How to Mitigate AI Biases Using Data Contracts | Data Governance to Improve Data Quality
“Garbage in, garbage out.” This old adage perfectly captures the importance of data quality in the age of AI and machine learning (ML). When AI models are trained on low-quality or biased data, they produce inaccurate predictions, flawed recommendations, and — in the case of LLMs (Large Language Models) — hallucinations. To mitigate these issues, organizations are turning to data contracts and data governance as foundational pillars of AI system development.
This article explores how data contracts and data governance improve data quality, reduce bias, and ensure fair, transparent, and accurate AI predictions. We’ll walk through key concepts, examples, and practical steps to build contract-based AI pipelines and safeguard AI systems against bias.
📘 What Are Data Contracts?
A data contract is a formal, API-based agreement between data producers (those creating or collecting data) and data consumers (those analyzing, transforming, or using data for AI/ML models). Data contracts define the following elements:
Schema: Specifies the structure, format, and field types of data.
Semantics: Describes what the data represents (e.g., "age" refers to a person's age in years).
Distribution: Details the data range, frequency, and distribution.
Enforcement Policies: Sets validation rules and quality checks to ensure incoming data adheres to the defined structure.
Example of a Data Contract
An e-commerce company's data contract for a "Customer Order" dataset might define:
Schema: Fields like Order_ID (int), Product_ID (int), Quantity (int), Price (float).
Semantics: Quantity refers to the number of items purchased, and Price refers to the price per item.
Distribution: Quantity is always a positive integer.
Enforcement: Any deviation from the schema (like missing values) triggers a notification to the data producer.
By treating data like an API with an agreed-upon "contract," data producers and consumers have a single surface for collaboration. This approach makes data pipelines more predictable, testable, and less error-prone.
💡 Why Low-Quality Data Leads to Biased AI and Hallucinations
Low-quality data is one of the root causes of biased AI models and hallucinations. When ML models are trained on inaccurate, incomplete, or biased datasets, the consequences can be severe. Here’s what happens:
AI Bias: If certain demographic groups are underrepresented or misrepresented in the training data, AI models learn these biases, leading to unfair predictions.
Example: A hiring algorithm may exhibit gender bias if historical hiring data reflects gender inequalities.Hallucinations in LLMs: LLMs trained on incorrect, incomplete, or noisy data can generate responses that sound plausible but are factually wrong.
Example: ChatGPT claiming that a non-existent "Greenland Prime Minister" made a certain statement.Failed Predictions: Poor-quality data can skew prediction models, leading to incorrect business decisions.
Example: A recommendation engine that uses low-quality purchase history data may recommend irrelevant products, reducing customer satisfaction.
🛠️ How Data Contracts Mitigate AI Bias and Improve Data Quality
Data contracts provide a contractual obligation for data producers and consumers to define, measure, and enforce data quality standards. This ensures that incoming data meets specific quality criteria before it is used in AI/ML pipelines.
1️⃣ Enforce Data Quality at the Source
With data contracts, any out-of-spec data (like missing fields, incorrect formats, or out-of-range values) is automatically flagged. This prevents bad data from ever reaching the ML model.
How It Works:
Data contracts enforce schema validation at the point of data entry.
Data validation rules ensure required fields, data types, and formats are present.
If errors are detected, an alert or rejection is sent back to the data producer for correction.
Example:
If an ML model expects the "Date of Birth" to be in YYYY-MM-DD format, a contract ensures no other format (like MM/DD/YYYY) is accepted. This prevents downstream errors in age calculations.
2️⃣ Avoid Complex and Noisy Data
Complex data with unnecessary attributes increases the risk of model overfitting, hallucinations, and poor generalization. Data contracts ensure that only relevant data attributes are shared.
How It Works:
Contracts define which fields are mandatory and which are optional.
Data that does not match the schema (e.g., excess attributes) is automatically dropped.
This reduces the risk of "feature overload" in ML models.
Example:
In a retail dataset, unnecessary fields like "Store Opening Hours" may be irrelevant for a customer segmentation model. Data contracts specify only relevant attributes (like "Customer Age, Gender, Region, Purchase History") to prevent feature bloat.
3️⃣ Monitor AI Bias in Real Time
Data contracts allow teams to track the distribution of data over time and detect shifts in the data's statistical distribution. This ensures that demographic fairness is preserved.
How It Works:
Data contracts define the expected distribution of values.
If a data shift occurs (e.g., customer age skewing toward one demographic), alerts are triggered to investigate the root cause.
Example:
If a hiring model expects 50% of candidates to be male and 50% female, a shift to 80% male and 20% female signals potential bias.
4️⃣ Create Contract Pipelines for AI/ML Systems
To operationalize data contracts, you’ll need a contract pipeline — an end-to-end system that validates, logs, and tracks incoming data.
How to Create a Contract Pipeline:
Define the Contract: Specify schema, semantics, and validation rules.
Implement Contract Validation: Use tools like Great Expectations to check for compliance.
Enforce Alerts: Use WhyLabs or DataDog to create alerts for violations.
Feedback Loop: Return bad data to producers for correction.
Example:
If you receive customer data from multiple e-commerce sites, the contract pipeline checks each dataset for schema compliance, missing values, and distribution drift before merging it into the training pipeline.
🔧 Practical Tools for Implementing Data Contracts
ToolDescriptionWhyLabs LangKitReal-time monitoring and alerting for data quality.Great ExpectationsOpen-source tool for validating and documenting data contracts.DataHubData discovery and metadata platform for shared data visibility.dbt (data build tool)Data transformation tool that supports data contracts.
🚀 Key Takeaways
ProblemSolution Using Data ContractsBias in AIMonitor demographic distribution and set fairness constraints.HallucinationsEnsure high-quality training data and prevent garbage data.Failed ModelsCreate schema contracts to ensure data quality from day one.Data ComplexityDefine clear rules for required and optional fields.
🌐 Real-World Use Case: AI-Powered Hiring Platform
Problem: A hiring platform was accused of gender bias in job applicant recommendations.
Solution Using Data Contracts:
Data Contract for Candidate Profile
Fields: Name, Age, Gender, Experience, Education Level.
Schema Check: No null values in "Gender" or "Education".
Distribution Check: Ensure 50/50 gender distribution of candidates.
Contract Pipeline
Enforce field requirements using Great Expectations.
Send alerts if any gender-skewed data is detected.
Ongoing Monitoring
Use WhyLabs LangKit to track data drift, demographic shifts, and signs of algorithmic bias.
📚 Final Thoughts
Data contracts are transforming how we build, monitor, and govern AI systems. By establishing a formal agreement on data quality, schema, and semantics, data producers and consumers work collaboratively. This reduces AI hallucinations, prevents bias, and ensures predictable, accurate models.
Want to implement data contracts in your pipeline? Start by defining simple rules for data format and schema, then use tools like WhyLabs, Great Expectations, and DataHub to automate enforcement.
By focusing on data quality from the ground up, you’ll ensure your AI models stay fair, responsible, and effective. 🚀
Call-to-Action: Want to learn more about Data Contracts and AI Bias? Start by exploring tools like WhyLabs and Great Expectations to create your first data contract pipeline.