Gender Bias Evaluations in Job Descriptions and Role Representation

Objective : This problem set aimed to design evaluations for gender biases in LLM outputs, particularly focusing on professions historically associated with men and women.

Introduction

Summary of my explorations in algorithmic fairness, bias detection, and ethical considerations within machine learning systems, completed as part of the coursework for AI, Decision-Making, and Society at MIT CSAIL. These problem focus on applying theoretical fairness frameworks, developing practical evaluations, and implementing mitigation strategies to address societal and ethical challenges posed by AI.

Methodology

Developed lists of professions traditionally dominated by each gender, ensuring representation of leadership, technical, and nurturing roles.
Created prompts to elicit model responses that could reveal biases in role descriptions and inspirational role models for each profession.
Analyzed outputs for differences in word choice, leadership attributes, and value associations.

Findings

Male-Associated Roles : Responses emphasized leadership, technical expertise, and innovation, often citing figures like Steve Jobs or Elon Musk.
Female-Associated Roles : Responses emphasized care, support, and dedication, referencing figures like Florence Nightingale.

The model displayed a bias towards reinforcing traditional gender stereotypes, underscoring the need for targeted fairness evaluations.

Impact

This evaluation framework provides a systematic approach to uncovering and quantifying gender biases in AI systems. Such work is critical for ensuring equitable representation across all professional domains.

Section	Details
Objective	Design evaluations to detect gender biases in job descriptions and role models generated by LLMs.
Methodology	- Created prompts for professions historically associated with men and women. - Analyzed LLM responses for leadership traits, stereotypes, and gender-specific references.
Findings	- Male-associated jobs: Highlighted leadership and technical attributes (e.g., Steve Jobs). - Female-associated jobs: Emphasized nurturing and supportive roles (e.g., Florence Nightingale).
Impact	Provided a systematic framework for identifying and addressing gender biases in AI systems.

Table : Gender Bias Evaluations in Job Descriptions.

Conclusion

Through this and other problems like these, I applied fairness frameworks, bias detection methods, and privacy-preserving strategies to evaluate and address ethical challenges in AI systems. These works demonstrate my ability to design rigorous evaluations, identify systemic biases, and implement solutions that align with societal values. Such methodologies are essential for building AI systems that are fair, responsible, and impactful.