Ethics and bias

• AI projects can be biased if the training data used to build the AI system is not diverse and representative of the target population.

• True

• False

AI projects are immune to biases since they operate solely based on data and algorithms.

• True

• False

AI projects do not need to account for historical biases in data since they are reflections of past realities and not indicative of present society.

• True

• False

Ethical concerns in AI projects are only relevant during the development phase and do not require ongoing consideration once the system is deployed.

• True

• False

AI algorithms are inherently neutral and unbiased, and any biases observed in AI systems are solely due to human error in data collection and model development.

• True

• False

Ethical AI projects ensure transparency by providing clear explanations for the decisions made by the AI system, allowing users to understand the underlying processes.

• True

• False

Bias in AI projects can be eliminated by removing demographic attributes like race and gender from the training data to make the system more impartial.

• True

• False