{"id":3982,"date":"2022-02-02T10:22:44","date_gmt":"2022-02-02T10:22:44","guid":{"rendered":"https:\/\/www.coala-h2020.eu\/?page_id=3982"},"modified":"2023-01-26T13:03:39","modified_gmt":"2023-01-26T13:03:39","slug":"why-engine","status":"publish","type":"page","link":"https:\/\/www.coala-h2020.eu\/index.php\/why-engine\/","title":{"rendered":"Why Engine"},"content":{"rendered":"\n<section id=\"gm453cf5e\" class=\"wp-block-gutentor-m3 section-gm453cf5e gutentor-module gutentor-container-cover has-color-bg has-custom-bg\"><div class=\"grid-container\">\n<section id=\"gm1bda698\" class=\"wp-block-gutentor-m4 section-gm1bda698 gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gm986802\" class=\"wp-block-gutentor-m4-col col-gm986802 gutentor-single-column  grid-lg-6 grid-md-12 grid-12\"><div id=\"section-gm986802\" class=\"section-gm986802 gutentor-col-wrap\">\n<h4 class=\"wp-block-heading\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-white-color\">Why Engine<\/mark><\/h4>\n\n\n\n<h1 class=\"wp-block-heading\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-white-color\">Explainable AI in Manufacturing Industry<\/mark><\/h1>\n<\/div><\/div>\n\n\n\n<div id=\"col-gm32730f\" class=\"wp-block-gutentor-m4-col col-gm32730f gutentor-single-column  grid-lg-6 grid-md-12 grid-12\"><div id=\"section-gm32730f\" class=\"section-gm32730f gutentor-col-wrap\">\n<div id=\"section-gca802f\" class=\"wp-block-gutentor-e6 section-gca802f gutentor-element gutentor-element-image\"><div class=\"gutentor-element-image-box\"><div class=\"gutentor-image-thumb\"><img decoding=\"async\" class=\"normal-image\" src=\"https:\/\/www.coala-h2020.eu\/wp-content\/uploads\/2022\/02\/Icons-website-2.png\"\/><\/div><\/div><\/div>\n<\/div><\/div>\n<\/div><\/div><\/section>\n<\/div><\/section>\n\n\n\n<section id=\"gm34ad186\" class=\"wp-block-gutentor-m3 section-gm34ad186 gutentor-module gutentor-container-cover has-color-bg has-custom-bg\"><div class=\"grid-container\">\n<section id=\"gm32d9215\" class=\"wp-block-gutentor-m4 section-gm32d9215 gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gmca19e8\" class=\"wp-block-gutentor-m4-col col-gmca19e8 gutentor-single-column  grid-lg-12 grid-md-12 grid-12\"><div id=\"section-gmca19e8\" class=\"section-gmca19e8 gutentor-col-wrap\">\n<p class=\"has-text-align-center\">Why Engine is a new, experimental solution component that will allow the assistant to answer \u201cwhy\u201d questions concerning advices and predictions provided by the DIA. Explainability will increase trust in the applied AI-based functions.&nbsp;<\/p>\n\n\n\n<p class=\"has-text-align-center\">The Why Engine will generate explanations of the provided predictions and recommendations to workers on the shop floor and to new workers in training. This is an exciting new application of explainability in the manufacturing context.&nbsp;<\/p>\n\n\n\n<p class=\"has-text-align-center\">The Why Engine will communicate with the train data quality analytics models of the Prescriptive Quality Analytics component and interact with the Cognitive Advisor service. The basic rules and recommendations generated by the Cognitive Advisor will be used for its interpretation.