AI Bias

What is AI bias?

• 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

What is training data for an AI?

• 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

What is testing data for an AI?

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

• The information used to slow down the AI

• The information used to confuse the AI

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

What is the key to reducing AI bias?

• Coming up with as diverse and complete a set of training data as possible

• Using only one type of data for training the AI

• Not using any testing data

• Not using any training data

What are the two different types of data used when computer scientists create AI?

• Input data and output data

• Primary data and secondary data

• Training data and testing data

• Raw data and processed data

How can AI bias occur?

• When the AI is trained on diverse datasets

• When the AI makes decisions based on facts

• When the AI does not learn accurately from its training data

• When the AI is designed to be completely objective

In the context of AI, what does bias refer to?

• An AI tool making incorrect or problematic decisions based on inaccurate training data

• An AI tool always producing consistent and accurate results

• An AI tool being completely unbiased in its decision-making process

• An AI tool having access to unlimited amounts of data

How can we reduce AI bias in a tool like a fruit detector?

• Show it more examples of non-fruits and other kinds of fruits

• Only train it with images of apples to avoid confusion

• Limit the variety of images it sees to reduce complexity

• Provide it with incomplete and biased training data

Why is it important to have diverse training data when developing AI?

• To ensure the AI always makes biased decisions

• To limit the AI's exposure to different scenarios

• To reduce the chances of AI bias and improve accuracy

• To confuse the AI and make its decisions less reliable

What is a potential consequence of an AI tool having bias?

• The AI tool always making accurate and fair decisions

• The AI tool failing to learn from its training data

• The AI tool making incorrect or unfair decisions

• The AI tool becoming too objective and losing functionality