Bias Monitoring: Keep AI Solutions Aligned to Original Intent
Minimizing bias is one of AI’s most challenging technical problems. Failure to reduce bias has legal and ethical implications and impacts AI performance. Bias monitoring is recognized as a critical component to reliable and effective AI- but not all bias monitoring is equal.
As AI models identify and reproduce patterns, developers must invest time and effort to ensure that pattern identification and reproduction do not overlook minority patterns and outliers, leading to high rates of false positives and negatives and resultant compliance issues. Artificial Intelligence systems may inherit biases from the data used to train or amplify them during production and in their outcomes.
While auditing for bias is universally seen as a solution to this challenge, there are some key differences in audit methodologies that impact outcomes. In this blog post we’ll walk through the items most impactful to audit outcomes and show how these principles are woven into the ITACA solution developed by Eticas.ai.
A Comprehensive Approach to Bias Auditing
Our methodology evaluates AI systems across all lifecycle stages, comparing privileged and underprivileged groups to ensure an unbiased assessment of model behavior. These groups are typically categorized based on protected attributes such as age, gender, ethnicity, disability status, marital status, and veteran status.
By leveraging a broad set of metrics, our framework offers a comprehensive perspective on fairness, enabling detailed reporting in production environments. The methodology encompasses three primary phases:
- Labeled Data Assessment: Evaluating the dataset used for model training.
- Production Monitoring: Analyzing results from the operational AI model.
- Impact Analysis: Examining final decisions, including human intervention when applicable.
Each phase incorporates key analytical techniques to identify and mitigate bias. Below, we elaborate on each of the key monitoring processes used in our methodology:
- Demographic Benchmarking Monitoring: Perform in-depth analysis of population distribution.
- Model Fairness Monitoring: Ensure equality and detect equity issues in decision-making.
- Features Distribution Evaluation: Analyse correlations, causality, and variable importance to detect proxy attributes and labeled bias.
- Performance Analysis: Metrics to assess model performance, accuracy, and recall.
- Model Drift Monitoring: Detect and measure changes in data distributions and model behavior over time.
Demographic Benchmarking Monitoring
Demographic benchmarking establishes the expected representation of different groups in AI model outputs. For example, if evaluating gender representation, a 50/50 distribution between male and female groups may be expected. However, more complex intersections, such as race and gender, may require deeper analysis.
A thorough demographic benchmark evaluates distribution across multiple stages, including training and production. This ensures that any disparities in positive outcomes or overall representation are systematically identified and corrected. Given the complexity of demographic benchmarking, ongoing refinement and stakeholder collaboration are essential.
That’s why our onboarding process integrates robust benchmarking into the audit, ensuring precision and fairness.
| Metric Used | True Label | Description |
|---|---|---|
|
Demographic Disparity (DD) |
No |
Measures the percentage of underprivileged group samples. |
|
DD Positive Decision |
No |
Assesses the percentage of underprivileged group samples receiving favorable outcomes. |
Model Fairness Monitoring
Ensuring fairness in AI models requires evaluating outcomes through both equality (treating all groups the same) and equity (acknowledging differences and addressing disparities). Relying solely on selection rates can be misleading: if one group systematically meets different requirements, it may indicate underlying structural biases.
A particular selection rate might indicate favouritism toward one group over another. Additionally, differences in selection rates could arise because one group does not meet specific requirements, potentially due to structural biases that require additional resources for success.
By expanding our analysis beyond simple selection rates, we can uncover and address the root causes of these disparities, ensuring a more comprehensive understanding and mitigation of bias.
For this reason, the equality and equity metrics developed at Eticas have raised the fairness evaluation to a new level. With this understanding, we can ensure that the model’s behavior is equally distributed across different groups. Additionally, in post-marketing monitoring, the vision of possible equity problems allows for improvement in the process, allowing the integration of positive discrimination techniques.
| Metric Used | True Label | Description |
|---|---|---|
|
Disparate Impact (DI) |
No |
Measures the ratio of selection rates. |
|
Statistical Parity Difference (SPD) |
No |
Evaluates differences in selection rates. |
|
Calibration |
Yes |
Assesses how well predicted outcomes align with true outcomes across groups. |
|
Equal Odds (EO) |
Yes |
Examines whether false and true outcomes are equally distributed among groups. |
|
Equality vs. Equity Analysis |
No |
Determines if disparities arise due to systemic model bias or external group characteristics. |
Features Distribution Evaluation
Evaluating the distribution of features ensures that the selected inputs for inference are free from discrimination. This analysis verifies that features are correctly chosen and that any transformations applied do not introduce bias. For this reason, features must be assessed after they have undergone the transformation process.
