Limitations and risks of Machine Learnings Clause Samples

The 'Limitations and risks of Machine Learnings' clause defines the boundaries and potential issues associated with using machine learning technologies within the agreement. It typically outlines that machine learning systems may not always produce accurate or reliable results, and may be subject to biases, errors, or data limitations. This clause serves to inform parties of the inherent uncertainties and risks involved, helping to allocate responsibility and manage expectations regarding the performance and outcomes of machine learning solutions.
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Limitations and risks of Machine Learnings. The product produces and displays Artificial Intelligence (AI) generated content and the User should be aware of the limitations and risks associated with an open-ended machine learning algorithm. Twyg uses third party machine learning technology to deliver its services: Known risks with using an open-ended machine learning algorithm includes but are not limited to: i) Machine learning components can only be expected to provide reasonable outputs when given inputs similar to the ones present in the training data. Even if an ML system is considered safe when operated under conditions similar to training data, human operators can provide unfamiliar inputs that put the system into an unsafe state, and it is often not obvious to an operator what inputs will or won’t lead to unsafe behavior ii) Machine learning components are biased. ML components reflect the values and biases present in the training data, as well as those of their developers. Systems using ML components—especially systems that interact with people in open-ended ways—can perpetuate or amplify those values. iii) Safety is a moving target for ML systems. The safety characteristics of ML systems change every time the ML components are updated, for example if they are retrained with new data, or if new components are trained from scratch with novel architectures. Twyg cannot guarantee full safety and/or unbiased results and the User is requested to report any offensive, biased or similarly harmful machine learning generated results directly to the Twyg. Twyg will take appropriate action to remove any harmful content and provide feedback to relevant service providers.
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