The principle "Draft Ethics Guidelines for Trustworthy AI" has mentioned the topic "bias" in the following places:

    4. The Principle of Justice: “Be Fair”

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

    2. Data Governance

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

    2. Data Governance

    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.

    5. Non Discrimination

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

    5. Non Discrimination

    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.

    5. Non Discrimination

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

    5. Non Discrimination

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

    5. Non Discrimination

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

    5. Non Discrimination

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

    5. Non Discrimination

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

    5. Non Discrimination

    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.

    5. Non Discrimination

    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.

    5. Non Discrimination

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