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8.5.2  Capabilities

            In order to match the procurement norm being developed, the following is the set of six minimal
            requirements for suppliers of algorithmic systems to ensure that they are fair, trustworthy and
            transparent.



            Procedural Transparency

            Full disclosure of the type of choices made, parties involved, risks and mitigation actions in the
            process of creating an algorithmic model.


            Technical Transparency


            –  Full disclosure to allow the buyer of the source code and model to enable them to explain the
                model to inhabitants or other stakeholders.

            –  Access to the learnings of the model, ideally structured using MIM2, to prevent vendor lock-ins.

            –  Clarity about the process by which an algorithmic system makes decisions in an overall system,
                i.e., the optimization goals and outcomes of an algorithm.


            Technical Explainability


            –  Ability to explain on an individual level how a model creates certain outcomes.
            –  Ability to address any restrictions as to whom the information will be classified: e.g., public
                servants and other experts.



            Fairness

            Ensuring that the algorithmic systems do not systematically disadvantage, show bias against, or
            even discriminate against different social groups and demographics.



            Context

            However, the assessment of fairness depends on facts, events, and goals and, therefore, has to be
            understood as situation or task-specific and necessarily addressed within the scope of practice. For
            instance, there may be an explicit goal to address a historic imbalance, where positive discrimination
            is considered appropriate. Here the aspect of “fairness” needs to be seen in the wider context.












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