<\/p>\n<\/div><\/div>\n<\/div><\/div><\/section>\n<\/div><\/section>\n\n\n\n<section id=\"gma7292fa\" class=\"wp-block-gutentor-m3 section-gma7292fa gutentor-module gutentor-container-cover\"><div class=\"grid-container\">\n<h2 class=\"has-text-align-center wp-block-heading\">Video<\/h2>\n\n\n\n<section id=\"gm1431ff6\" class=\"wp-block-gutentor-m4 section-gm1431ff6 gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gm29325f\" class=\"wp-block-gutentor-m4-col col-gm29325f gutentor-single-column  grid-lg-4 grid-md-12 grid-12\"><div id=\"section-gm29325f\" class=\"section-gm29325f gutentor-col-wrap\"><\/div><\/div>\n\n\n\n<div id=\"col-gmd06c91\" class=\"wp-block-gutentor-m4-col col-gmd06c91 gutentor-single-column  grid-lg-4 grid-md-12 grid-12\"><div id=\"section-gmd06c91\" class=\"section-gmd06c91 gutentor-col-wrap\">\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<div class=\"nv-iframe-embed\"><iframe loading=\"lazy\" hcb-fetch-image-from=\"https:\/\/www.youtube.com\/watch?v=H5duoFRx8TI\" title=\"COALA Why Engine Prototype for Explainable AI in Manufacturing Industry\" width=\"1200\" height=\"675\" src=\"https:\/\/www.youtube.com\/embed\/H5duoFRx8TI?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen><\/iframe><\/div>\n<\/div><figcaption class=\"wp-element-caption\">How to acess and use the Why Engine Prototype<\/figcaption><\/figure>\n<\/div><\/div>\n\n\n\n<div id=\"col-gm97fc49\" class=\"wp-block-gutentor-m4-col col-gm97fc49 gutentor-single-column  grid-lg-4 grid-md-12 grid-12\"><div id=\"section-gm97fc49\" class=\"section-gm97fc49 gutentor-col-wrap\"><\/div><\/div>\n<\/div><\/div><\/section>\n<\/div><\/section>\n\n\n\n<section id=\"gmbba35d4\" class=\"wp-block-gutentor-m3 section-gmbba35d4 gutentor-module gutentor-container-cover has-color-bg has-custom-bg\"><div class=\"grid-container\">\n<h2 class=\"has-text-align-center wp-block-heading\">Public Deliverables<\/h2>\n\n\n\n<section id=\"gma79fd5b\" class=\"wp-block-gutentor-m4 section-gma79fd5b gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gm9daed7\" class=\"wp-block-gutentor-m4-col col-gm9daed7 gutentor-single-column  grid-lg-6 grid-md-12 grid-12\"><div id=\"section-gm9daed7\" class=\"section-gm9daed7 gutentor-col-wrap\">\n<h2 class=\"has-text-align-center wp-block-heading\"><a href=\"https:\/\/www.coala-h2020.eu\/wp-content\/uploads\/2021\/10\/COALA_D2.4_COALA_Why-Engine-Theory.pdf\"><img loading=\"lazy\" decoding=\"async\" width=\"40\" height=\"40\" class=\"wp-image-1071\" style=\"width: 40px;\" src=\"https:\/\/www.coala-h2020.eu\/wp-content\/uploads\/2020\/09\/faviconsmall-01.png\" alt=\"\"><\/a><\/h2>\n\n\n\n<h5 class=\"has-text-align-center wp-block-heading\"><a href=\"https:\/\/ncld.ips.biba.uni-bremen.de\/s\/m5NYmx24Nsszr6E\" target=\"_blank\" rel=\"noreferrer noopener\">Why Engine Theory<\/a><\/h5>\n<\/div><\/div>\n\n\n\n<div id=\"col-gm8c3086\" class=\"wp-block-gutentor-m4-col col-gm8c3086 gutentor-single-column  grid-lg-6 grid-md-12 grid-12\"><div id=\"section-gm8c3086\" class=\"section-gm8c3086 gutentor-col-wrap\">\n<h4 class=\"has-text-align-center wp-block-heading\"><img loading=\"lazy\" decoding=\"async\" width=\"40\" height=\"40\" class=\"wp-image-1071\" style=\"width: 40px;\" src=\"https:\/\/www.coala-h2020.eu\/wp-content\/uploads\/2020\/09\/faviconsmall-01.png\" alt=\"\"><\/h4>\n\n\n\n<h6 class=\"has-text-align-center wp-block-heading\"><a