Zurich/Stuttgart - The start-up LatticeFlow from the Greater Zurich Area and the testing organization DEKRA have agreed a strategic partnership. Together, the two partners intend to offer the industry’s first safety assessment service for applications on the basis of Artificial Intelligence.

The Zurich-based start-up LatticeFlow and the testing organization DEKRA, which is headquartered in the German city of Stuttgart, have agreed a partnership in the field of safety assessments for applications based on Artificial Intelligence (AI). The partners intend to evaluate AI models used by businesses in accordance with the latest ISO standards for data quality and model robustness, further details of which can be found in a press release.

To achieve this, the two companies will combine the AI development and deployment platform from LatticeFlow with DEKRA’s experience as a testing, inspection and certification organization. In this way, the aim is to provide clients with “a comprehensive and unbiased evaluation of their AI data and models”, the press release explains.

The assessment of AI applications is a time-critical matter, according to the press release, as the adoption of AI has rapidly accelerated over the past six months. “This raised safety concerns by prominent AI researchers, who called for a temporary halt to large-scale AI experiments – all with the hope of ensuring more secure and responsible implementations”, as Petar Tsankov, CEO of LatticeFlow, explains in the press release.

With a complete halt deemed to be unrealistic, Tsankov elaborates that he is instead keen to empower companies to “mitigate the risk associated with AI – and hasten the move towards safe AI adoption.”.

LatticeFlow was founded in 2020 as a spin-off from the Swiss Federal Institute of Technology in Zurich (ETH) by a team of AI researchers and experts. The company has developed the first scalable platform for evaluating AI data and models, which helps to guide AI teams through the AI lifecycle by independently identifying and rectifying issues with AI data and models. ce/ko

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