Inspiration

I believe hiring should be fair and transparent, but bias often hides in the process. I wanted to use AI + Tableau Next to uncover those blind spots and help teams make better decisions.

What it does

FairHire Insights analyzes applicant data to spot bias at each stage of hiring. It applies fairness metrics, runs “what-if” simulations, and triggers alerts when inequities appear.

How I built it

I connected Salesforce candidate data into Tableau Next, applied AI-driven analytics to measure fairness, and designed an interactive dashboard that highlights insights clearly.

Challenges I ran into

Getting Salesforce access, preparing sample hiring data, and embedding fairness testing into Tableau took extra problem-solving and iteration.

Accomplishments that I’m proud of

I created a working prototype that blends AI fairness testing with a user-friendly Tableau dashboard—something HR teams can actually use to act on bias.

What I learned

I learned how to integrate Salesforce data into Tableau Next, experiment with fairness metrics, and design analytics tools that balance transparency with usability.

What’s next for FairHire Insights

I plan to expand this into larger datasets, add predictive simulations, and integrate Slack alerts so hiring teams can respond in real time.

Built With

• sql
• tableau