Algorithmic Bias
Algorithmic Bias
Unfair outcomes for certain people or groups resulting from biased logic or calculations in AI and other systems.
In Simple Terms
Algorithmic bias is when a computer's output ends up being skewed or unfair. For example, an AI trained on past hiring records might learn from a history of mostly hiring men, and then unfairly rate women lower as a result. Facial recognition systems have also been reported to struggle with accurately identifying certain skin tones. These biases occur because the data used for training already carries the prejudices present in society.
Behind the Name
Algorithmic bias combines two words: algorithm, meaning a set of step-by-step instructions for solving a problem, and bias, meaning an unfair slant or preference. This kind of bias creeps in when skewed data or assumptions get baked into the way the calculations work. Even when no one means to cause harm, the results can still end up being discriminatory or unfair.
Take a Closer Look!
Algorithmic bias refers to the phenomenon where AI or software produces unfair results for certain people or groups.
Computers build their decision-making criteria based on the data they are given, so if that data is skewed, the computer ends up thinking in a biased way too.
In simple terms, it is as though the computer has learned and absorbed the flawed patterns that already exist in the world.
The main reason this happens is that the training data fed into AI contains bias.
For example, if AI is used to screen job applicants in a field where most successful candidates in the past were men, the AI may mistakenly assume men are more capable and evaluate women less favorably.
Even when no bias is intended, this kind of skew risks amplifying discrimination and inequality that already exist in society.
Bias can also enter a system when developers' unconscious assumptions get reflected in the program's design.
This is why it is important to bring in diverse perspectives during development and to regularly verify that AI decisions are fair.
Technical research into ensuring fairness is advancing, and this remains a challenge that all of society needs to approach thoughtfully.