At the same time, beneficent AI systems can contribute to wellbeing by seeking achievement of a fair, inclusive and peaceful society, by helping to increase citizen’s mental autonomy, with equal distribution of economic, social and political opportunity.

To avoid harm, data collected and used for training of AI algorithms must be done in a way that avoids discrimination, manipulation, or negative profiling.

The Principle of justice: “Be Fair”

The Principle of Justice: “Be fair”

For the purposes of these Guidelines, the principle of justice imparts that the development, use, and regulation of AI systems must be fair.

For the purposes of these Guidelines, the principle of justice imparts that the development, use, and regulation of AI systems must be fair.

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

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

Additionally, the positives and negatives resulting from AI should be evenly distributed, avoiding to place vulnerable demographics in a position of greater vulnerability and striving for equal opportunity in terms of access to education, goods, services and technology amongst human beings, without discrimination.

justice also means that AI systems must provide users with effective redress if harm occurs, or effective remedy if data practices are no longer aligned with human beings’ individual or collective preferences.

Lastly, the principle of justice also commands those developing or implementing AI to be held to high standards of accountability.

In a case of discrimination, however, an explanation and apology might be at least as important.

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.

Non discrimination

discrimination concerns the variability of AI results between individuals or groups of people based on the exploitation of differences in their characteristics that can be considered either intentionally or unintentionally (such as ethnicity, gender, sexual orientation or age), which may negatively impact such individuals or groups.

Direct or indirect discrimination through the use of AI can serve to exploit prejudice and marginalise certain groups.

Direct or indirect discrimination through the use of AI can serve to exploit prejudice and marginalise certain groups.

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

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

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.

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.

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.

AI systems should be designed not only to uphold rights, values and principles, but also to protect citizens in all their diversity from governmental and private abuses made possible by AI technology, ensuring a fair distribution of the benefits created by AI technologies, protect and enhance a plurality of human values, and enhance self determination and autonomy of individual users and communities.

The lack of reproducibility can lead to unintended discrimination in AI decisions.

Poor governance, by which it becomes possible to intentionally or unintentionally tamper with the data, or grant access to the algorithms to unauthorised entities, can also result in discrimination, erroneous decisions, or even physical harm.