if available), or question complexity
Compare Answer Relevance and RAG Faithfulness scores across segments
Monitor response token count and sentiment across different question types
Example: Do questions about minority health issues receive lower Answer Relevance scores or shorter responses than general health questions?

Chatbots

Use Case Description: Conversational AI agents engage in multi-turn dialogues with users for customer support, HR assistance, IT helpdesk, sales qualification, or general-purpose assistance. Unlike simple Q&A, chatbots maintain conversation context and handle follow-up questions. Accuracy Evaluators:

Evaluator	What does it measure?	What value does it provide?
Answer Relevance	Assesses whether responses stay on-topic throughout the conversation.	Conversational Coherence: Prevents the chatbot from drifting off-topic or ignoring user intent.
RAG Faithfulness	Ensures responses are grounded in retrieved knowledge (if using RAG).	Trust Building: Users trust chatbots that cite or reference real information rather than making things up.
Sentiment Analysis	Tracks the emotional tone of chatbot responses.	Tone Management: Ensures the bot maintains an appropriate, helpful tone even when users are frustrated.

Note: Apply the same Context Relevance and Conciseness evaluators recommended for Q&A systems if your chatbot uses retrieval. Bias Detection Strategy:

Tag conversations with user demographics, conversation topic, or user satisfaction ratings
Compare Answer Relevance, RAG Faithfulness, and Sentiment scores across segments
Monitor average response length and response time across different user groups
Example: Do users from certain demographic groups receive responses with consistently more negative sentiment or fewer tokens?

Classification

Use Case Description: Classification models categorize text inputs into predefined classes. Common applications include sentiment analysis (positive/negative/neutral), topic categorization (finance/sports/politics), intent detection (purchase/support/information), content moderation, and spam filtering. Accuracy Measurement Strategy: For GenAI classification tasks, traditional ML performance metrics such as precision, recall, and F1-score are highly effective. Since the task can be treated the same way as a predictive ML task, these metrics provide quantitative benchmarks for application performance. Bias Detection Strategy:

Tag classifications with metadata about input characteristics (writing style, topic, demographic context)
Compare precision, recall, and F1 scores across segments in Fiddler
Example: Does sentiment classification achieve 85% accuracy for product reviews written by younger users but only 70% for older users?

Autonomous Agents

Use Case Description: Autonomous agents are AI systems that independently plan, execute multi-step workflows, call external tools or APIs, and make decisions to accomplish complex goals. Examples include AI assistants that can book travel, research assistants that gather and synthesize information, or automation agents that handle business processes. Accuracy Evaluators:

Evaluator	What does it measure?	What value does it provide?
Tool Call Accuracy LLM-as-a-Judge	Evaluates whether the agent selected and invoked the correct tool with proper parameters.	Execution Reliability: Prevents the agent from calling the wrong APIs or passing invalid arguments.
Context Relevance	Assesses whether the agent retrieved or used appropriate information before taking action.	Decision Quality: Ensures the agent’s actions are informed by relevant context.

Bias Detection Strategy:

Tag agent interactions with task type, user demographics, or workflow complexity
Compare Context Relevance and Tool Call Accuracy across segments
Monitor number of tool calls and task completion time across different user groups
Example: Does the agent require more steps to complete identical tasks for certain user populations?

Code Generation

Use Case Description: Code generation models translate natural language descriptions into working code. Applications range from developer productivity tools (generating boilerplate, writing tests, explaining code) to low-code/no-code platforms where non-developers can create automation scripts or data processing pipelines. Accuracy Evaluators:

Evaluator	What does it measure?	What value does it provide?
Custom LLM-as-a-Judge	Evaluates code quality, correctness, security, and adherence to coding standards.	Code Review Automation: Acts as a first-pass reviewer to catch obvious errors, security vulnerabilities, or style violations.

Bias Detection Strategy:

Tag code generation requests with programming language, user experience level, or problem domain
Compare correctness and security scores across segments
Monitor generated code token count for similar requests across different segments
Example: Does the model generate less secure code for web development tasks compared to data science tasks?

