Posts tagged bias

Physiognomy’s New Clothes

Medium, AI, machine learning, physiognomy, bias, prejudice, false objectivity, Blaise Aguera y Arcas

The practice of using people’s outer appearance to infer inner character is called physiognomy. While today it is understood to be pseudoscience, the folk belief that there are inferior “types” of people, identifiable by their facial features and body measurements, has at various times been codified into country-wide law, providing a basis to acquire land, block immigration, justify slavery, and permit genocide. When put into practice, the pseudoscience of physiognomy becomes the pseudoscience of scientific racism.

Rapid developments in artificial intelligence and machine learning have enabled scientific racism to enter a new era, in which machine-learned models embed biases present in the human behavior used for model development. Whether intentional or not, this “laundering” of human prejudice through computer algorithms can make those biases appear to be justified objectively.


Data, Fairness, Algorithms, Consequences

Medium, danah boyd, data, privacy, algorithms, bias, discrimination, transparency, responsibility

When we open up data, are we empowering people to come together? Or to come apart? Who defines the values that we should be working towards? Who checks to make sure that our data projects are moving us towards those values? If we aren’t clear about what we want and the trade-offs that are involved, simply opening up data can — and often does — reify existing inequities and structural problems in society. Is that really what we’re aiming to do?


Facial recognition database used by FBI is out of control

facial-recognition, pattern-matching, bias, FBI, USA, policing, errorism

Approximately half of adult Americans’ photographs are stored in facial recognition databases that can be accessed by the FBI, without their knowledge or consent, in the hunt for suspected criminals. About 80% of photos in the FBI’s network are non-criminal entries, including pictures from driver’s licenses and passports. The algorithms used to identify matches are inaccurate about 15% of the time, and are more likely to misidentify black people than white people.