PARSONS E-LAB
(01/10)
Hidden Alpha: Reframing Bias in Early-Stage Investment Decisions
Reframing AI for Equity
AN ANTI-BIAS TRAINING TOOL FOR CAPITAL ALLOCATORS
anti-bias Training
A Systems Thinking Approach to Inclusive Startup Ecosystem Design
Hidden Alpha: Reframing Bias in Early-Stage Investment Decisions
Reframing Capital: AI, Bias, and Decision Infrastructure in Early-Stage Investing
This project is rooted in over two years of extensive research accomplished by Professor Rhea Alexander and the Parsons Entrepreneurial Lab (E-lab).
Prof. Rhea Alexander
Head of E-Lab
Yash Sonwaney
Administrative Lead
Rudy Ofori
Technical Lead
Meet
The Team
(02/10)
PARSONS E-LAB
2 .5x
1%
98%
78
Opportunity Loss
(05/10)
The Problem
PARSONS E-LAB
When Melinda Gates launched Pivotal Ventures, focusing on women-led businesses, the portfolio significantly outperformed traditional VC funds in similar sectors. It turns out, female founders generate 78 cents per dollar invested vs. 31 cents for male founders—that's 2.5x better capital efficiency. They also burn 15% less capital on average, which means better economics and dealflow. However, female-founded startups currently receive under 2% of all investment.
This creates an untapped market segment of 98% that investors can capitalize on to improve their returns if they can refine their analysis and overcome their biases.
We aim to create an AI enabled open-source web application that enables capital allocators (generally early-career VCs, angel investors, and post-secondary MBA program students) improve portfolio performance by increasing bias awareness when evaluating startup pitches, at the earliest funding stages.
Our Solution
(06/10)
PARSONS E-LAB
WHAT
Hidden Alpha: Reframing Bias in Early-Stage Investment Decisions
See signal. Reduce noise. Capture missed returns.
The open-source, AI-enabled platform would be accessible online. Users login and select from a library of pre-recorded, five-minute seed-stage pitch videos from early-stage founders. Some recordings feature blind (mediated) pitches, where founders present through masked avatars with voice modulation, while others are presented natively.
After viewing a pitch, investors complete a short questionnaire and then enter an open AI-driven evaluation space, where they can “dialogue” with the AI. The system analyzes their questions and responses for potential bias or decision-making blind spots, generates a scorecard, and offers feedback. Investors can then repeat the process with additional pitches from the library.
The overarching goal is to help investors recognize and correct their own biases in order to improve capital allocation decisions, diversify their portfolios, and ultimately achieve stronger returns. This is not about hiding information—it is about reordering how information is presented so that:
promising opportunities are not overlooked
their fund doesn’t miss out
increase returns to reach alpha sooner!
(06/10)
PARSONS E-LAB
HOW
Reframing Capital: A Transparent Capital Initiative
Their blind auditions increased the proportion of women in orchestras by 25-46%, with the greatest impact occurring in the final rounds of auditions. For instance, the New York Philharmonic went from less than 5% women in 1970 to 45% by 2020
They implemented "Blind CV" screening for graduate recruitment and used AI to redact identifying information, resulting in 40% increase in ethnic minority candidates progressing to assessment centers
They removed names, photos, and university information from initial job applications in 2017, increasing diversity in shortlisted candidates by 30%. More candidates from non-traditional backgrounds advanced to interviews.
US Orchestras
Success
Stories (Precedents)
HSBC
BBC
(08/10)
PARSONS E-LAB
Goals: Better Questions. Better Bets.
Addressing : Gender bias as a pricing error
Early testing ways to achieve goals
APPROACH
PHASE 1
Fall 2025
Building platform on a server
then measure impact
PHASE 2
Spring 2026
Definition & Experimentation
Platform development & mass testing
(WHERE WE ARE CURRENTLY)
PHASE 2
Building platform on a server
then measure impact
PARSONS E-LAB
Core Platform Roadmap
(08/10)
Engagement & Interaction Design
Assessment System
AI Integration - Core Functionality
Founder persona simulation
Bias detection algorithms
Response analysis
1
2
3
AI Model responds with improvements and suggestions on reframing language to be less biased
FRONT-END
BACK-END
How It Works
Founder pitches are recorded and stored in the platform cloud network
Brofolio analysis responses, generates scores and suggestions on reframing and change of mindset through API
Investor watches anonymized and non-anonymized startup pitches
Naturally react, ask clarifying questions and judge founders
Answer perception survey about investment size, founder perception
1
2
3
4
5
6
Video Walkthrough
How it Works
Designed with behavioral economists and neuroscientists to create self-awareness and an 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 Codeless than
Archive
Parsons E-LAB 2025
(10/10)
Phase 1
Early testing ways to achieve goals
APPROACH
INSTRUCTIONS for Recording Founders' Pitches on local computer for Elab Research Project Library
(Version of this for when we have a server to be updated)
Instructions:
1 .Open the Elab Zoom account (Critical) on the computer that has the anonymized program (Dell) only (make sure no other laptop is on the Zoom call to create an echo or noise)
2. Make sure that when you open a Zoom session, the screen name is “Parsons Elab” or “Research Project” and not a person’s name for their video screen name and no pronouns!!
NOTE:
Before each session, do a test run, of EVERY STEP, before each session, to make sure the system and protocol is working and we can capture the live recordings!!
