As the CEO of an Algorithmic Fairness Company, I’m often asked this question by sophisticated quantitative teams:
“Can’t we just build this ourselves?”
My answer? Sure, you can. But should you?
Here’s what to consider:
Resource Allocation:
- Your AI talent is gold.
- Do you want them tackling high value business problems? Or focusing on regulatory testing and reporting?
Complexity & Maintenance:
- Think building commercial-grade AI fairness infrastructure is easy?
- Consider the scope: data ingestion, data cleaning, geocoding, demographic imputation, computation of regulatory metrics, ongoing monitoring, auto-alerting, and model de-biasing…
- Build it, then what? Maintenance? Quality Control? Regulatory updates?
- Key personnel turnover? There goes your institutional knowledge.
Independence & Credibility:
- Model builders shouldn’t be model validators.
- Effective challenge is a regulatory best practice.
- Our processes? Third-party audited for extra reliability.
The Bottom Line
The right fairness tool lets you focus on your core business, enhances compliance, and provides independent validation of the outcomes of your decisions.
That’s a much more sustainable strategy for the long term.
