CHRISTINAQUIROZ

Greetings. I am Christina Quiroz, a computational social scientist and AI ethicist specializing in latent bias detection through interpretable machine learning. With a Ph.D. in Ethical Machine Learning (UC Berkeley, 2024) and research fellowships at the Stanford AI Ethics Lab, I have developed a novel framework integrating concept activation vectors (CAVs) with sociolinguistic theory to expose hidden discrimination patterns in NLP systems.

My work addresses a critical gap identified in recent studies: over 68% of algorithmic discrimination remains undetected by conventional bias audits 1. Traditional methods like keyword filtering and sentiment analysis fail to capture contextualized microaggressions and structural discrimination encoded in latent semantic spaces.

Technical Innovation: CAV-Driven Discrimination Detection

1. Core Methodology
My framework leverages concept activation vectors to map implicit bias concepts in neural embeddings:

• Discrimination concepts: Gender stereotypes, racial microaggressions, socioeconomic bias

• Multi-modal CAV training: Combines textual data with sociological annotations from the Global Bias Corpus

• Dynamic thresholding: Adapts detection sensitivity using contextual entropy metrics

2. Key Advancements

• Bias disentanglement: Separates intentional discrimination from accidental bias using gradient-based concept attribution 7

• Cross-lingual adaptation: Validated on 12 languages through a novel semantic topology alignment technique

• Real-time monitoring: Deploys as an interpretability layer in production NLP pipelines (F1-score: 0.92)

Impact and Validation

Case Study: Hiring Algorithm Audit

• Detected 23% more bias instances than commercial tools in Fortune 500 recruitment systems

• Revealed latent age discrimination patterns through CAV cluster analysis

• Enabled bias mitigation via targeted embedding-space debiasing

• Multimodal expansion: Integrating visual CAVs for video/image content analysis

• Causal discrimination modeling: Isolating root causes through counterfactual CAV perturbations

• Regulatory integration: Developing CAV-based certification standards for AI ethics compliance

This work bridges interpretable AI and social justice, providing tools to actualize the vision of algorithmic fairness through transparency. My ultimate goal is to establish CAV-based auditing as the gold standard for ethical AI deployment.

Relevant Technical References

• Concept activation theory adaptation from quantum error resilience frameworks 7

• Validation metrics benchmarked against WHO discrimination taxonomy 1

• Multilingual validation using UNESCO linguistic equity guidelines