Tomorrow's AI in your processes today.
In the financial industry, data-driven automated decisions promise significant optimization and savings.
However, blind faith in AI systems is not an option due to regulatory requirements and business risks.
On the way to the optimal data basis for your AI projects: Because you can only get good information with an excellent database.
High data quality is the basic building block for important business decisions. Insufficient data leads to poor performance and results from AI systems. Then, there are too few clues to learn a machine learning model that fully captures the complexity.
The goal of tailored use of Explainable AI is to make better decisions.
Only when the background of a decision or recommendation is understood across departments can work on a project in a target-oriented manner. Employees from different departments are thus able to critically examine system outputs and work with AI.
Explainable and comprehensible decisions thus lead not only to easier communication, but also to considerable time savings. Explainable decisions thus create acceptance of the systems used.
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Transparency and the ability to articulate why a particular decision is made by the AI system is critical to trust and thus adoption of machine learning applications.
The growing importance of AI systems makes it inevitable for companies to increase the acceptance of algorithm-based decisions among their customers and employees.
Different stakeholders should be able to understand internal decisions. This not only ensures the use of AI systems as "team members" but also extends the scope of AI to applications where "black boxes" are not acceptable.
Explain. Justify. Build trust. Learn more about designing interactive user experiences with Explainable AI!