III. Privacy and Data Governance
To allow individuals to trust the data processing, it must be ensured that they have full control over their own data, and that data concerning them will not be used to harm or discriminate against them.
III. Privacy and Data Governance
When data is gathered, it may reflect socially constructed biases, or contain inaccuracies, errors and mistakes.
V. Diversity, non discrimination and fairness
V. Diversity, non discrimination and fairness
V. Diversity, non discrimination and fairness
V. Diversity, non discrimination and fairness
V. Diversity, non discrimination and fairness
Data sets used by AI systems (both for training and operation) may suffer from the inclusion of inadvertent historic bias, incompleteness and bad governance models.
V. Diversity, non discrimination and fairness
The continuation of such biases could lead to (in)direct discrimination.
V. Diversity, non discrimination and fairness
The continuation of such biases could lead to (in)direct discrimination.
V. Diversity, non discrimination and fairness
Harm can also result from the intentional exploitation of (consumer) biases or by engaging in unfair competition.
V. Diversity, non discrimination and fairness
Harm can also result from the intentional exploitation of (consumer) biases or by engaging in unfair competition.
V. Diversity, non discrimination and fairness
the way in which the programming code of an algorithm is written) may also suffer from bias.
VII. Accountability
Finally, when unjust adverse impact occurs, accessible mechanisms should be foreseen that ensure adequate redress.