FIRST RECORDING (with Avatar)
Previous Iterations
Zapier + LLM + ElevenLabs (Audio Only)
Using Zapier, we created a simple workflow wherein a file would be uploaded in the Drive folder, sent to ChatGPT/Gemini/Claude for anonymization processing and then would be vocalized using ElevenLabs before saving the output in the Drive folder once again
(07/10)
Prototype 1
PARSONS E-LAB
For prototype 2, we created a minimal workable prototype using Zoom Avatars and Voicemod to neutralize the visual identity and voice of the user.
(08/10)
Previous Iterations
Prototype 2
PARSONS E-LAB
For prototype 3, we added Animaze, an avatar animation platform, and imported avatars created in Viverse. Then combined the Animaze avatars, for visual identity masking, and Voicemod for voice modification with Zoom as the foundational application.
Previous Iterations
Prototype 3
(10/10)
Demo
Video conferencing platform for pitch delivery
Voice modulation software to mask accent and gendered vocal characteristics
Desktop application providing reactive avatars with upper and lower body tracking
Zoom
Voicemod
Animaze
Prototype 4
PARSONS E-LAB
Prototype 4
Claude AI Interface
SECOND RECORDING (Native)
Workshop (Proof of Concept)
(09/10)
In addition to creating minimal viable prototypes (MVPs), we also organized a workshop where we invited VCs and a founder to participate in our research experiment.
PARSONS E-LAB
We used a claude generated AI model to simulate the AI platform we envision integrating into our platform and took a survey afterwards for feedback.
Brofolio AI
(08/10)
PARSONS E-LAB
Link to AI Model: https://brofolio-two.vercel.app/
General
Investment Decision
Founder Assessment
Risk Assessment
Open-ended Reflection
Sample Questions
Defining
Questions for Workshop Participants
(08/10)
PARSONS E-LAB
(10/10)
Demo
1. Initialize Zoom
2. Launch Avatar (Animaze)
3. Configure Voice (Voicemod)
4. Connect to Zoom
Quick Setup Guide for recording pitches with/without obfuscation
PARSONS E-LAB
Workshop Setup
(09/10)
PARSONS E-LAB
we set up two rooms for the workshop. One for the experimental pitch with the AI bias detecting System and another as a control.
SYSTEM DIAGRAM
The the diagram below describes the version of the system we are currently working on, a library of recordings with standard questionnaires and an open AI to prompt with questions and feedback. It show the step where founders do anti-bias training, recording of anonymized and non-anonymized pitch recordings.
It also shows the step where the venture capitalists respond to questionnaires, ask questions and the trained AI responds with improvements and suggestions to be less biased. It also enlightens them on how they might gain more alpha if they stayed open to the project or took it to the next level and brought in the founder, how they get scored and how they come back for more training to improve their score. It shows how an office (VC Firm) or MBA program in an institution might have this system or portal set up and their goal to improve alpha and diversity of portfolio. It also shows our back end capturing data on learning and how this might translate into bigger returns for the investor or allocator.
Our Solution
(06/10)
PARSONS E-LAB
We address a persistent market inefficiency in seed-stage investing. The goal of this (anti-bias) training tool for capital allocators (early-career VCs, angel investors, and post-secondary MBA program students) is to help train investors to improve their portfolio performance by increasing their bias awareness when evaluating startup pitches, at the earliest funding stages.
WHAT
The open-source AI enabled platform would be accessible online. Users would log in to the platform and choose from a library of pre-recorded 5min videos seed-stage pitches from actuarial founders.
Some of the videos recordings are of mediated pitches where the founder uses a masked avatars with voice modulation and some are native. Investors hear the pitch, then they answer a questionnaire before going to an AI evaluation prompt/response area to “dialog” with our AI and getting a scorecard and can try again with other recordings in the library to learn to check their biases to improve their allocation and diversify their portfolio for bigger returns..
This isn't about hiding information; it's about reordering it.
After investors express genuine interest based on business merit, and some personality traits of the presenter, identities are revealed, and traditional due diligence begins. We're not replacing the investment process; we're making the first step fairer.
(06/10)
PARSONS E-LAB
We address a persistent market inefficiency in seed-stage investing. The goal of this (anti-bias) training tool for capital allocators (early-career VCs, angel investors, and post-secondary MBA program students) is to help train investors to improve their portfolio performance by increasing their bias awareness when evaluating startup pitches, at the earliest funding stages.
HOW
What is Beat The Odds (BTO) ?
(03/10)
A blind pitching tool where investors evaluate startups based purely on business fundamentals—no faces, no voices, no bias
PARSONS E-LAB
DIAGRAM DEPICTS: change to the latest version of the platform with the server, no longer live, but a library of recordings with a standard questionnaire and an open AI to prompt with questions and feedback. so a diagram with a person sitting in front of a computer alone doing anti-bias training. with an arrow to a cloud with a library of anonymized pitches by sector of pre-seed and seed pitches, and a library of live people pitching where we see real faces and not avatars.
show the step where the respond to questionnaire and ask questions> AI responds with improvements and suggestions to be less bias and how they might gain more alpha if they stayed open to the project or took it to the next level and brought in the founder, how they get scored and how they come back for more training to improve their score… shoe how an office (VC Firm) or MBA program in an institution might have this system or portal set up and their goal to improve alpha and diversity of portfolio…show then our back end capturing data on learning and how this might translate into bigger returns for the investor or allocator.