Fairness First: The Cornerstone of Responsible AI

The principle of fairness in responsible AI emphasizes the importance of ensuring that AI systems operate without bias and provide equitable treatment to all individuals. Fairness involves defining and measuring metrics that reflect how AI decisions affect different groups, striving to achieve equitable outcomes across these groups. Bias independence seeks to design AI systems that do not inherit biases from training data or unintended biases from their creators.

Biases in AI systems can lead to severe social and legal implications, underscoring the critical need for rigorous testing and validation to ensure fairness. When AI systems exhibit bias, they can perpetuate and even exacerbate existing inequalities, leading to consequences that affect individuals and society.

IBM’s “Watson for Oncology” project exemplifies the pitfalls of deploying AI without thorough validation. In 2013, IBM partnered with The University of Texas MD Anderson Cancer Center to develop an “Oncology Expert Advisor” system with the ambitious goal of curing cancer. However, the project, which cost over $62 million, was ultimately canceled due to unsafe treatment recommendations. Internal documents revealed that IBM’s engineers had trained Watson on a small number of hypothetical cancer patients rather than actual patient data. This resulted in multiple instances of Watson providing incorrect and potentially dangerous treatment advice. For example, Watson once recommended a drug that could worsen severe bleeding in a cancer patient.

Microsoft’s AI chatbot, Tay, highlights the dangers of insufficient testing and monitoring. Launched in 2016, Tay was designed to engage in casual conversations on Twitter. However, within 24 hours, internet trolls manipulated Tay to produce offensive and harmful messages, forcing Microsoft to shut down the chatbot. This incident demonstrated how AI systems could quickly adopt and amplify negative behaviors if not properly safeguarded.

These examples underscore the importance of implementing rigorous testing and validation protocols to ensure the fairness and reliability of AI systems. Without such measures, AI can inadvertently perpetuate biases and cause significant social and legal harm. Ensuring fairness in AI is not just a technical challenge but a moral and ethical imperative that requires continuous oversight and improvement. For instance, Hexaware’s AI-first approach is founded on three core principles: ethics, reliability, and transparency. Check out our eBook to discover how our transformational AI advancements stay true to our core principles in ensuring fairness.

Now, let’s examine how fairness and bias independence can be ensured throughout the AI development process.

Fairness and Bias Independence

Fairness in AI is measured by defining metrics that capture the impact of AI decisions on different groups, aiming to ensure equitable outcomes. Bias independence involves designing AI systems to operate without inheriting biases from the training data or the creators’ unintended biases.

Ensuring Fairness and Bias Independence

Data preparation is a critical step in building AI models. It involves multiple stages to ensure that the data used for training the model is of high quality and properly structured. Here’s a breakdown of each stage:

Data Collection & Analysis

Effective data management is critical for developing robust AI models. It begins with data collection and proceeds through data analysis, ensuring that the gathered data is representative, unbiased, and ready for model training. Here are the key steps:

• Data Collection: This involves gathering raw data from various sources. The goal is to collect diverse datasets representing all potential users or affected groups to avoid biases and ensure the model performs well across different scenarios.
• Data Analysis: Once collected, the data is analyzed to understand its structure, quality, and patterns. This step helps identify any initial issues or biases in the data and understand how the data can be used to train the model effectively.

For more insights, check out this blog on navigating eDiscovery challenges with generative AI to make sense out of data deluge. The blog suggests ways to handle data volume and variety while managing privacy and security and filtering massive amounts of data to identify relevant information.  

Also, this blog holds key insights into how AI can transform data analytics and usher in material benefits for businesses across domains.

Data Cleansing

Ensuring the quality of data is crucial for building accurate AI models. Effective data cleaning involves several key steps:

• Removing Inaccurate Data: Identifying and removing incorrect data entries, such as typos or logical inconsistencies, to prevent the model from learning incorrect patterns.
• Completing Incomplete Data: Handling missing values by either filling them in with appropriate values (imputation) or removing the affected records, depending on the situation and the amount of missing data.
• Eliminating Irrelevant Data: Filter out data that doesn’t contribute to the model’s learning process. This can include redundant columns or features that do not impact the outcome.

