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Trust & ethics

Trust can be based on different ethical frameworks. Trust is the most general factor towards AI.

Audience of trust

Not all stakeholders in AI applications have the same perspective and need for trust.

Domain experts and users

Users of the model must be able to trust AI applications in critical areas.

Users affected by the model

Must understand their situation, verify fair decisions of AI applications.

Regulators and agencies

Regulators must be able to certify model compliance with legislation in force.

Managers and executive board

Managers have to be able to assess regulatory compliance and understand corporate AI.

Trust perspectives

Trust in AI addresses different stakeholders and perspectives.

Trust is a complex phenomenon, comprising facts, structures, behaviours and emotions - and most of all, it's dynamic. We deliver dynamic AI trust evaluations and scenarios for different stakeholders, regulators, managers, users and society. Trust is never finally established, but an ongoing process in a complex landscape.

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Trust in AI
Trustability and ethical context

Trustability and ethical context

There is not one global ethical system. Although human rights are at the base of most ethical systems today, they differ in the details depending on cultural background and history. We adapt AI scenarios to the respective ethical framework.

Trusted AI solutions
for every purpose.

We provide customized AI trust training sessions, presentations and workshops for different stakeholders and different phases of the product life cycle.
Comprehensive AI trustability reports analyse corporate applications from the ground up and on all levels. The reports can be used for coporate communication with the different stakeholders involved.
We provide dashboard-based modeling and multiagent simulation to capture the complexity of risk and opportunity scenarios. Simulation provides a dynamic and ongoing assessment of AI efforts.
AI modeling and simulation

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