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README.md

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 # Giskard: The testing framework dedicated to ML models 🐢🕊️
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 *Eliminate risks of biases, performance issues and errors in ML models.*
 
 We are building the first collaborative & open-source Quality Assurance platform for all AI models. 
-We help teams increase the safety of their AI development workflow, eliminate risks of AI biases and ensure robust, reliable & ethical AI models.
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-Testing Machine Learning applications can be tedious. Since ML models depend on data, testing scenarios depend on the domain specificities and are often infinite.
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-Where to start testing? Which tests to implement? What issues to cover? How to implement the tests?
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-At Giskard, we believe that Machine Learning needs its own testing framework. Created by ML engineers for ML engineers, Giskard enables you to:
-- **Scan your model to find dozens of hidden vulnerabilities:** The Giskard scan automatically detects vulnerability issues such as performance bias, data leakage, unrobustness, spurious correlation, overconfidence, underconfidence, unethical issue, etc.
-- **Instantaneously generate domain-specific tests:** Giskard automatically generates relevant tests based on the vulnerabilities detected by the scan. You can easily customize the tests depending on your use case by defining domain-specific data slicers and transformers as fixtures of your test suites.
-- **Leverage the Quality Assurance best practices of the open-source community:** The Giskard catalog enables you to easily contribute and load data slicing & transformation functions such as AI-based detectors (toxicity, hate, etc.), generators (typos, paraphraser, etc.), or evaluators. Inspired by the Hugging Face philosophy, the aim of Giskard is to become the open-source hub of AI Quality Assurance.
+We help teams increase the safety of their AI development workflow, eliminate risks of AI biases and ensure robust, reliable & ethical AI models.