AI Bias Calculator

Estimate how algorithmic systems may misjudge your identity based on real-world bias data.

Race/Ethnicity:

Gender:

Age:

How to Use the AI Bias Calculator (and Why It Matters)

The AI Bias Calculator is a simple educational tool that estimates your potential risk of being misjudged by automated systems based on your demographic profile. These include facial recognition tools, hiring algorithms, predictive policing, and loan scoring systems.

What It Does

Using available bias research and real-world examples, this tool helps you:

Understand how AI may treat different identities unequally
Visualize how race, gender, and age can alter how algorithms “see” you
Reflect on the ethical implications of automated decision-making

How to Use the Calculator (Step by Step)

Step 1: Select Your Race/Ethnicity

Choose the group you most identify with. This reflects known disparities in AI recognition systems and data-driven tools.

Step 2: Choose Your Gender

Select your gender. Nonbinary and women-identifying individuals are often misclassified or underrepresented in training data.

Step 3: Enter Your Age

Age plays a subtle role in algorithmic performance. People under 25 and over 60 may face skewed results in systems like job hiring, healthcare diagnostics, and facial detection.

Step 4: Click “Check Bias Risk”

Press the red “Check Bias Risk” button. The calculator will use known research and risk factors to estimate your bias risk score (as a percentage).

Interpreting the Results

0% – 15%: Minimal risk – You are likely represented in most datasets.
20% – 40%: Moderate risk – Be cautious in algorithmic environments (hiring, credit).
40% – 60%: High risk – You may be subject to AI bias in multiple contexts.
60% – 95%: Severe risk – AI systems are statistically much more likely to misjudge or penalize people with your profile.

Why This Matters

AI is not neutral. It learns from biased data collected from decades (or centuries) of societal inequality. This means:

Black faces are more likely to be misidentified by facial recognition.
Women are underrepresented in tech hiring algorithms.
Latino applicants may be rejected by credit-scoring bots at higher rates.

By understanding your bias risk, you can ask better questions, demand transparency, and advocate for more ethical systems.