Bias can arise from two primary sources:
- Feature Relationships or proxy bias: When non-sensitive features are strongly correlated with protected attributes, indirectly encoding discrimination.
- Information Gain: When certain attributes disproportionately influence the model’s decision-making process.
To mitigate these risks, we use three primary techniques, with a special focus on identifying and mitigating proxy bias:
- Correlation Analysis: Identifies strong associations between features.
- Protected Attribute Inference: Determines whether protected attributes can be inferred from other variables.
- Causality Analysis: Examines causal relationships between features to detect indirect pathways for bias.
Similarly, the evaluation of information gain can be performed using the following methods:
- Correlation with Output: Analyse the correlation between the protected attribute and the model’s output to ensure that protected characteristics do not unduly affect decisions.
- Model Comparison: Develop two models:
- One that includes the protected attribute.
- Another that excludes it. The performance difference between these models indicates the extent of information gain derived from the protected attribute, highlighting potential biases.
3. Feature Importance Assessment: Evaluate the importance of each feature in the model to determine whether protected attributes or their proxies are significant in the decision-making process.
By rigorously applying these techniques and emphasizing the detection of proxy bias, we help ensure that our AI systems remain fair and unbiased, thereby delivering more equitable and trustworthy outcomes.
| Metric Used | True Label | Description |
|---|---|---|
|
Informative |
No |
Identifies proxy features acting as protected attributes. |
|
Inconsistency |
No |
Measures the correlation between protected attributes and outputs. |
Performance Analysis
Performance analysis compares the model’s outputs with labels to ensure fair behavior and the best results. This analysis reveals any disparities affecting fairness and overall performance by disaggregating outcomes for different groups.
A comprehensive evaluation considers prediction accuracy and the potential for bias. If a model is considered fair but falls short in critical areas, it fails to provide value. Our approach ensures the model is free from discrimination and optimized for peak performance across all population patterns.
Evaluation bias arises when a model is assessed without examining its performance across diverse groups. Without this level of detail, hidden biases might persist, compromising equity and efficiency.
It’s important to note that performance analysis relies on actual labels, so its application is limited to labelled datasets. Despite this constraint, robust performance analysis is pivotal in guiding the development of AI systems that deliver outstanding, fair, and reliable results.
| Metric Used | True Label | Description |
|---|---|---|
|
Accuracy |
Yes |
The proportion of correct predictions. |
|
F1 Score |
Yes |
The harmonic mean of precision and recall. |
|
Precision |
Yes |
The ratio of true positives to all predicted positives. |
|
Recall |
Yes |
The ratio of true positives to all actual positives. |
Model Drift Monitoring
Drift monitoring is a critical step to ensure that a model continues to perform in the environment for which it was designed.
By continuously comparing training and production datasets, drift analysis helps detect shifts that could compromise performance and fairness. This monitoring can be conducted at three key levels:
- Dataset Level: Evaluate whether the multivariate distribution and overall data patterns observed during training remain in the production environment.
- Feature Level: Assess if the individual distributions of each feature remain consistent with those seen during training.
- Output Level: Analyse whether the output distribution in production is similar to the one observed during training or if it has deviated. Drift monitoring ensures that AI systems remain aligned with their intended purpose and adapt to real-world changes while maintaining fairness.
| Metric Used | True Label | Description |
|---|---|---|
|
Drift |
No |
Measures shifts in data or model performance over time. |
Bias monitoring is critical for AI systems to function ethically, legally, and effectively.
A structured, multi-phase audit approach, such as the one employed by ITACA, ensures fairness, transparency, and accountability. By integrating demographic benchmarking, fairness evaluation, feature assessment, performance analysis, and drift monitoring, we provide a comprehensive solution that safeguards AI integrity across its lifecycle.
Ensuring unbiased AI is not a one-time effort but an ongoing commitment. Organizations that prioritize continuous bias monitoring will not only comply with regulations but also build AI systems that are fair, trustworthy, and beneficial for all users.
Want to learn more about how to check and improve your AI systems? Contact us.