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Responsible AI Blog

Current US legislation prohibits AI applications in recruiting, healthcare, and advertising from discrimination and bias. This requires organizations who deploy such systems to test and prove that their solutions are robust and unbiased – in the same way that they’re required to comply with security and privacy regulations. This session introduces Pacific AI, a no-code tool built on top of the LangTest library, which applies Generative AI to:– Automatically generate tests for accuracy, robustness, bias, and fairness for text classification and entity recognition tasks– Automatically run test suite, create detailed model report cards, and compare different models against the same test suite– Publish, share, and reuse AI test suites across teams and projects– Automatically generate synthetic training data to augment model training and minimize common model bias and reliability issuesThis session then presents how John Snow Labs uses Pacific AI to test and improve its own healthcare-specific language models.

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Current US legislation prohibits AI applications in recruiting, healthcare, and advertising from discrimination and bias. This requires organizations who deploy such systems to test and prove that their solutions are...

In today’s landscape of AI-driven recruitment, candidate-job matching models play a pivotal role in enhancing the hiring process’s efficiency and effectiveness. This necessitates rigorous evaluation to ensure fairness and equity....

Builders and buyers of AI systems are required to test and show that their systems comply with legislation – on safety, discrimination, privacy, transparency, and accountability. This talk covers recent...

Grant will fund R&D of LLMs for automated entity recognition, relation extraction, and ontology metadata...

This talk presents new levels of accuracy that have very recently been achieved, on public and independently reproducible benchmarks, on the three most common use cases for language models in...