Content Generation

Use Case Description: Content generation models create original written content tailored to specific audiences and purposes. Applications include marketing copy, blog posts, social media content, email campaigns, product descriptions, internal communications, and creative writing assistance. Accuracy Evaluators:

Evaluator	What does it measure?	What value does it provide?
Answer Relevance	Assesses whether the content addresses the original prompt or brief.	Instruction Adherence: Ensures the model delivers what was requested, not something tangentially related.
Custom LLM-as-a-Judge: Audience Alignment	Evaluates whether the content is appropriate for the target audience (tone, complexity, terminology).	Targeted Communication: Confirms the content speaks to the intended reader (e.g., executives vs. technical users vs. general public).
Coherence	Assesses logical flow and narrative quality.	Readability: Ensures content is easy to follow and professionally written.
Sentiment Analysis	Tracks the emotional tone of generated content.	Brand Safety: Prevents overly negative or inappropriate messaging from reaching audiences.

Bias Detection Strategy:

Tag content with target audience demographics, topic category, or content type
Compare Answer Relevance, Coherence, and audience alignment scores across segments
Monitor content token count and sentiment scores for similar prompts targeting different audiences
Example: Does content generated for female audiences consistently receive lower Coherence scores or different sentiment patterns than content for male audiences?

Deep Dive: Detecting Bias with LLM-as-a-Judge

LLM-as-a-Judge evaluators can be designed to assess individual requests for potential bias indicators. This approach is most effective when you have specific bias criteria you want to detect in each prompt or response.

When to Use LLM-as-a-Judge for Bias Detection

Use this approach when you need to:

Flag potentially biased content in individual responses (e.g., stereotypical language, exclusionary phrasing)
Assess prompt safety for bias-related concerns (e.g., requests that might elicit biased responses)
Evaluate fairness of individual outputs (e.g., does a job description use inclusive language?)
Score bias indicators that can later be aggregated across segments

Limitations of LLM-as-a-Judge for Bias Detection

Important: LLM-as-a-Judge evaluators process one request at a time and cannot compare performance across demographic groups. They can identify potentially problematic content, but they cannot tell you if your model performs differently for different user populations. For that, you need segment analysis (see next section).

Deep Dive: Detecting Bias Through Segment Analysis

Segment analysis is the most powerful method for detecting performance disparities across protected cohorts. By comparing accuracy metrics between demographic groups, you can identify systemic bias in your GenAI systems.

How Segment Analysis Works

Since LLM-as-a-Judge evaluators process requests individually and cannot compare across prompts, Fiddler enables bias detection through a four-step process:

Step 1: Apply Accuracy Evaluators to All Requests

Use the appropriate out-of-the-box and custom LLM-as-a-Judge evaluators for your use case (as outlined above) to score every request for accuracy and quality. Examples:

Summarization: Faithfulness, Conciseness
Q&A: Answer Relevance, RAG Faithfulness
Content Generation: Answer Relevance, Coherence, Audience Alignment
Information Extraction: Per-Field Accuracy, Overall Accuracy

Step 2: Tag Requests with Relevant Metadata

Enrich your data with tags that enable meaningful comparisons. The specific tags depend on your use case: User-level tags:

Demographics (age group, gender, location, language)
User segment (enterprise vs. SMB, premium vs. free tier)
Experience level (new user vs. power user)

Content-level tags:

Topic category (e.g., “women’s health” vs. “men’s health” vs. “general health”)
Content source (author demographics, publication type)
Language or dialect
Domain (technical vs. general, formal vs. informal)

Interaction-level tags:

Channel (web, mobile, API)
Time of day
Session length

Step 3: Compare Metrics Across Segments in Fiddler

Use Fiddler’s dashboards to calculate and compare: Accuracy metrics:

Average Answer Relevance, Faithfulness, Coherence scores by segment
Per-field extraction accuracy rates by segment
Classification precision/recall/F1 by segment

Behavioral metrics:

Token count distributions (are responses equally detailed?)
Response time (do certain queries take longer?)
Retrieval quality (Context Relevance by segment)
Sentiment patterns (are responses equally positive/neutral?)

