These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
Biaslyze - The NLP Bias Identification Toolkit
Bias is often subtle and difficult to detect in NLP models, as the protected attributes are less obvious and can take many forms in language (e.g. proxies, double meanings, ambiguities etc.). Therefore, technical bias testing is a key step in avoiding algorithmically mediated discrimination. However, it is currently conducted too rarely due to the effort involved, missing resources or lack of awareness for the problem.
The biaslyze python toolbox supports developers in facilitating an ethically responsible development and deployment of the NLP components they work with. For developers, researchers and teams with limited resources, the toolbox provides a low-effort access to bias testing and mitigation for NLP use cases and helps to get started with the analysis of bias within NLP models and offers a concrete entry point for further impact assessments and mitigation measures.
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Tags:
- ai ethics
- biases testing
- large language model
- nlp
- trustworthy ai
- text understanding
- ai auditing
- bias
- natural language processing
- accountability
- ai oversight
- explainability
- ethical risk
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