Anti-bias training tool for Capital Allocators
An experimental training platform for early-stage capital allocators that improves venture evaluation at the pre-seed and seed stages, reduces bias in pitch decision-making, and supports more accurate capital allocation to increase investor returns.
This project addresses a persistent market inefficiency in early-stage investing. At the pre-seed/seed stages, investors often lack sufficient performance data and rely on gut instinct, pattern recognition, and heuristics. Behavioral economics shows that these conditions heighten affinity bias and other cognitive shortcuts, leading to distorted decision-making and missed investment opportunities. Despite strong evidence that female-led startups generate higher revenue efficiency and competitive returns with less capital, they receive under 2% of venture funding.
At the same time, AI tools are increasingly embedded in startup evaluation and risk assessment. When trained on biased historical data, these systems risk reinforcing exclusion rather than improving decision quality, thereby undermining market transparency and integrity. This creates an opportunity to apply rigorous, evidence-based research to redesign how early-stage investment decisions are supported and to develop AI-enabled tools that strengthen financial literacy, improve signal quality, and enable better capital allocation.
By Rudy Ofori and Yash Sonwaney, Parsons ELab
Project Description
Problem
Female founders receive < 2% of VC funding despite generating higher revenue per dollar invested. Research shows investors ask men promotional questions (growth, potential) and women preventative questions (risks, obstacles)—creating systematic disadvantage unrelated to business quality. (Dr Dana Kanze, We Ask Men to Win and Women Not to Lose: Closing the Gender Gap in Startup Funding)
Target users
Capital allocators seeking to improve their returns on investment (ROI) while they reduce unconscious bias in their evaluation practices.
Core solution
A within-subjects research design where investors become their own control group. The platform, Brofolio analyzes submitted questions for promotional vs. preventative framing, maps investment decisions and founder perception ratings, then delivers personalized “BroFolio Score” reports combining humor with actionable coaching to make abstract bias concrete and undeniable.
How it Works
- Investors select from a library of video pitches without decks from founders at the pre-seed/seed stage, some of which are anonymized.
- Watches two video pitchs by founders
- Condition A (Anonymized): No founder photos, 5min talking head video using an avatar and voice modulator, Gender-neutral language, sanitized bios
- Condition B (Traditional): 5min “talking head” video, live-action on-camera founder pitching at seed/pre-seed stage
- Investor shares their responses to a questionnaire and submits reactions and questions via text input into the AI
- Behind-the-scenes AI analysis (using trained AI model API)
- Each question is analyzed for: Promotional focus (growth, opportunities, strengths, vision) and Preventative focus (risks, concerns, obstacles)
- Bias indicators (disproportionate scrutiny, personal attribute vs. business questions)
- Investor completes a comprehensive survey measuring:
- Investment decisions: Likelihood (1-7), Yes/No/Unsure, check size, portfolio allocation
- Founder perceptions: Confidence, knowledge, resilience, leadership, data presentation, coachability, Q&A effectiveness
- Risk assessments: Market, execution, team, technology, funding risks
- Qualitative responses: Strongest/weakest aspects, additional info needed, founder-opportunity fit, desired co-founder attributes, 3-word founder description
- BroFolio Bias Report is presented with:
- Overall and per-question bias score
- Semi-mean humor opening (attention-grabbing roast)
- Warm guidance (constructive coaching)
- Visual charts showing promotional vs. preventative ratio
- Question breakdown with classifications
- Actionable recommendations
Designed with behavioral economists and neuroscientists to create self-awareness andan open mindset for improvement through subtle humor and empathy.
Stage
Iteration 5: developing the tool on a server to collect and build a library of seed/pre-seed pitches
Tools used
Animaze and Zoom for pitch obfuscation, Vibe-coded entirely using Claude Code.
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