href=\"https:\/\/ncld.ips.biba.uni-bremen.de\/s\/X7x6bbNgo2T7Nnx\" target=\"_blank\" rel=\"noreferrer noopener\">Why Engine Prototype<\/a><\/h6>\n<\/div><\/div>\n<\/div><\/div><\/section>\n<\/div><\/section>\n\n\n\n<section id=\"gm9380765\" class=\"wp-block-gutentor-m3 section-gm9380765 gutentor-module gutentor-container-cover has-color-bg has-custom-bg\"><div class=\"grid-container\">\n<h2 class=\"has-text-align-center wp-block-heading\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-black-color\">Scientific Publications<\/mark><\/h2>\n\n\n\n<section id=\"gm9508c15\" class=\"wp-block-gutentor-m4 section-gm9508c15 gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gm3a9f31\" class=\"wp-block-gutentor-m4-col col-gm3a9f31 gutentor-single-column  grid-lg-12 grid-md-12 grid-12\"><div id=\"section-gm3a9f31\" class=\"section-gm3a9f31 gutentor-col-wrap\">\n<p><a href=\"https:\/\/dl.acm.org\/doi\/10.1145\/3531146.3533078\">N. Asher, J. Hunter, &#8216;When learning becomes impossible\u2019, FACCT 2022, ACM Conference on Fairness, Accountability, and Transparency<\/a><\/p>\n<\/div><\/div>\n<\/div><\/div><\/section>\n\n\n\n<section id=\"gm2a9cf38\" class=\"wp-block-gutentor-m4 section-gm2a9cf38 gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gm1a74c5\" class=\"wp-block-gutentor-m4-col col-gm1a74c5 gutentor-single-column  grid-lg-12 grid-md-12 grid-12\"><div id=\"section-gm1a74c5\" class=\"section-gm1a74c5 gutentor-col-wrap\">\n<p><a href=\"https:\/\/www.aaai.org\/AAAI22Papers\/AAAI-12256.IgnatievA.pdf\" data-type=\"URL\" data-id=\"https:\/\/www.aaai.org\/AAAI22Papers\/AAAI-12256.IgnatievA.pdf\">A. Ignativ, Y. Izza, P.J. Stuckey &amp; J. Marques-Silva: Using MaxSAT for Efficient Explanations of Tree Ensembles: Association for the Advancement of Artificial Intelligence (AAAI 2022)<\/a><\/p>\n<\/div><\/div>\n<\/div><\/div><\/section>\n\n\n\n<section id=\"gm8570743\" class=\"wp-block-gutentor-m4 section-gm8570743 gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gm7cebb3\" class=\"wp-block-gutentor-m4-col col-gm7cebb3 gutentor-single-column  grid-lg-12 grid-md-12 grid-12\"><div id=\"section-gm7cebb3\" class=\"section-gm7cebb3 gutentor-col-wrap\">\n<p><a href=\"https:\/\/www.aaai.org\/AAAI22Papers\/SMT-00448-Marques-SilvaJ.pdf\" data-type=\"URL\" data-id=\"https:\/\/www.aaai.org\/AAAI22Papers\/SMT-00448-Marques-SilvaJ.pdf\">Y. Izza, J. Marques-Silva, 2021, \u201cOn Explaining Random Forests with SAT\u201d, International Joint Conference on Artificial Intelligence (IJCAI).<\/a><\/p>\n<\/div><\/div>\n<\/div><\/div><\/section>\n\n\n\n<section id=\"gm4b5d060\" class=\"wp-block-gutentor-m4 section-gm4b5d060 gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gm7c0819\" class=\"wp-block-gutentor-m4-col col-gm7c0819 gutentor-single-column  grid-lg-12 grid-md-12 grid-12\"><div id=\"section-gm7c0819\" class=\"section-gm7c0819 gutentor-col-wrap\">\n<p><a href=\"https:\/\/mdpi-res.com\/d_attachment\/make\/make-04-00014\/article_deploy\/make-04-00014.pdf?version=1648732097\" data-type=\"URL\" data-id=\"https:\/\/mdpi-res.com\/d_attachment\/make\/make-04-00014\/article_deploy\/make-04-00014.pdf?version=1648732097\">N. Asher, L. De Lara, S. Paul, C. RusselL.: (2022) Counterfactual Models for Fair and Adequate Explanations. Machine Learning and Knowledge