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Eric Maugendre<a href="https://social.coop/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://social.coop/tags/interpretability" class="mention hashtag" rel="nofollow noopener" target="_blank">#interpretability</a> vs <a href="https://social.coop/tags/explainability" class="mention hashtag" rel="nofollow noopener" target="_blank">#explainability</a> 🧵"The explanations themselves can be difficult to convey to nonexperts, such as end users and line-of-business teams" <a href="https://www.techtarget.com/searchenterpriseai/feature/Interpretability-vs-explainability-in-AI-and-machine-learning" rel="nofollow noopener" translate="no" target="_blank">https://www.techtarget.com/searchenterpriseai/feature/Interpretability-vs-explainability-in-AI-and-machine-learning</a><a href="https://social.coop/tags/AIEthics" class="mention hashtag" rel="nofollow noopener" target="_blank">#AIEthics</a> <a href="https://social.coop/tags/compliance" class="mention hashtag" rel="nofollow noopener" target="_blank">#compliance</a> <a href="https://social.coop/tags/taxonomy" class="mention hashtag" rel="nofollow noopener" target="_blank">#taxonomy</a> <a href="https://social.coop/tags/ethicalAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#ethicalAI</a> <a href="https://social.coop/tags/AIEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#AIEvaluation</a> <a href="https://social.coop/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#linearRegression</a> <a href="https://social.coop/tags/trust" class="mention hashtag" rel="nofollow noopener" target="_blank">#trust</a> <a href="https://social.coop/tags/neuralNetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#neuralNetworks</a> <a href="https://social.coop/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#ML</a> <a href="https://social.coop/tags/governance" class="mention hashtag" rel="nofollow noopener" target="_blank">#governance</a> <a href="https://social.coop/tags/AIgovernance" class="mention hashtag" rel="nofollow noopener" target="_blank">#AIgovernance</a> <a href="https://social.coop/tags/safety" class="mention hashtag" rel="nofollow noopener" target="_blank">#safety</a> <a href="https://social.coop/tags/bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#bias</a>

Eric Maugendre"Feature importance helps in understanding which features contribute most to the prediction"A few lines with <a href="https://hachyderm.io/tags/sklearn" class="mention hashtag" rel="nofollow noopener" target="_blank">#sklearn</a>: <a href="https://mljourney.com/sklearn-linear-regression-feature-importance/" rel="nofollow noopener" translate="no" target="_blank">https://mljourney.com/sklearn-linear-regression-feature-importance/</a> <a href="https://hachyderm.io/tags/interpretability" class="mention hashtag" rel="nofollow noopener" target="_blank">#interpretability</a> <a href="https://hachyderm.io/tags/explainability" class="mention hashtag" rel="nofollow noopener" target="_blank">#explainability</a> <a href="https://hachyderm.io/tags/AIethics" class="mention hashtag" rel="nofollow noopener" target="_blank">#AIethics</a> <a href="https://hachyderm.io/tags/compliance" class="mention hashtag" rel="nofollow noopener" target="_blank">#compliance</a> <a href="https://hachyderm.io/tags/taxonomy" class="mention hashtag" rel="nofollow noopener" target="_blank">#taxonomy</a> <a href="https://hachyderm.io/tags/ethicalAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#ethicalAI</a> <a href="https://hachyderm.io/tags/AIevaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#AIevaluation</a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#linearRegression</a> <a href="https://hachyderm.io/tags/featureEngineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#featureEngineering</a>

Eric Maugendre<a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener" target="_blank">@datadon</a>"The following sections discuss several state-of-the-art interpretable and explainable <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#ML</a> methods. The selection of works does not comprise an exhaustive survey of the literature. Instead, it is meant to illustrate the commonest properties and inductive biases behind interpretable models and [black-box] explanation methods using concrete instances." <a href="https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.1493#widm1493-sec-0010-title" rel="nofollow noopener" translate="no" target="_blank">https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.1493#widm1493-sec-0010-title</a> 🧵<a href="https://hachyderm.io/tags/interpretability" class="mention hashtag" rel="nofollow noopener" target="_blank">#interpretability</a> <a href="https://hachyderm.io/tags/explainability" class="mention hashtag" rel="nofollow noopener" target="_blank">#explainability</a> <a href="https://hachyderm.io/tags/aiethics" class="mention hashtag" rel="nofollow noopener" target="_blank">#aiethics</a> <a href="https://hachyderm.io/tags/compliance" class="mention hashtag" rel="nofollow noopener" target="_blank">#compliance</a> <a href="https://hachyderm.io/tags/taxonomy" class="mention hashtag" rel="nofollow noopener" target="_blank">#taxonomy</a> <a href="https://hachyderm.io/tags/ethicalai" class="mention hashtag" rel="nofollow noopener" target="_blank">#ethicalai</a> <a href="https://hachyderm.io/tags/aievaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#aievaluation</a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#linearRegression</a>

Eric MaugendreModel "<a href="https://hachyderm.io/tags/interpretability" class="mention hashtag" rel="nofollow noopener" target="_blank">#interpretability</a> and [black-box] <a href="https://hachyderm.io/tags/explainability" class="mention hashtag" rel="nofollow noopener" target="_blank">#explainability</a>, although not necessary in many straightforward applications, become instrumental when the problem definition is incomplete and in the presence of additional desiderata, such as trust, causality, or fairness."<a href="https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.1493" rel="nofollow noopener" translate="no" target="_blank">https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.1493</a><a href="https://hachyderm.io/tags/aiethics" class="mention hashtag" rel="nofollow noopener" target="_blank">#aiethics</a> <a href="https://hachyderm.io/tags/compliance" class="mention hashtag" rel="nofollow noopener" target="_blank">#compliance</a> <a href="https://hachyderm.io/tags/taxonomy" class="mention hashtag" rel="nofollow noopener" target="_blank">#taxonomy</a> <a href="https://hachyderm.io/tags/ethicalai" class="mention hashtag" rel="nofollow noopener" target="_blank">#ethicalai</a> <a href="https://hachyderm.io/tags/aievaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#aievaluation</a>

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