Feature Engineering

Feature engineering is a critical process in machine learning that involves creating, transforming, and selecting features from raw data to improve model performance. This section covers the essential steps:

• Creating Features: This step involves creating new input variables (features) from the raw data to help the model learn better. For example, if the raw data includes a date and time, you might create features like “day of the week” or “hour of the day.”
• Transforming Features: Modifying existing features to improve the model’s learning. This can involve normalization (scaling features to a standard range), encoding categorical variables, or creating polynomial features.
• Selecting Features: Identifying and selecting the most relevant features that have the most significant impact on the model’s predictions can help reduce complexity and improve the model’s performance.

Splitting

In machine learning, a dataset is typically divided into three key subsets: the training set, the validation set, and the test set. These subsets play distinct roles in the model development process, ensuring that the model learns effectively, is fine-tuned properly, and is evaluated accurately.

• Training Set: A portion of the dataset used to train the model. The model learns from this data.
• Validation Set: A separate portion tunes the model’s parameters and optimizes hyperparameter. This set helps evaluate the model’s performance and adjust before final testing.
• Test Set: A final portion of the dataset used to evaluate the model’s performance after training. This set assesses how well the model generalizes to unseen data.

Dividing the dataset into these three sets ensures that the model can be trained, validated, and tested effectively. This provides a more accurate assessment of its performance and helps prevent overfitting.

Model Tuning and Training

Bias Mitigation comprises three key steps: Pre-Processing, Model Training, and Post-Processing. These steps are key to ensuring that biases are identified and mitigated at each stage of model development.

Pre-processing Methods

Pre-processing methods involve modifying the training data to minimize or eliminate biases before it is used to train an AI model. Examples of three standard pre-processing techniques are resampling, reweighting, and feature selection or modification.

Resampling

Resampling adjusts the distribution of the training data to address imbalances. To create a more balanced dataset, either oversampling under-represented groups or undersampling over-represented groups can be used.

Example: Imagine you are developing a model to predict loan approvals. Your dataset has a significant imbalance, with far fewer approved loans for a particular minority group than the majority group.

• Oversampling: You duplicate instances of the minority group in the dataset until their representation matches that of the majority group. For example, if the dataset originally had 1,000 majority group instances and 100 minority group instances, you would increase the minority group instances to 1,000 through duplication or synthetic data generation.
• Undersampling: You reduce the number of instances of the majority group to match the minority group. For example, if the dataset had 1,000 majority group instances and 100 minority group instances, you would randomly sample 100 instances from the majority group to balance the dataset.

By resampling, the model is trained on a dataset that equally represents both groups, helping reduce prediction bias.

Reweighting

Reweighting assigns weights to instances in the training data to make some cases more influential than others during model training. This approach compensates for under-representation without changing the size of the dataset.

Example: Consider the same loan approval prediction model with an imbalanced dataset.

• Instead of duplicating or removing instances, you assign higher weights to cases of the minority group and lower weights to the majority group during training. For example, each minority group instance could be given a weight of 10, while each majority group instance could be given a weight of 1.
• When the model trains, it treats each minority group instance as if it were ten instances, thus giving it more influence in the learning process.

Reweighting ensures that the model gives more attention to the minority group without altering the number of instances, helping to balance the influence of different groups in the model’s decisions.

Feature Selection or Modification

Feature Selection or Modification involves altering or selecting features used in model training to reduce the impact of biased data. This method can exclude sensitive attributes that could introduce bias or transform them to prevent inappropriate use.

Example: In the loan approval prediction model, specific sensitive attributes like race or gender could introduce bias.

• Exclusion: You decide to exclude the race and gender attributes from the dataset, preventing the model from considering these factors in its predictions.
• Transformation: Instead of completely excluding sensitive attributes, you transform them. For example, you could replace specific values with more general categories that retain some information without revealing the sensitive attribute directly. Alternatively, you could encode the sensitive attribute to minimize its direct influence (e.g., using one-hot encoding in a way that the model cannot directly infer the bias).