Extraction<\/a><\/p>\n<\/div><\/div>\n<\/div><\/div><\/section>\n\n\n\n<section id=\"gm1cc6116\" class=\"wp-block-gutentor-m4 section-gm1cc6116 gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gmd8eb55\" class=\"wp-block-gutentor-m4-col col-gmd8eb55 gutentor-single-column  grid-lg-12 grid-md-12 grid-12\"><div id=\"section-gmd8eb55\" class=\"section-gmd8eb55 gutentor-col-wrap\">\n<p><a href=\"https:\/\/proceedings.kr.org\/2021\/34\/kr2021-0034-huang-et-al.pdf\" data-type=\"URL\" data-id=\"https:\/\/proceedings.kr.org\/2021\/34\/kr2021-0034-huang-et-al.pdf\">X. Huang, Y. Izza, A. Ignatiev, J. Marques-Silva : On Efficiently Explaining Graph-Based Classifiers (2021) KR 2021<\/a><\/p>\n<\/div><\/div>\n<\/div><\/div><\/section>\n\n\n\n<section id=\"gm896dd27\" class=\"wp-block-gutentor-m4 section-gm896dd27 gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gm1771e9\" class=\"wp-block-gutentor-m4-col col-gm1771e9 gutentor-single-column  grid-lg-12 grid-md-12 grid-12\"><div id=\"section-gm1771e9\" class=\"section-gm1771e9 gutentor-col-wrap\">\n<p><a href=\"https:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/00031305.2021.1952897?journalCode=utas20\" data-type=\"URL\" data-id=\"https:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/00031305.2021.1952897?journalCode=utas20\">P. Besse, E. del Barrio, P. Gordaliza, J.M. Loubes, L. Risser, : A survey of Bias in Machine Learning through Theorism of Statistical Parity. The American Statistician<\/a><\/p>\n<\/div><\/div>\n<\/div><\/div><\/section>\n\n\n\n<section id=\"gm7a8d843\" class=\"wp-block-gutentor-m4 section-gm7a8d843 gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gmade1d9\" class=\"wp-block-gutentor-m4-col col-gmade1d9 gutentor-single-column  grid-lg-12 grid-md-12 grid-12\"><div id=\"section-gmade1d9\" class=\"section-gmade1d9 gutentor-col-wrap\">\n<p><a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10988-021-09334-x\" data-type=\"URL\" data-id=\"https:\/\/link.springer.com\/article\/10.1007\/s10988-021-09334-x\">N. Asher, J. Hunter &amp; S. Paul. : Bias in Semantic and Discourse Interpretation (2021) Linguistics and Philosophy<\/a><\/p>\n<\/div><\/div>\n<\/div><\/div><\/section>\n\n\n\n<section id=\"gm5bc0ab0\" class=\"wp-block-gutentor-m4 section-gm5bc0ab0 gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gm4011d3\" class=\"wp-block-gutentor-m4-col col-gm4011d3 gutentor-single-column  grid-lg-12 grid-md-12 grid-12\"><div id=\"section-gm4011d3\" class=\"section-gm4011d3 gutentor-col-wrap\">\n<p><a href=\"https:\/\/ieeexplore.ieee.org\/abstract\/document\/9474083?casa_token=zpW-yq40xTUAAAAA:Gl88S7TLmtwkgUT7i2Il2IX_Z-EaIjWop22ZKSBHVtJam7XBsoXH3jpGTyOpyVEWeH1jSB0NWVo\" data-type=\"URL\" data-id=\"https:\/\/ieeexplore.ieee.org\/abstract\/document\/9474083?casa_token=zpW-yq40xTUAAAAA:Gl88S7TLmtwkgUT7i2Il2IX_Z-EaIjWop22ZKSBHVtJam7XBsoXH3jpGTyOpyVEWeH1jSB0NWVo\">G. Cabodi, P.E. Camurati, A. Ignatiev, J. Marques-Silva, M. Palena, &amp; P. Pasini,: Optimizing binary decision diagrams for interpretable machine learning classification (2021) Design, Automation &amp; Test in Europe Conference &amp; Exhibition<\/a><\/p>\n<\/div><\/div>\n<\/div><\/div><\/section>\n\n\n\n<section id=\"gm8d64b7c\" class=\"wp-block-gutentor-m4 section-gm8d64b7c gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gmd6a021\" class=\"wp-block-gutentor-m4-col col-gmd6a021 gutentor-single-column  grid-lg-12 grid-md-12 grid-12\"><div id=\"section-gmd6a021\" class=\"section-gmd6a021 gutentor-col-wrap\">\n<p><a href=\"https:\/\/cd-make.net\/\" data-type=\"URL\" data-id=\"https:\/\/cd-make.net\/\">N. Asher, S. Paul. C. Russell, &#8216;Fair and Adequate Explanations&#8217;, Workshop on Explainable AI, International Cross-Domain Conference for Machine Learning and Knowledge Extraction, 2021<\/a><\/p>\n<\/div><\/div>\n<\/div><\/div><\/section>\n\n\n\n<section id=\"gm0c35689\" class=\"wp-block-gutentor-m4 section-gm0c35689 gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gm04653c\" class=\"wp-block-gutentor-m4-col col-gm04653c gutentor-single-column  grid-lg-12 grid-md-12 grid-12\"><div id=\"section-gm04653c\" class=\"section-gm04653c gutentor-col-wrap\">\n<p><a href=\"https:\/\/arxiv.org\/abs\/2105.06782\" data-type=\"URL\" data-id=\"https:\/\/arxiv.org\/abs\/2105.06782\">A. Ignatiev and J. Marques-Silva: SAT-Based Rigorous Explanations for Decision Lists (2021). International Conference on Theory and Applications of Satisfiability Testing (SAT)<\/a><\/p>\n<\/div><\/div>\n<\/div><\/div><\/section>\n\n\n\n<section id=\"gm69772bf\" class=\"wp-block-gutentor-m4 section-gm69772bf gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gm12921e\" class=\"wp-block-gutentor-m4-col col-gm12921e gutentor-single-column  grid-lg-12 grid-md-12 grid-12\"><div id=\"section-gm12921e\" class=\"section-gm12921e gutentor-col-wrap\">\n<p><a href=\"https:\/\/arxiv.org\/abs\/2105.10278\" data-type=\"URL\" data-id=\"https:\/\/arxiv.org\/abs\/2105.10278\">Y. Izza and J. Marques-Silva: On Explaining Random Forests with SAT (2021). International Joint Conference on Artificial Intelligence (IJCAI)<\/a><\/p>\n<\/div><\/div>\n<\/div><\/div><\/section>\n\n\n\n<section id=\"gm69e4cc6\" class=\"wp-block-gutentor-m4 section-gm69e4cc6 gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gm10bf8e\" class=\"wp-block-gutentor-m4-col col-gm10bf8e gutentor-single-column  grid-lg-12 grid-md-12 grid-12\"><div id=\"section-gm10bf8e\" class=\"section-gm10bf8e gutentor-col-wrap\">\n<p><a href=\"https:\/\/icml.cc\/\" data-type=\"URL\" data-id=\"https:\/\/icml.cc\/\">J. Marques-Silva, T. Gerspacher, M. C. Cooper, A. Ignatiev and N. Narodytska: Explanations for Monotonic Classifiers (2021). International Conference on Machine Learning (ICML)<\/a><\/p>\n<\/div><\/div>\n<\/div><\/div><\/section>\n\n\n\n<section id=\"gm6e9c4f9\" class=\"wp-block-gutentor-m4 section-gm6e9c4f9 gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gma693fc\" class=\"wp-block-gutentor-m4-col col-gma693fc gutentor-single-column  grid-lg-12 grid-md-12 grid-12\"><div id=\"section-gma693fc\" class=\"section-gma693fc gutentor-col-wrap\">\n<p><a href=\"https:\/\/www.aaai.org\/AAAI21Papers\/AAAI-8406.IgnatievA.pdf\" data-type=\"URL\" data-id=\"https:\/\/www.aaai.org\/AAAI21Papers\/AAAI-8406.IgnatievA.pdf\">A. Ignatiev, E.Lam, J. Marques-Silva &amp; P. J. Stuckey: Reasoning-Based Learning of Interpretable ML Models (2021) International Joint Conference on Artificial Intelligence (IJCAI)<\/a><\/p>\n<\/div><\/div>\n<\/div><\/div><\/section>\n\n\n\n<section id=\"gm367af23\" class=\"wp-block-gutentor-m4 