Operational risks in AI assess misleading programming and security vulnerabilities.

Algorithmic biasEdit

Machine-learning algorithms identify patterns in data and codify them in predictions, rules and decisions. If those patterns happen to reflect some existing bias then machine-learning algorithms are likely to amplify that bias and may produce outcomes that reinforce existing patterns of discrimination.

Cyber attacksEdit

AI systems can be trained to detect, monitor and repel cyber attacks. They identify software with certain distinguishing features – for example, a tendency to consume a large amount of processing power or transmit a lot of data – and then take action to close down the attack.

Programmatic errorsEdit

Errors are a common problem with all computer programs and AI is no exception. Where errors exist, algorithms may not perform as expected and may deliver misleading results that have serious consequences – for example, the integrity of an organization’s financial reporting could be compromised.