Dathena NEO AI Benefits

Meet Regulatory Requirements and Reduce Compliance Fines

End AI Magic and Build Trust

Dathena ensures that data users trust the quality of the data and the integrity of the algorithms. All results from Dathena’s solutions can be clearly articulated and explained. Data users always know how results are generated, what data is used, and what calculations are performed.

Remove Uncertainty and Randomness

Dathena’s solutions are deterministic. They are based on clear logical rules and relationships between established facts. There are no heuristics nor stochastic elements that introduce uncertainty or randomness into the algorithms. No matter how many times you run the same process, you always get the same result.

Capture Your Customers’ Trust
Meet Regulatory Requirements and Reduce Compliance Fines

Ensure Compliance with GDPR, CCPA, and other regulations

Dathena’s solutions meet AI explainability regulatory requirements, which include: GDPR’s stipulation that consumers have a “right to explanation,” the CCPA requirement that decisions be explainable, and U.S. insurance regulations that require insurers to always explain their rate and coverage decisions.

Meet the Requirements for Legally Defensible Evidence

Dathena’s solutions are legally defensible because of Dathena’s focus on data quality and integrity, transparent and ethical approach to product development, ability to liver reliable outcomes that are explainable continuously improving Dathena’s solutions help you comply with privacy regulations and deliver outcomes that are explainable and continuously improving. These factors, coupled with Dathena’s transparent and ethical approach to product development and commitment to data quality and integrity, make Dathena’s solution legally defensible.

Capture Your Customers’ Trust
Meet Regulatory Requirements and Reduce Compliance Fines

Eliminate the Biases

Ensure ethical and fair outcomes. With an explainable solution, your organization can always correct elements which can inadvertently lead to bias, whether caused by the data on which the models are trained and/or the design of the models.

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