Amazon AI Fairness and Explainability with Amazon SageMaker Clarify

This article was written by John Patrick Laurel. John Patrick is a Head of Data Science at a European short-stay real estate business group. He boasts a diverse skill set in the realm of data and AI, encompassing Machine Learning Engineering, Data Engineering, and Analytics. Additionally, he serves as a Data Science Mentor at Eskwelabs. Outside of work, he enjoys taking long walks and reading.

Introduction

In the rapidly advancing domain of machine learning, it is essential to prioritize fairness and transparency in model predictions. Amazon SageMaker Clarify integrates these vital elements into the model development and deployment process rather than treating them as secondary concerns. This article explores SageMaker Clarify in-depth, providing a thorough overview of its capabilities and practical uses.

Our journey begins by gaining a broad understanding of SageMaker Clarify and its significance in the everyday activities of machine learning modeling.?We'll examine a practical example employing a creating dataset that simulates loan approval scenarios in the Philippines. This dataset, intentionally structured to reveal specific biases, provides an ideal platform to showcase the effectiveness of SageMaker Clarify in detecting and mitigating fairness concerns in machine learning models.

As we navigate through the complex terrain of developing machine learning models, we'll utilize AWS's Python SDK, adhering closely to the documentation while making necessary adjustments to accommodate our specific dataset. Our attention will be directed towards various crucial subjects, spanning from the prerequisites for utilizing SageMaker Clarify to the training process of an XGBoost model. Subsequently, we'll explore the functionality of SageMaker Clarify in identifying bias within model predictions and elucidating these predictions in a clear and comprehensible manner.

Join us as we set out on this enlightening expedition to become proficient in SageMaker Clarify, equipping ourselves with the understanding and resources to construct machine learning models that are not only powerful but also equitable and comprehensible.

What is SageMaker Clarify?

Amazon SageMaker Clarify represents a potent solution designed to introduce transparency and equity into the domain of machine learning. In an era where AI-driven choices profoundly influence various facets of our existence, SageMaker Clarify emerges as a symbol of responsibility and comprehension. Positioned as an indispensable element within the Amazon SageMaker collection, it guarantees that machine learning models not only operate effectively but also prioritize fairness and interoperability.

Core Functions

1. Bias Detection and Mitigation: SageMaker Clarify tackles a core issue in machine learning: bias. It offers tools for detecting and measuring biases that may be present in your data and models. This functionality is particularly crucial when handling sensitive characteristics such as gender, ethnicity, or age. Through the analysis of these attributes, SageMaker Clarify aids in uncovering potential biases that could influence decision-making, thereby ensuring equitable treatment of all individuals by the models.
2. Model Explainability: Understanding the rationale behind a model's predictions is just as important as the predictions themselves. SageMaker Clarify provides visibility into the reasoning and mechanisms behind model decisions. This transparency holds significant value, especially in contexts where explanations are necessary for compliance or to establish trust with users. It dissects prediction outcomes, offering a transparent view of the factors involved, thereby elucidating the sometimes obscure workings of machine learning algorithms.

Integrating with Your Machine Learning Workflow

SageMaker Clarify effortlessly fits into your current AWS machine-learning setup. Whether you're building from the ground up or working with an existing model, Clarify can seamlessly join your process at different points, spanning from data preparation to post-deployment stages. This adaptability enables ongoing monitoring and enhancement of your models, guaranteeing their fairness and comprehensibility throughout their lifespan.

Why SageMaker Clarify Matters

In the case study, we'll utilize an artificial dataset simulating loan approvals in the Philippines. This dataset is intentionally crafted to highlight biases, making it an excellent platform for showcasing the abilities of SageMaker Clarify. Through this demonstration, we'll directly observe how Clarify identifies biases within the dataset and the machine learning model. This hands-on experience not only emphasizes the significance of fairness in AI but also demonstrates the seamless integration of SageMaker Clarify into routine machine learning endeavors.

To sum up, SageMaker Clarify represents more than just a tool; it embodies a dedication to ethical AI practices. By guaranteeing fairness and explainability, it enables developers and businesses to develop machine learning models that excel not only in performance but also in fairness and transparency. This fosters trust and reliability in the decisions driven by AI.

Prerequisites and Data

Importing Libraries

• pandas and numpy for data manipulation and numerical operations.
• os and boto3 for operating system and AWS SDK operations.
• datetime for handling date and time data.

SageMaker-specific libraries like session and get_execution_role for managing SageMaker sessions and roles.

Initializing Configurations

Establishing the SageMaker session and specifying the role is essential for seamlessly integrating our local environment with AWS services. This initial setup enables smooth interaction with SageMaker and other AWS services throughout our project.

Downloading the Data

We will use a pre-prepared dataset that portrays loan applications in the Philippines. This dataset is purposefully designed to highlight potential biases and will be the cornerstone of our analysis with SageMaker Clarify. You can access and download this dataset using the provided link.

Preprocessing

Preprocessing entails standardizing numerical features and encoding categorical ones, readying the dataset for utilization in machine learning models.

