Current Projects and Upcoming Events

A research-driven initiative to develop a certification system that determines whether automated systems can make ethical, predictable, and accountable decisions. Our framework focuses on increasing transparency and trust in AI through:

• Algorithmic fairness testing 
• Black-box auditing and interpretability 
• Deep learning model diagnostics 
• Ethical behavior modeling in machines 
• Understanding model origins and decision logic
   

FeelAI's audit methodology is grounded in this research programme. Our  paired-CV testing approach draws on established fairness testing  literature including algorithmic fairness, black-box auditing, and  disparate impact analysis.
 

 Interested in collaborating? Reach out to us to join the research initiative

With only 28% of women in research fields (2017), AI systems risk inheriting and amplifying gender bias. This initiative aims to uncover, analyze, and address gender-based disparities in AI decision-making — particularly in hiring automation and predictive scoring systems.

At FeelAI, we're designing research methods to measure gender bias in AI outputs and working toward tangible solutions, including:

• Bias detection algorithms using CV variants 
• Star-rated fairness scoring 
• Educational workshops on AI and gender
   

 If you care about inclusive AI, we’d love to hear your story or welcome you to this project.

FeelAI also applies machine learning and data science to real-world domains such as:

• Health analytics 
• Spatial data insights 
• Ethical business intelligence 
• Fairness in API-based decision systems. 

Our team brings expertise in:

• Machine learning and NLP 
• Modeling, simulation, and optimization 
• Visualization and geospatial analytics

 We're actively developing tools and frameworks that help businesses and governments make socially responsible, data-driven decisions.

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