Causal Analysis & Bias Mitigation
Causal Inference & Ethical AI Research Platform
01 / Project Overview
An academic and technical toolkit focusing on identifying, measuring, and correcting algorithmic biases in Large Language Models (LLMs). Utilizing causal graphs and counterfactual testing pipelines, the platform allows developers to debug text generation systems to enforce demographic parity and causal fairness constraints.
02 / The Challenge & Problem
Real-World Problem Statement
AI models trained on web-scale datasets inherit severe human biases related to gender, race, and background. Existing bias metrics are superficial, looking only at statistical correlations and failing to address underlying causal links, which leads to biased outputs when models face unseen prompts.
03 / The Engineering Solution
Implementation & Architectural Approach
Implemented a framework using structural causal models (SCMs) to trace decision-making pathways within neural network outputs. Developed counterfactual generation algorithms that alter demographic attributes of prompts to audit and dynamically debias model weights.
04 / Technical Architecture Flow
Counterfactual Generator
Transforms standard prompts into matching pairs by substituting demographic/identity keywords for testing.
DoWhy & PyTorch Inference
Measures the direct causal impact of demographic switches on subsequent token probabilities.
Vector Subspace Projection
Projects intermediate model representations onto a clean subspace, filtering out bias direction vectors.
05 / Key Project Features
Counterfactual Audit Engine
Automates testing of models using thousands of parallel demographic variations to evaluate bias.
Parity Analytics Dashboards
Renders metrics like Demographic Parity Difference and Equalized Odds in clear graphics.
Active Inference Filter
Intercepts and dynamically adjusts attention weights during text generation to mitigate output bias.
06 / Engineering Challenges & Mitigations
Calculating causal effects across large transformer models requires massive GPU compute cycles.
Optimized token caching mechanisms and designed sub-model vector extraction routines targeting layer representations.
Heavy debiasing can degrade the overall language coherence and capability of the model.
Developed fine-tuned projections that adjust debiasing strength dynamically based on semantic context checks.
07 / Technical & Personal Learnings
Obtained deep conceptual insights into neural model vector spaces and ethical AI alignment paradigms.
Mastered structural causal modeling (SCMs), causal discovery graphs, and causal effect estimation using DoWhy.