Our company ethos revolves around increasing diversity and representation for POC and there is no greater under-represented sector than machine learning and AI
Building Inclusive Transparent Explainable Accurate Robust AI
Publicly available datasets are heavily Caucasian biased in their weighting of ethnicities and facial diversity.
Common facial biometric applications are poorly trained for darker skin tones, features and low contrast scenarios.
”Melanoma is most common in white skin. Black people are less likely to get it, but they are more likely to die from it.Poppy Noor,The Guardian, New York
Why Racial & Sex Fairness
Matters In Machine Learning
AI Is Used To…
- Conduct demographic surveys of racial groups for census counts (1)
- Measure racial diversity in geographic regions and how racial groups interact amongst each other (2)
- Analyse facial traits for facial recognition and biometrics (3)
- Facial recognition is poor at guessing age in people of colour (4)
- Facial recognition struggles with diversity in racial groups, especially in terms of headwear (veils, hijabs), facial hair (moustaches, beards) and facial ornaments such as glasses
- Facial recognition tools produce higher false positives for African and East Asian faces compared to Caucasian (6)
- Enforce the law through photo matching CCTV to facial databases
- High false positives can lead to the wrong person being convicted (7)