LLM Bias Evaluation via Contrastive Resume Decoding
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Solution Overview
Problem
Existing audit methods for evaluating bias in large language models (LLMs) used in algorithmic hiring lack the ability to establish causal relationships between sensitive attributes and outcomes, due to confounding variables.
Innovation Solution
The development of systems, methods, and computer-accessible media that utilize a contrastive input decoding approach to evaluate bias in LLMs, specifically by modifying resumes with sensitive attributes and analyzing the LLM's classification outputs to determine statistically significant bias.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If observational methods are used to audit bias in LLM hiring tools, then audit reports can be generated, but causal relationships between sensitive attributes and outcomes cannot be established due to confounders
Solution Approach 1:
The system performs preliminary actions by creating synthetic resume datasets with controlled sensitive attributes before evaluation. It pre-modifies resumes to include specific sensitive attribute combinations (e.g., gender, race, maternity status) in a controlled manner, allowing the LLM's responses to be analyzed for causal effects rather than just observational correlations.
Solution Approach 2:
The system introduces an intermediary evaluation framework that mediates between the LLM hiring tool and the audit process. This framework includes a bias detection module that acts as an intermediary to isolate and measure the effect of sensitive attributes on hiring decisions, filtering out confounding variables through controlled synthetic data generation and analysis.
2Productivity
If LLMs are used for automated hiring decisions, then efficiency gains are achieved, but bias and discrimination concerns arise
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring LLM responses for biased language and decision patterns. It provides feedback loops that detect harmful factors (bias, discrimination) in real-time and can trigger alerts or adjustments, allowing the system to maintain efficiency while addressing bias concerns through active monitoring and response.
Solution Approach 2:
The system changes parameters by modifying the evaluation criteria and data generation parameters to detect and mitigate bias. It adjusts the synthetic data generation parameters to create diverse resume scenarios and modifies the analysis parameters to identify subtle bias patterns, enabling the system to maintain hiring efficiency while detecting and addressing discrimination issues.
3Reliability
If comprehensive bias evaluation across multiple sensitive attributes is conducted, then fairness is improved, but evaluation complexity increases
Solution Approach 1:
The system applies segmentation by dividing the comprehensive bias evaluation into separate modules and dimensions. It segments the evaluation process into individual attribute analyses (gender, race, maternity status, etc.) and uses separate synthetic data generation modules for each attribute combination, making the overall complex evaluation system more manageable and systematic.
Solution Approach 2:
The system achieves universality by creating a multi-functional evaluation framework that can assess multiple sensitive attributes using a unified approach. The synthetic data generation mechanism and bias detection algorithms are designed to work across different attributes and combinations, reducing the need for separate evaluation systems for each attribute and thereby managing complexity while maintaining comprehensive fairness evaluation.
Data Source
AI summary
Exemplary systems, methods and computer-accessible medium according to the exemplary embodiments of the present disclosure are provided for determining bias in at least one large language model (LLMs). Thus, exemplary systems, methods, and computer-accessible medium can receive a plurality of baseline resumes, create or generate a plurality of flagged resumes from the plurality of baseline resumes, create or generate a resume corpus from the plurality of baseline resumes and the plurality of flagged resumes, input the resume corpus into the LLM, receive an LLM classification output for the resume corpus, and measure a LLM bias based on the classification output.