Scaling the numerical features:

Splitting the dataset:

Encoding categorical columns:

Data Definition

Gaining a deep comprehension of our dataset is vital for pinpointing and remedying potential biases. It includes:

• Monthly Income: A numerical feature representing the applicant’s income, a key factor in loan decisions.
• Credit Score: Indicates the creditworthiness of an applicant, crucial for loan approvals.
• Employment Years: Represents the duration of employment, potentially influencing loan decisions.
• Debt-to-Income Ratio and Other Obligations: Assess financial stability and repayment capacity.
• Gender: A sensitive attribute that could be a basis for gender bias in loan decisions.
• Ethnicity: Reflects the diverse cultural backgrounds in the Philippines, a potential ground for ethnic bias.
• Age: Ranges from 18 to 70, and could influence decisions, leading to age discrimination.

The loan approval status serves as the target variable, subject to bias analysis through SageMaker Clarify. By exploring these features, we gain insight into how biases may arise in a model, allowing proactive measures to foster a fairer machine learning solution.

Model Training

In this section, we'll walk through the steps of training an XGBoost model using our prepared dataset.

Putting Data into S3

Prior to training, it's necessary to upload our dataset to Amazon S3, AWS’s scalable storage service. This step guarantees that our data is readily available for the SageMaker training job.

Training an XGBoost Model

XGBoost is a widely used and efficient open-source implementation of gradient-boosted trees, celebrated for its performance and speed. In this phase, we'll set up and initiate the training of an XGBoost model on our dataset.

Create a SageMaker Model

After completing the training phase, the next task involves creating a SageMaker model. This model will serve the purpose of making predictions and will also be subjected to fairness and explainability analysis using SageMaker Clarify.

In this section, we've accomplished uploading our data to S3, training an XGBoost model, and establishing a SageMaker model. These actions set the foundation for the following phases, during which we'll use SageMaker Clarify to identify biases and provide explanations for the predictions made by our model.

Amazon SageMaker Clarify

Detecting Bias

Identifying and mitigating bias is essential for responsible AI practices. In this section, we'll examine how Amazon SageMaker Clarify assists in uncovering and addressing biases in machine learning models.

Understanding Bias in Machine Learning

In machine learning, bias denotes the unfair and discriminatory treatment of specific groups due to characteristics such as gender or ethnicity. This inequitable treatment typically originates from the training data or the model's data processing methods. Biases can have profound effects on individuals and communities, resulting in skewed and unjust results. Hence, it's imperative to identify and address these biases to uphold fairness and equity in AI-driven decisions.

SageMaker Clarify for Bias Detection

SageMaker Clarify offers tools for identifying biases both before and after training, employing a range of metrics. Pre-training bias originates from the training data, whereas post-training bias may emerge during the model's learning phase.

Initializing Clarify

To begin, we set up a SageMakerClarifyProcessor, tasked with calculating bias metrics and providing model explanations:

DataConfig: Setting Up Data for Bias Analysis

The DataConfig provides SageMaker Clarify with information regarding the data utilized for bias analysis:

This configuration defines the S3 paths for input data and output reports, the target label, column headers, and the dataset type.

ModelConfig and ModelPredictedLabelConfig: Configuring the Model

ModelConfig defines the trained model details:

ModelPredictedLabelConfig sets up how SageMaker Clarify interprets the model’s predictions:

BiasConfig: Specifying Bias Parameters

BiasConfig is used to specify parameters for bias detection:

In our example, we center our attention on gender as the sensitive attribute and age as the subgroup for assessing bias.

Pre-training vs Post-training Bias

In our scenario, pre-training bias would pertain to any inherent biases present in the dataset, such as an uneven representation of specific genders or ethnicities. Post-training bias would involve biases that the model might adopt as it learns from this data, potentially amplifying existing biases or introducing new ones.

Running Bias Report Processing

Finally, we run the bias analysis using SageMaker Clarify:

This procedure thoroughly investigates both pre-training and post-training biases, providing insights into areas where the model might exhibit unfair biases. By addressing these biases, we can strive for fairer and more equitable AI systems.

Viewing the Bias Report

Accessing the Report

Once the SageMaker Clarify analysis is complete, you can review the bias report results. If you're running the demo locally, you can access the report by navigating to the output generated by the following command:

Subsequently, you can retrieve the report from this location and examine it. If you're conducting the demo via SageMaker Studio, you can directly access the results in the "Experiments" tab.