Step 4: Investigate Disparities

When you identify statistically significant differences: Quantify the gap:

What’s the magnitude of the disparity? (e.g., 15% difference in Faithfulness scores)
How many users are affected?
Is the gap consistent over time or growing?

Root cause analysis:

Is the bias in the training data?
Is it in the prompt or system instructions?
Is it in the retrieval system (for RAG applications)?
Is it in the evaluation criteria themselves?

Remediation:

Adjust prompts to be more explicit about fairness requirements
Augment training data to balance representation
Retrain or fine-tune models
Filter or reweight retrieval results
Update evaluation criteria to catch edge cases

Example: Detecting Gender Bias in a Healthcare Q&A System

Scenario: You want to ensure your healthcare Q&A system provides equally helpful answers regardless of the topic’s demographic context. Implementation: Step 1: Apply Accuracy Evaluators

Enable Answer Relevance evaluator for all questions
Enable RAG Faithfulness evaluator for all questions

Step 2: Tag Questions by Topic Create a tagging system for health topics:

topic: "women's health" (menstruation, pregnancy, menopause, etc.)
topic: "men's health" (prostate health, testosterone, etc.)
topic: "general health" (nutrition, exercise, sleep, etc.)
topic: "pediatric care" (child development, vaccinations, etc.)

Step 3: Compare Metrics in Fiddler Create segments for each topic category and compare. Example:

Segment	Avg Answer Relevance	Avg RAG Faithfulness	Avg Response Length (tokens)	Sample Size
Women’s health	0.78	0.82	145	1,247
Men’s health	0.91	0.89	203	1,156
General health	0.88	0.87	195	3,892
Pediatric care	0.85	0.86	188	891

Step 4: Investigate & Remediate Finding: Women’s health questions receive:

14% lower Answer Relevance scores (0.78 vs. 0.91)
8% lower RAG Faithfulness scores (0.82 vs. 0.89)
29% shorter responses (145 vs. 203 tokens)

Root Cause Investigation:

Examine retrieved documents for women’s health queries → Discovery: Knowledge base has 40% fewer articles on women’s health topics
Review sample low-scoring responses → Discovery: Model often retrieves general health content instead of women’s-health-specific sources
Check Context Relevance scores → Discovery: Retrieved documents are less relevant for women’s health (0.72 vs. 0.85 for men’s health)

Remediation Actions:

Immediate: Adjust retrieval parameters to prioritize topic-specific matches
Short-term: Expand knowledge base with high-quality women’s health content
Medium-term: Fine-tune retrieval model on balanced health topic dataset
Ongoing: Monitor disparity metrics weekly to ensure improvement

Example Result After Remediation: Numbers below are illustrative.

Segment	Avg Answer Relevance	Avg RAG Faithfulness	Avg Response Length (tokens)
Women’s health	0.86 ↑	0.87 ↑	192 ↑
Men’s health	0.91	0.89	203

Gap reduced from 14% to 5% for Answer Relevance.

Advanced Segment Analysis Techniques

1. Intersectional Analysis You can also examine intersections in protected groups rather than just single demographic attributes:

How does the system perform for questions about “women’s health” in Spanish vs. English?
Do younger users get different quality responses than older users for the same topics?

2. Temporal Monitoring Track metrics over time to catch drift:

Did a model update introduce new disparities?
Are gaps widening or narrowing?
Do disparities appear at certain times of day or during high-traffic periods?

3. Cohort Comparison Compare multiple segments simultaneously:

Segment Analysis: Answer Relevance by Topic
- Women's health: 0.78
- Men's health: 0.91
- LGBTQ+ health: 0.74 ← Additional disparity discovered
- General health: 0.88

4. Statistical Significance Testing Ensure observed differences aren’t due to random variation:

Use adequate sample sizes for each segment
Calculate confidence intervals
Apply appropriate statistical tests (t-tests, ANOVA, etc.)

