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Published July 16, 2024 | Version 24.05.14 Public Demonstrator
Python Library Restricted

Explainability Platform Kaa

Description

Description

Kaa is a framework allowing to apply several methods and metrics from several dedicated libraries on AI model
to verify them.
It is built around a kernel offering a text interface ( textitText-based User Interface) and hosting two sets of
plugins:

  • plugins of explainability libraries called pluginsXAI
  • the plugins of the textitvs explicability checkboxes called pluginsUCXAI

In this version of Kaa, the AIX360, Alibi, Captum, PAIR saliency, Shap and Xplique plugins are provided

Documentation

User Manual

User manual is available for download here.

Methodological Guidelines

Scientific Contribution

Benchmark

Application over use-case

State of the Art

Files

Files

Restricted

The record is publicly accessible, but files are restricted to users with access.

Additional details

Trustworthy Attributes
Accountability
Engineering roles
Data Engineer
ML-Algorithm Engineer
IVVQ Engineer
Engineering activities
Use cases
NLP
Object Reidentification
Time series
Visual Inspection
Vision
Surrogate Model
Functional Set
Explainability
Functional maturity
Technological maturity
Image Link
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xU56IHyokTL+H4K/xNNxg52ojvfOvb6n33LqeP//tTaz7e88H9jUf2BvL9Ijt4uJ7On30pTja2wWq1gSspaAmGjz7ahxVLvwezyPDQY79GXiDHGGw40odtH7jgctqQ7fPq/aueNg0X14nw829Tf716yXNGcK6qOQSJoqcnCFXhMMkmRFmfIQ836p4ZVcGN8M+gcSAaiyM3OwsQOCLJDPvN7zaQEUNHti5ZcvOaO2+/mw4bPlhdv379X9P9WwGWXH8D3t6wDT6fXw/Y8X/5/m2rfvaTxhdiqbPa8cO1dOrk0QiFI2hv60Ruth3Zfj+Kc3Px3u59mHvFN1BZUQpOJEgSgdVqwqOPv4jCwjK8v38fTpxsw7BBlagIZEHv2XWvcdgtCBTkw+vxQMkoYLog+lTQmxzhXMbXq8WoGkKNee73u5CdlQ1F0YweRW/QEqkU3A4r7K4sPPjIc4xTh3jHbXes0jn4/F7p+7ffxv6Gve5Bn3fT6XNh1aOPsmXL7paW3rPsk0Xf+vbw+hMNg3s6mhWn00GmfmMyCXZ1ISvLa2yHFQVysHnrLgysGAi/yw6rSGGTGTxWAq/Lg5OnG/HUT38Ih8WCl197E8FQFLLVhvazIRw4dAIN7T0ggoQJI6sgQEMqo0CSZTSeaUM8nsLIoQORTCaN+a4y4NNDdRg8cABkkUOkBKF4Ao3Nbbhs4ni8+NJm9vu124Ubb7r5nWX33vPA8qVLlcWLr2eqpuLTTw98MQH279+PZcvuQSKd5k0n68WNm9/c/cn+2gwTbVM+2vsxyfVms7wcL6xmE9Fd3eN0QTTZsH3Hexg9egQ4VKTVFNR0BiWFxfj00EkcOVKP225ejEEVpTj+WQsOHT2GmgPHsfvDA9DAcPJkA9q7ghDNZgTyC+BzuFB/sgHRvjhGDKtAJpWEQDnSmoD6+tMYXl1qZAVIBB09Qbg82Xh3T43yyAuviktuWNLym9/+ehwhRFm0cBEZPXYUv/6GxZ9H9fMF0DG4ugqCJOKnP/kpt9rssWuuXbRr08YN77WGM9PXv7XLXX/iFLG7nGzEiKEk2NODkvJSJNIK1m/ZhgGDKpHtcSKdyhjReOKlo1Fz8Chqaw5iyoSxuGzcQIwZVoFJE8dDohqunj0VC2ZPRUtnCG+/t8+Iv5m0gu54AlQ04RujhkJJZSBT0cgJ0Wgvhg4ohSwIRkx+98MD2p+27ORv7PhYnDlrbus9d98xdcGi6yN33XE7qT9+nFksNry+du3FCbBx0ybMnTcXBTk52H/oIPnjmj8Ki2/4duOdd9z1v10e/5tdvckZr2/a6m5ubdPGTRgFNdlHBpbmgQgUr67dDGgURcWFMFlNSMYTGDGsGo1NzXhnzwegZgscTitEqKisqMB7+2q4xWrVxo8ZyUaPHMZc3hx2uvUs23voFDvx2SlGQFkiQ5hs97BTrZ2sMxplefmFrPbgcf7717eS1Wt3UJVa6ZLFN237/R9evK6l6UxDefkA+ln9MU0313vvW3Ehmv/1ucDuHdsQTaVx1byr8b9eeEa49fa7tYdXPYWfrFzuf+KJx5a9vm79/YlIB37+o++xAYVZlDIVoXgGaze+A1kQMWb4IBSXlsHutMDptOHYiSZ88FENPC4TBg8ajJa2IHtlw1Z67FQr7HY7VMaR5XWgKJCH7nAf9h+oM8KT02FHTk6OsWL0xWPwe9xIxJOQLHYMGjJ425233bZq2vRpH+qrxLPP/IpmVM6aGk/h2eef+7v8vvDByIYtWxHu6cGwMWPIptdepQ8/8nMjMTaerr9s2Yofv7Zn17sFv33yAVaZ66RqOgXZakZzRwiHDn2Gjs42yGYbysqKUVlaihyPHQoonnrxdf7W7hoydtyEHo/LtaOqaiCJxRNc4xpSqYwRZ90eFwgnSKVScDmcsNttaG5uZiXFxTQvN+/wJRPHbykqLj22bt16LFx4rfDqa+u4LImspaUVS5fe+V/y+sLnAtfMn21c16xZzW+99Xvart27SHlFgZBIpz/csHbjtBkzprzz6L//NvD8Yyu53SoRRUuiKN+F0sBERCJ9aGwN4kTjGfTFYsh2WtDSHdO279pLp0yb9rvNG9/41+EjRp3t6u6GqmrghCGRSBnrf3aOH5rGkUgk4PN44XG78dmJz4zoq2oMA0IRfVi0rq6OcM61P6xeA1lyfyHy+LLfB+zcuRMQVUyfPBMPP/xv0soHViiffHxgwo03LNlX6DWzyd8YQcuKS5Djc8Nhl2CVJLhcLoj0XIMDixt3rXxSC6aIsPfDPWMBsfYHd9xm7unqUTOKanSJeinz8zlfT0d6L5/l8xs9f3NLM8pLS+B0OuH1Z7E5V81jFSVl2L7jHcy8/IqL4vKlP5DQ8ciqn2HOrEXklddW08cfe9I/f/7V+2QeL71h0QxWu6+WQtVgsggQRTNisTg0hRlR1Zvl1p7741Zh0vRZNc8+/8z0mxbflHrplZfVLRs2GUdf/0xc9NHYf8YDK/8NV8xYwDvaO3UhO8eMHbvnrU2vlVYEstiga6+k+hLYm0ghEs6ci7UahyxTRJNxhHtTCOQV9fncvti2N7cLqWTyn04eX1UAHR0drSgsLNRjJimvqPxdKqXe+Mmnx4XxQyuNIOQwmeHJt6Cy0AuNMTidDuysrTNqr7ioyKmwNIYNG47Tpxv6h9FF4it/JTb/qrkYOHAgst0uPn/e3A6LxZI+0dAByWQG0Ri4AqSVNGKpOGLJGKLxOHp7++DzepGTk71HFsyYMWMqLrvksv5hdJHol8/kysvLeSqVpg67NZZIKQ37DxzVHZwRiYCRNJimgjNmHGQIlKClpR1Wmx1VVZVGPBs1amR/DONLoV8ESCaT/NIpU4iiKJGxEyY0159uItv37OdhRYRstsDjdMJpscFhMsHtdEFRVWSUDPpiEbv+fCIZ649hfCl8ZQ+AceQswWKzEEmS0uXlpeFoMoG6kw38TGMjkok4svxueNwuOBxOpI98hpbmFsiSsT9vtKiCcMFE/g9Hvwigr9l6t6YjHOqh6WQGgewszL18HOpOtaClpRNUtiCpcJitDkMI8LjR35/DV1qNvxL6ZQpMnToVJflFxgZHRUm5yWGyGi0roKIs4MWcSWMwZfQgXDq0HJdPHAOX2wlF00CNlHNub/DrQr9UgA5/bo5Bw+5yHSwqKbo62N1BkqkMVEVDNBk9t9unaRAkCZIoQBIlWCx2Y/KbZHN/DeOi0W8fSydSCeMajkYbnA4rVCVBOnuiBjlKzu3fUyoYV33KJOIJnDnTbOTWU6e+ngyA/hQgEg4ZFXCk7kStpC9/Woa0tYchC7Kxc/tnMM6NfYJoXwSRSHiefm/fvr39NYyLRr8J0NF21ri2tnSY0vE4vFYzWpu7IckiNELAKAETyPlTG/LnQ8o+/R+L1dpfw7ho/J8AAAD//xOqFuXbA09TAAAAAElFTkSuQmCC