section-gm367af23 gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gme1c17d\" class=\"wp-block-gutentor-m4-col col-gme1c17d gutentor-single-column  grid-lg-12 grid-md-12 grid-12\"><div id=\"section-gme1c17d\" class=\"section-gme1c17d gutentor-col-wrap\">\n<p><a href=\"https:\/\/drops.dagstuhl.de\/opus\/volltexte\/2021\/15312\/pdf\/LIPIcs-CP-2021-21.pdf\" data-type=\"URL\" data-id=\"https:\/\/drops.dagstuhl.de\/opus\/volltexte\/2021\/15312\/pdf\/LIPIcs-CP-2021-21.pdf\">M.C. Cooper,: On the tractability of explaining decisions of classifiers.27th International Conference on Principles and Practice of Constraint Programming (CP 2021)<\/a><\/p>\n<\/div><\/div>\n<\/div><\/div><\/section>\n\n\n\n<section id=\"gm37b7f0a\" class=\"wp-block-gutentor-m4 section-gm37b7f0a gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gm291a06\" class=\"wp-block-gutentor-m4-col col-gm291a06 gutentor-single-column  grid-lg-12 grid-md-12 grid-12\"><div id=\"section-gm291a06\" class=\"section-gm291a06 gutentor-col-wrap\">\n<p><a href=\"https:\/\/openreview.net\/pdf?id=kVZ6WBYazFq\" data-type=\"URL\" data-id=\"https:\/\/openreview.net\/pdf?id=kVZ6WBYazFq\">A.A. Shrotri, N. Narodytska, A. Ignatiev, J. Marques-Silva, K.S. Meel &amp; M. Vardi : Constraint-Driven Explanations of Black-Box ML Models (2020) ICLR<\/a><\/p>\n<\/div><\/div>\n<\/div><\/div><\/section>\n\n\n\n<section id=\"gm5452dc1\" class=\"wp-block-gutentor-m4 section-gm5452dc1 gutentor-module gutentor-advanced-columns\"><div class=\"grid-container\"><div class=\"grid-row\">\n<div id=\"col-gm57f5af\" class=\"wp-block-gutentor-m4-col col-gm57f5af gutentor-single-column  grid-lg-12 grid-md-12 grid-12\"><div id=\"section-gm57f5af\" class=\"section-gm57f5af gutentor-col-wrap\">\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2005.11720.pdf\" data-type=\"URL\" data-id=\"https:\/\/arxiv.org\/pdf\/2005.11720.pdf\">T.L. Gouic, J.M. Loubes, P. Rigollet, : Projection to Fairness in Statistical Learning (2020)<\/a><\/p>\n<\/div><\/div>\n<\/div><\/div><\/section>\n<\/div><\/section>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"_mi_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-3982","page","type-page","status-publish","hentry"],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false,"neve-blog":false},"uagb_author_info":{"display_name":"Indah Lengkong","author_link":"https:\/\/www.coala-h2020.eu\/index.php\/author\/len\/"},"uagb_comment_info":0,"uagb_excerpt":null,"_links":{"self":[{"href":"https:\/\/www.coala-h2020.eu\/index.php\/wp-json\/wp\/v2\/pages\/3982","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.coala-h2020.eu\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.coala-h2020.eu\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.coala-h2020.eu\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.coala-h2020.eu\/index.php\/wp-json\/wp\/v2\/comments?post=3982"}],"version-history":[{"count":19,"href":"https:\/\/www.coala-h2020.eu\/index.php\/wp-json\/wp\/v2\/pages\/3982\/revisions"}],"predecessor-version":[{"id":5237,"href":"https:\/\/www.coala-h2020.eu\/index.php\/wp-json\/wp\/v2\/pages\/3982\/revisions\/5237"}],"wp:attachment":[{"href":"https:\/\/www.coala-h2020.eu\/index.php\/wp-json\/wp\/v2\/media?parent=3982"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}