Report Overview

The comprehensive bias report generated by Amazon SageMaker Clarify is structured into different sections:

• Analysis Configuration: This portion provides an overview of the setup employed for the bias analysis, including details such as the outcome label column, the facet (the attribute examined for bias analysis), and an optional group variable.
• High-Level Model Performance: This section of the report presents metrics indicating the model’s performance, such as accuracy, true positive rate (recall), and false positive rate (precision).
• Pre-training Bias Metrics: These metrics measure imbalances in the representation of facet values (e.g., gender) within the training data. Various metrics, including Conditional Demographic Disparity in Labels (CDDL), Class Imbalance (CI), and Difference in Proportions of Labels (DPL), provide insights into the balance or imbalance of the training data concerning the facet.
• Post-training Bias Metrics: This part measures imbalances in model predictions across various inputs. Metrics such as Accuracy Difference (AD), Conditional Demographic Disparity in Predicted Labels (CDDPL), and Disparate Impact (DI) aid in determining whether the model's predictions are equitable across different groups defined by the facet (e.g., gender).

Each of these sections offers valuable insights into different facets of bias within the machine learning model, enabling a comprehensive understanding of potential biases and how they manifest in both the data and the model's predictions.

You can check the whole bias report in this link.

Explaining Predictions with Kernel SHAP

In the domain of machine learning, particularly in applications with significant social impacts such as loan approvals, understanding the 'why' behind a model's decision is just as crucial as the decision itself. Amazon SageMaker Clarify employs Kernel SHAP (SHapley Additive exPlanations) to clarify the contribution of each input feature to the final decision. This method, rooted in cooperative game theory, offers a way to interpret complex model predictions by assigning each feature an important value for a particular prediction.

To execute the run_explainability API call, SageMaker Clarify necessitates configurations akin to those employed for bias detection, encompassing DataConfig and ModelConfig. Furthermore, SHAPConfig is introduced explicitly for the Kernel SHAP algorithm.

In our demonstration, we configure SHAPConfig with the following parameters:

• Baseline: The Kernel SHAP algorithm requires a baseline or background dataset for reference. This baseline can be either a predefined set of data or automatically calculated using methods like K-means. In our case, we choose the latter approach, using the mean of the training dataset as our baseline. Choosing an appropriate baseline is critical as it establishes the foundation for SHAP value calculation.
• Num_samples: This parameter determines the quantity of synthetic data samples utilized for computing SHAP values. The selection of this number can strike a balance between computational efficiency and the fidelity of the explanations.
• Agg_method: This denotes the technique employed for aggregating global SHAP values. We use 'mean_abs', which calculates the mean of the absolute SHAP values across all instances, offering an assessment of the overall impact of each feature.
• Save_local_shap_values: When set to True, this option saves the local SHAP values in the output location, enabling detailed examination of feature contributions for individual predictions.

Explainability Report Configuration

Running Explainability Report Processing

Executing the explainability analysis involves running the run_explainability method, which typically requires around 10-15 minutes:

Viewing the Explainability Report

The SageMaker Clarify-generated Explainability Report provides a comprehensive insight into how various features impacted the model's predictions. The report comprises:

1. Model Explanations: This section provides SHAP explanations for individual labels, detailing the contribution of each of the 8 features in the model.
2. Visualization of SHAP Values: Within the report, there's a chart where each point signifies an individual instance. The x-axis illustrates the SHAP value for a particular instance and feature, while the red-blue color scale reflects the feature value, with red indicating higher values and blue indicating lower values.

This comprehensive breakdown encourages a deeper comprehension of the model's decision-making process, emphasizing the factors most influential in predictions. Such transparency is essential not just for regulatory adherence but also for building trust in machine learning systems among users and stakeholders.

You can check the whole explainability report in this link.

Wrapping Up

Embracing Fairness and Explainability in Machine Learning

As we wrap up our exploration of Amazon SageMaker Clarify, it’s clear that this tool is crucial in promoting fairness and transparency in machine learning models. Throughout our journey, from configuring our environment to training an XGBoost model and employing SageMaker Clarify, we've witnessed firsthand the significance and indispensability of these tools in modern machine-learning practices.

Key Takeaways

1. Detecting Bias: We discovered how SageMaker Clarify assists in identifying biases both before and after model training. Through the analysis of our loan approval dataset, Clarify highlighted biases that might result in unfair treatment of individuals based on sensitive attributes such as gender, ethnicity, or age.
2. Explaining Predictions: Through Kernel SHAP, SageMaker Clarify offered valuable insights into the contribution of each feature to the model's predictions. This depth of explainability isn't merely a technical necessity but a significant advancement towards ethical AI, ensuring that stakeholders comprehend and trust the decisions made by the models.
3. Practical Application: Using an artificial dataset that resembles real-life situations showed how these ideas work in real life. This example helped to explain how models that seem fair could actually be unfair if we don't check and fix them carefully.

Moving Forward

As machine learning keeps getting better and becomes a bigger part of different fields, tools like SageMaker Clarify become really important. They help us make models that not only work great but also match our ethical rules and what we believe in as a society. We're still working to make AI responsible, and SageMaker Clarify is a big help in that mission.

Final Thoughts

We urge machine learning and data science experts to use SageMaker Clarify in their work. This way, we can all work together to make AI systems fairer and more transparent. Remember, the aim isn't just to make smart machines but to make sure they make fair, clear, and responsible decisions.

* This newsletter was sourced from this Tutorials Dojo article.