Explainability and Interpretability

by abala

Will you trust a potential global war threat decision with AI?Reuters reported that Deputy Secretary of Defense Kathleen Hicks was briefed on a new software created by US military commanders in the Pacific that can predict Chinese reaction to US actions in the region. The tool looks at data since early 2020. It predicts response across various activities such as congressional visits to Taiwan, arms sales to allies in the region, or when several US ships sail through the Taiwan Strait.It is heartening to see AI mature into strategic roles, especially in the backdrop of Zillow iBuying algorithms causing a loss of more than $300m a few weeks ago and costing over 2000 jobs and an unsold inventory of 7000 homes!Well, the answer lies in strategic oversight. Algorithmic decisions reflect data quality, rigorous training, and introduced biases, among other factors. Both these situations reflect on the maturity of AI as a technology and the need for better design and review.With AI becoming almost a black box to most engineers given the complexity of the high number of parameters and nodes, Explainable AI brings a set of tools and frameworks to help understand predictions made by machine learning models. Explainability shows how significant each of the parameters and nodes contribute to the final decision. This helps debug and improve model performance and understand the model's behavior. Interpretability communicates the extent to which a cause and effect can be observed within a system. i.e. the extent to which you can predict what will happen, given a change in input or algorithmic parameters. Together, they help understand how the model arrived at a decision and how each step contributed to that.Try over 100s of Explainability and Interpretability solutions on kandi to make your next big decision.

Use the open source, cloud APIs, or public libraries listed below in your application development based on your technology preferences, such as primary language. The below list also provides a view of the components' rating on different dimensions such as community support availability, security vulnerability, and overall quality, helping you make an informed choice for implementation and maintenance of your application. Please review the components carefully, having a no license alert or proprietary license, and use them appropriately in your applications. Please check the component page for the exact license of the component. You can also get information on the component's features, installation steps, top code snippets, and top community discussions on the component details page. The links to package managers are listed for download, where packages are readily available. Otherwise, build from the respective repositories for use in your application. You can also use the source code from the repositories in your applications based on the respective license types.
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shapby slundberg

A game theoretic approach to explain the output of any machine learning model.

Jupyter Notebook Updated: 4 mo ago License: Permissive

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limeby marcotcr

Lime: Explaining the predictions of any machine learning classifier

JavaScript Updated: 6 mo ago License: Permissive

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interpretby interpretml

Fit interpretable models. Explain blackbox machine learning.

C++ Updated: 12 d ago License: Permissive

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lucidby tensorflow

A collection of infrastructure and tools for research in neural network interpretability.

Jupyter Notebook Updated: 4 mo ago License: Permissive

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shapashby MAIF

🔅 Shapash makes Machine Learning models transparent and understandable by everyone

Jupyter Notebook Updated: 15 d ago License: Permissive

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explainerdashboardby oegedijk

Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

Python Updated: 6 mo ago License: Permissive

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DrWhyby ModelOriented

DrWhy is the collection of tools for eXplainable AI (XAI). It's based on shared principles and simple grammar for exploration, explanation and visualisation of predictive models.

R Updated: 6 mo ago License: No License

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