From Data to Decision: Pre-, In-, and Post-Processing Techniques for Fairer AI in BFSI
The Bias Conundrum in AI-Driven Financial Decisions
Imagine building a skyscraper: every stage, from laying the foundation to choosing the finishing materials, impacts the structure’s stability and appearance. Similarly, in AI systems, bias can enter at any stage, influencing how decisions are made. In BFSI, where AI powers loan approvals, fraud detection, and insurance pricing, even small biases can lead to significant ethical, financial, and reputational consequences.
This article explores pre-, in-, and post-processing techniques to mitigate bias, ensuring fairness across the AI lifecycle. Each stage represents a key step in constructing an equitable AI system, much like erecting a skyscraper that’s stable, inclusive, and sustainable.
The AI Lifecycle and Points of Bias
Think of an architect designing a building. The initial blueprint, construction process, and final touches all influence the outcome. A poorly thought-out blueprint leads to structural flaws, while ignoring inclusivity during construction could make the building inaccessible. Similarly, in AI, biases can creep in during data collection (blueprint), model training (construction), or decision-making (final touches).
Key Points:
1. Pre-Processing Bias: When the data used to train AI models is skewed or incomplete, it’s like starting with a flawed blueprint. For example, the underrepresentation of certain groups in loan approval data can cause the AI to unfairly favor other groups.
2. In-Processing Bias: During model training, prioritizing efficiency over fairness is akin to using cheaper materials during construction — quick but structurally unsound.
3. Post-Processing Bias: After the AI is deployed, uncorrected disparities in outcomes are like uneven finishing touches that alienate certain users — e.g., a grand staircase but no ramp for wheelchair access.
Pre-Processing Techniques — Fixing the Blueprint
When designing a building, architects ensure the blueprint includes all the necessary details, like measurements, load capacity, and accessibility features. In AI, pre-processing involves preparing the data to ensure it represents all groups fairly and avoids perpetuating existing inequities.
Some Techniques to consider:
1. Rebalancing Datasets: Just as an architect adjusts the design to balance weight distribution, AI practitioners use oversampling (duplicating data from underrepresented groups) or synthetic data generation to address imbalances in the dataset.
For example, in credit risk modeling, ensuring that lower-income applicants are well-represented to avoid biased predictions.
2. Anonymization: Similar to creating a design that doesn’t reveal private information about tenants, anonymizing sensitive attributes (e.g., race, gender) during data preparation prevents AI models from discriminating.
For Example removing demographic details from loan applications while retaining financial indicators.
3. Feature Engineering: Architects might replace ambiguous details with clearer ones to improve functionality. Likewise, replacing sensitive features with neutral proxies ensures predictive accuracy without bias.
Take for instance, using neighborhood financial data instead of race to predict creditworthiness.
A well-balanced dataset ensures the AI has a strong foundation, leading to more equitable decisions.
In-Processing Techniques — Strengthening the Construction
During construction, builders use safety standards and quality checks to ensure the structure holds up under stress. In AI, in-processing techniques embed fairness directly into the model training process, ensuring robustness against biases.
Some Techniques to consider:
1. Fairness Constraints: Builders might reinforce specific weak points in a structure; similarly, AI models are designed to optimize both fairness and accuracy.
For Example — A fraud detection system that maintains consistent error rates across all demographic groups, ensuring no group is disproportionately flagged.
2. Reweighted Loss Functions: Just as contractors prioritize areas with higher risk, AI practitioners give greater weight to underrepresented groups during training to balance outcomes.
Consider this as an example — Assigning higher importance to historically disadvantaged groups in credit scoring models.
3. Adversarial Debiasing: This involves running a secondary check during training, much like having an inspector independently verify construction quality. Like using an adversarial network to ensure the model doesn’t infer sensitive attributes, even indirectly.
Embedding fairness constraints during construction ensures the system is strong and equitable, capable of enduring real-world challenges.
Post-Processing Techniques — Perfecting the Finish
After a building is constructed, final touches like painting, landscaping, or adding ramps ensure inclusivity and appeal. In AI, post-processing addresses biases that remain after the model is trained, focusing on outcomes.
Some Techniques at this stage:
1. Outcome Calibration: Similar to repainting a wall to ensure uniform color, adjusting predictions ensures fairness across groups.
Take as an Example of recalibrating credit approval probabilities so that acceptance rates are equitable across income levels.
2. Threshold Tuning: Adjusting thresholds is like setting personalized entry points for different groups, ensuring everyone can access the building safely. Take the example of setting different loan approval cutoffs for different regions based on historical inequities.
3. Auditing and Transparency Tools: These act as the building’s inspection report, offering transparency and assurance of safety. Using explainable AI (XAI) dashboards to demonstrate fairness in decision-making to regulators and stakeholders.
Post-processing ensures the final product meets the needs of all stakeholders, fostering trust and inclusivity.
Just as constructing an inclusive, resilient skyscraper requires careful planning, building fair AI systems demands attention at every stage. Pre--, in-, and post-processing techniques form the blueprint, construction, and finishing needed to mitigate bias and foster equitable outcomes.
