Decision Science

Choices shape outcomes—for users, organizations, and society. We architect decision-making frameworks that prioritize transparency, reduce bias, and align with long-term human and ecological well-being.

Methodology:

• Bias Ecosystem Analysis: Identify how cultural, algorithmic, and institutional biases distort choices (e.g., gender assumptions in AI recruitment tools).
• Ethical Choice Scaffolding: Guide users with defaults that benefit their goals (e.g., sustainable purchase prompts vs. impulse-upselling).
• Future-Proofing: Model how decisions ripple across time and stakeholders (e.g., privacy trade-offs in data collection).

Impact:

Trustworthy systems where users feel understood, not targeted. Reduced ethical debt, e.g., avoiding redesigns to fix exploitative features. Making sure users are actually presented with all the data they need to make a informed decision, weather you are doing a fundraiser or selling a SaaS product.