AI Hiring Fairness Audit Platform

3.30

Derivation Chain

Step 1 AI-driven changes in employment structure

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Step 2 Widespread adoption of AI hiring tools

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Step 3 Bias and fairness audit service for AI hiring tools

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Step 4 Fairness certification badge for job postings based on audit results

Problem

Companies using AI resume screening and video interview analysis tools face employment discrimination lawsuit risks but lack the capability to independently verify tool bias. Regulations similar to NYC Local Law 144 are being discussed in Korea, exposing unprepared companies to fines and brand risk. AI recruitment solution vendors also need to provide fairness evidence to clients to maintain contracts.

Solution

Automatically calculates pass rate disparities (adverse impact ratio) across protected attributes such as gender, age, and education level for AI hiring tools, and determines 4/5 rule violations. Issues audit reports and fairness certification badges, and provides improvement guides.

Target: AI recruitment solution vendors (10–50 employees); HR teams at companies with 200+ employees

Revenue Model: Single audit: ~$592 (per hiring tool); Annual certification badge renewal: ~$367; SaaS Annual Subscription: ~$2,243 (includes quarterly audits)

Ecosystem Role: Regulation

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

4.0/5

U Urgency

3.0/5

M Market

2.0/5

R Realizability

4.0/5

V Validation

3.0/5

NUMR-V Scoring System

N Novelty	1-5	How uncommon the service is in market context.
U Urgency	1-5	How urgently users need this problem solved now.
M Market	1-5	Market size and growth potential from proxy indicators.
R Realizability	1-5	Buildability for a small team with realistic constraints.
V Validation	1-5	Validation signal quality from competition and demand data.

SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (78%)

Tech Complexity

34.7/40

Data Availability

23.1/25

MVP Timeline

20.0/20

API Bonus

0.0/15

Feasibility Breakdown

Tech Complexity	/ 40	Difficulty of core implementation stack.
Data Availability	/ 25	Practical availability and cost of required data.
MVP Timeline	/ 20	Expected time to ship a usable MVP.
API Bonus	/ 15	Bonus for viable public API leverage.

Market Validation (56/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

14.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

12.0/15

Solo Buildability

5.0/10

Validation Breakdown

Competition	/ 20	Signal quality from competitor landscape.
Market Demand	/ 20	Demand proxies from search and mention patterns.
Timing	/ 20	Fit with current shifts in tech, behavior, and regulation.
Revenue Signals	/ 15	Reference evidence for monetization viability.
Pick-Axe Fit	/ 15	How well the concept serves participants in a trend.
Solo Buildability	/ 10	Practicality for lean-team implementation.

Technical Requirements

Backend [medium] Frontend [low] Data Pipeline [low]

Dashboard