How These Evaluators Can Help

1. Establishing Accuracy Baselines

By combining out-of-the-box metrics (like Faithfulness, Answer Relevance) with custom LLM-as-a-Judge evaluators, you create a comprehensive accuracy profile for your GenAI application. This lets you:

Set quality thresholds (e.g., “95% of summaries must achieve Faithfulness > 0.85”)
Compare model versions objectively
Track accuracy degradation over time

2. Detecting Bias at Scale

Manual review of thousands of responses for bias is impractical. By combining LLM-as-a-Judge flagging with segment analysis, you can:

Flag problematic individual outputs for immediate remediation
Quantify fairness gaps across demographic groups with statistical rigor
Identify systemic disparities that would be invisible in aggregate metrics
Build audit trails demonstrating proactive bias monitoring for compliance

3. Continuous Monitoring & Improvement

By tracking these metrics in Fiddler over time, you can:

Identify systemic drift in accuracy or bias after model updates
Catch when prompt changes inadvertently introduce new biases
Build feedback loops that route low-quality outputs for retraining data
Measure the effectiveness of bias mitigation efforts

4. Compliance & Governance

For regulated industries, bias detection isn’t just about fairness—it’s about compliance. Automated bias tracking provides an audit trail showing you actively monitor and mitigate unfair treatment, which can be critical for regulatory reviews and demonstrating responsible AI practices.

​Overview

​Understanding Bias in GenAI

​Measuring Bias with Fiddler

​Out-of-the-Box Evaluators

​Custom LLM-as-a-Judge Evaluators

​Recommended Evaluators by GenAI Task

​Summarization

​Information Extraction

​Question & Answer (RAG Systems)

​Chatbots

​Classification

​Autonomous Agents

​Code Generation

​Content Generation

​Deep Dive: Detecting Bias with LLM-as-a-Judge

​When to Use LLM-as-a-Judge for Bias Detection

​Limitations of LLM-as-a-Judge for Bias Detection

​Deep Dive: Detecting Bias Through Segment Analysis

​How Segment Analysis Works

​Step 1: Apply Accuracy Evaluators to All Requests

​Step 2: Tag Requests with Relevant Metadata

​Step 3: Compare Metrics Across Segments in Fiddler

​Step 4: Investigate Disparities

​Example: Detecting Gender Bias in a Healthcare Q&A System

​Advanced Segment Analysis Techniques

​How These Evaluators Can Help

​1. Establishing Accuracy Baselines

​2. Detecting Bias at Scale

​3. Continuous Monitoring & Improvement

​4. Compliance & Governance

​Related Resources

Overview

Understanding Bias in GenAI

Measuring Bias with Fiddler

Out-of-the-Box Evaluators

Custom LLM-as-a-Judge Evaluators

Recommended Evaluators by GenAI Task

Summarization

Information Extraction

Question & Answer (RAG Systems)

Chatbots

Classification

Autonomous Agents

Code Generation

Content Generation

Deep Dive: Detecting Bias with LLM-as-a-Judge

When to Use LLM-as-a-Judge for Bias Detection

Limitations of LLM-as-a-Judge for Bias Detection

Deep Dive: Detecting Bias Through Segment Analysis

How Segment Analysis Works

Step 1: Apply Accuracy Evaluators to All Requests

Step 2: Tag Requests with Relevant Metadata

Step 3: Compare Metrics Across Segments in Fiddler

Step 4: Investigate Disparities

Example: Detecting Gender Bias in a Healthcare Q&A System

Advanced Segment Analysis Techniques

How These Evaluators Can Help

1. Establishing Accuracy Baselines

2. Detecting Bias at Scale

3. Continuous Monitoring & Improvement

4. Compliance & Governance

Related Resources