By carefully selecting or modifying features, you ensure the model is not influenced by biased data, leading to fairer and more equitable predictions.

Model Training

This step involves creating a teaching model and parameters to minimize bias while learning from the training data. These methods integrate fairness constraints or objectives directly into the learning algorithm.

• Fairness Constraints: These incorporate fairness directly into the optimization problem that machine learning algorithms solve. For example, they add a constraint to minimize the difference in predictive performance across groups defined by sensitive attributes.
• Adversarial Debiasing: This technique uses adversarial training to create a model that accurately predicts the target variable while an adversary tries to predict the protected attribute (like race or gender) from the model’s predictions. The idea is to make it difficult for the adversary, thereby ensuring the model does not retain discriminatory patterns.
• Modified Loss Functions: Adjust the model’s loss function to penalize unfair outcomes more heavily. This might involve adding terms to the loss function that increase the cost of biased predictions against certain groups.

Post-Processing

This step involves adjusting the model’s outputs to ensure fair results.

• Equalized Odds: This adjusts the decision threshold for different groups. For example, if a model for predicting loan approval shows bias against a particular group, the threshold for approval might be lowered for that group to balance the approval rates across groups.
• Calibration: Ensures that the model’s confidence levels are aligned with reality. For example, if a model predicts that applicants from a particular group are 70% likely to default on a loan, the actual default rate for those indicated should be close to 70%.
• Reject Option-Based Classification: This class introduces a rejection option where decisions can be deferred if the confidence in the decision is too low or if the decision is near the decision boundary. This can be useful in sensitive applications, allowing human reviewers to decide in ambiguous cases.
• Optimization and Calibration: Fine-tuning the model to perform optimally in its deployment environment.
• Model Management: Continuous oversight of the model once it is deployed.
• Monitoring & Feedback: Regularly check the model’s performance and impact and adjust to remain effective and fair.

Tools to Mitigate Bias

AI Fairness 360

IBM released an open-source library to detect and mitigate biases in unsupervised learning algorithms. The library is called AI Fairness 360, and it enables AI programmers to:

• Test biases in models and datasets with a comprehensive set of metrics.
• Mitigate biases with the help of 12 packaged algorithms such as Learning Fair Representations, Reject Option Classification, and Disparate Impact Remover.

However, AI Fairness 360’s bias detection and mitigation algorithms are designed for binary classification problems, so if your problem is more complex, they need to be extended to multiclass and regression problems.

IBM Watson OpenScale

IBM’s Watson OpenScale performs real-time bias checking and mitigation when AI makes its decisions.

Google’s What-If Tool

Using the What-If Tool, you can test performance in hypothetical situations, analyze the importance of different data features, visualize model behavior across multiple models and subsets of input data, and use different ML fairness metrics.

Conclusion

As we advance in developing and integrating AI into various aspects of society, the principle of fairness must remain a cornerstone of responsible AI practices. Ensuring that AI systems are free from bias and treat all individuals equitably is not just a technical challenge but a moral imperative. By committing to rigorous data preparation, thoughtful model training, and continuous oversight, we can build AI systems that uplift rather than marginalize. A case in point is this eBook, which offers ways to harness the potential of a responsible AI framework. The eBook delves into developing and deploying AI solutions that are fair, accountable, transparent, reliable, and secure to foster trust, which is key to leveraging the full potential of AI.

Fairness in AI reflects our societal values and ethics. It demands that we recognize and address the inherent biases in our data and the algorithms we create. This requires a collaborative effort across disciplines, combining the insights of data scientists, ethicists, policymakers, and affected communities. We must remain vigilant, constantly evaluating and improving our methods to ensure that fairness is not a static goal but a dynamic process of continuous refinement and accountability.

Ultimately, the accurate measure of our technological progress will be reflected in the fairness and justice that our AI systems bring to society. We must strive to create AI that advances our capabilities and upholds our values of equality and fairness for all. AI’s potential to transform our world is immense, but this transformation must be guided by a commitment to fairness, ensuring that the benefits of AI are shared equitably and do not reinforce existing disparities.