Developers and implementers need to ensure that individuals and minority groups maintain freedom from bias, stigmatisation and discrimination.

The datasets gathered inevitably contain biases, and one has to be able to prune these away before engaging in training.

Instead, the findings of bias should be used to look forward and lead to better processes and instructions – improving our decisions making and strengthening our institutions.

Those in control of algorithms may intentionally try to achieve unfair, discriminatory, or biased outcomes in order to exclude certain groups of persons.

Harm may also result from exploitation of consumer biases or unfair competition, such as homogenisation of prices by means of collusion or non transparent market.

Discrimination in an AI context can occur unintentionally due to, for example, problems with data such as bias, incompleteness and bad governance models.

Machine learning algorithms identify patterns or regularities in data, and will therefore also follow the patterns resulting from biased and or incomplete data sets.

While it might be possible to remove clearly identifiable and unwanted bias when collecting data, data always carries some kind of bias.

While it might be possible to remove clearly identifiable and unwanted bias when collecting data, data always carries some kind of bias.

Therefore, the upstream identification of possible bias, which later can be rectified, is important to build in to the development of AI.

Moreover, it is important to acknowledge that AI technology can be employed to identify this inherent bias, and hence to support awareness training on our own inherent bias.

Moreover, it is important to acknowledge that AI technology can be employed to identify this inherent bias, and hence to support awareness training on our own inherent bias.

Accordingly, it can also assist us in making less biased decisions.