Inappropriate bias is caused by overweighting of particular classes in algorithms

Inappropriate bias is caused by data used for training not being representative of the real world

Inappropriate bias only affects ML systems that process data about people

Inappropriate bias can be caused by aspects of the algorithm or the data

Complexity

Lack of standards

Lack of robustness

Lack of transparency

A house cleaning robot adjusts the path it takes on a daily basis based on the most efficient route and the amount of dirt it is able to remove.

A financial system that chooses which stocks to buy regularly optimizes based on new changes in stock price to improve its performance.

An image recognition system regularly searches the internet to find new examples to add to its training data.

A health warning system uses data from thousands of people to predict who is most likely to suffer from a particular health condition based on their behavior.

Many test cases can be generated

The purpose is to detect defects

Production data is not used

The purpose is to compare two variants of a system

As AI systems improve, the likelihood of reaching the technological singularity increases.

AI systems get better over time as they are exposed to more data.

The systems used to play chess against human experts in the 1990’s are no longer considered AI.

AI systems increasingly behave like humans.