Understand bias in AI
Because an AI model is trained on a data set to recognize patterns and perform tasks, the model is only as good as the data it receives.
The output from the AI tool may be affected by both systemic bias and data bias.
Systemic bias is a tendency upheld by institutions that favors or disadvantages certain outcomes or groups.
- Systemic bias exists within societal systems like healthcare, law, education, politics, and more.
- Even if the people who design and train an AI model think they’re using high-quality data, the data may already be biased because humans are influenced by systemic biases
Data bias is a circumstance in which systemic errors or prejudices lead to unfair or inaccurate information, resulting in biased outputs.
- Maybe you’re developing a work presentation, you ask an AI image generator to create a photo of a CEO. All of the images generated appear to be white males. Based on this result, you might assume that all CEOs are white men. Obviously, this data is biased.
- Therefore, the more the data represents a wider variety of people, the more inclusive the outcome of the image generation will be
Just as AI models reflect the biases of the data used to train them, they also reflect the values of the people who design them. In other words, AI models are value-laden.