Bias from unbalanced training data

Bias from systematic measurement errors

Bias from reinforcing harmful stereotypes

Bias from incomplete data

Bias from harmful stereotypes

Bias from relying on pre-existing beliefs

Bias from unrepresentative data

Bias from systematic data collection errors

Bias from misclassification of minority groups

Bias from balanced model training

Bias from explicit prejudices

Bias from diverse data collection

Bias from data differing from actual variables

Bias from diverse data collection

Bias from explicit prejudices

Bias from balanced model training

When AI makes faster decisions.

When a result is more favorable to a certain outcome.

When AI learns from too much data.

When AI models are created in secrecy.

To make the computer run faster

To ensure the machine learning model is accurate

To reduce electricity usage

To make the data collection process easier

Communicate with other robots

Gather information from the environment and provide feedback to the robot

Provide power to the robot

Control the movement of the robot

When an AI tool makes a decision based on incomplete data

When an AI tool makes a decision that is always correct

When an AI tool makes a decision based on random data

When an AI tool makes a decision that is wrong or problematic because it learned from training data that didn't treat all people, places, and things accurately

The information given to an AI to help it learn how to do a specific task

The information used to check whether the AI that was created is reliable and accurate

The information used to confuse the AI

The information used to slow down the AI