Preface - Proceedings of the 1st International Workshop on Explainable and Interpretable Machine Learning (XI-ML)
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(2) The fir edi ion of he XI-ML (E plainable and In erpre able Machine Learning) ork hop a held on Sep ember 21, 2020 a he 43rd German Conference on Ar ificial In elligence, Bamberg, German . The ork hop a de o ed o he di c ion of he opic men ioned abo e. I aimed o pro ide an in erdi ciplinar for m o in e iga e f ndamen al i e in e plainable and in erpre able machine learning a ell a o di c recen ad ance , rend , and challenge in he e area . From 8 bmi ion (6 f ll and o hor paper ), 5 f ll paper and he o hor paper ere accep ed for pre en a ion in a comprehen i e re ie proce . The remaining par of he ol me pre en re i ed er ion of paper ha ere di c ed d ring he ork hop. Bo r m e al. di c in heir paper abo ho o e plain m l i aria e ime erie foreca ing, in an applica ion o predic ing he S edi h GDP. Ne , M cha e al. pre en a po i ion paper on ho o con r c par icipa or de ign pace for he con e of e plainable AI in erface in e per domain . Af er ha , Volker di c ed ho he applica ion of he TED (Teaching E plana ion for Deci ion ) e plainable AI frame ork and he impac of cla (im-)balance. Flei , B ck and Thalmann pre en a hor paper on empirical re l in he con e of recr i ing - abo e plainabili and he in en ion o e AI-ba ed con er a ional agen . Po ka addre e fo nda ional i e o ard ol ing cla ifica ion problem i h q an i a i e ab rac arg men a ion. Mollenha er and A m eller pre en an approach for eq en ial e cep ional pa ern di co er ing pa ern-gro h (SEPP) - a he ba i of an e en ible frame ork for in erpre able machine learning on eq en ial da a. S n, Chakrabor i and Noble di c re l of a compara i e d of e plainer mod le in he con e of a oma ed kin le ion cla ifica ion. Finall , Marcin P. Joachimiak (En ironmen al Genomic and S em Biolog Di i ion, La rence Berkele Labora or ) kindl agreed o pre en a ke no e en i led Ho o each a comp er o learn abo microbe i h KG-COVID-19 . Thi alk in rod ced a ne re o rce ha amalgama e SARS-CoV-2 rela ed biological kno ledge from m l iple peciali ed kno ledge graph and on ologie . Wi h o er 10 million node , i i one of he large (if no he large ) re o rce of hi kind. In hi alk, Dr. Joachimiak demon ra ed he ili of hi re o rce for machine learning, empha i ing he need for e plainable echniq e .. Reference A m eller, M., Ha a , N., Schmid , A., & Kl pper, B. (2017). E plana ion-a are fea re elec ion ing mbolic ime erie ab rac ion: approache and e perience in a pe ro-chemical prod c ion con e . In IEEE I a a C c I d a I a c (INDIN) (pp. 799-804). IEEE, Bo on, MA, USA F rnkran , J., Kliegr, T., & Pa lheim, H. (2020). On cogni i e preference and he pla ibili r le-ba ed model . Mac L a , 109(4), 853-898.. of. Haile ila ie, T. (2016). R le e rac ion algori hm for deep ne ral ne ork : A re ie . a X a X :1610.05267..
(3) Kliegr, Tom , p n Bahn k, and Johanne F rnkran . "A re ie of po ible effec of cogni i e bia e on in erpre a ion of r le-ba ed machine learning model ." arXi preprin arXi :1804.02969 (2018). Lonjarre , C., Robarde , C., Plan e i , M., A b r in, R., & A m eller, M. (2020). Wh Sho ld I Tr Thi I em? E plaining he Recommenda ion of an Model. In IEEE I a a C c Da a Sc c a d A a c . IEEE, Bo on, MA, USA M ggle on, S. H., Schmid, U., Zeller, C., Tamaddoni-Ne had, A., & Be old, T. (2018). Ul ra-S rong Machine Learning: comprehen ibili of program learned i h ILP. Mac L a , 107(7), 1119-1140. Rabold, J., Deininger, H., Sieber , M., & Schmid, U. (2019). Enriching i al i h erbal e plana ion for rela ional concep combining LIME i h Aleph. In J E a C c Mac L a a dK d D c Da aba (pp. 180-192). Springer, Cham. R din, C. (2019). S op e plaining black bo machine learning model for high ake deci ion and e in erpre able model in ead. Na Mac I c , 1(5), 206-215. Schmid, U. (2018). Ind c i e Programming a Approach o Comprehen ible Machine Learning. In DKB/KIK@ KI (pp. 4-12). Weidner, D., A m eller, M., & Seipel, D. (2019). Finding Ma imal Non-red ndan A ocia ion R le in Tenni Da a. In D c a a P a a dK d Ma a (pp. 59-78). Springer, Cham.. Edi or ● ● ●. Mar in A m eller, O nabr ck Uni er i , German Tom Kliegr, Uni er i of Economic Prag e, C ech Rep blic U e Schmid, Uni er i of Bamberg, German. Program Commi ee of XI-ML 2020 ● ● ● ● ● ● ● ● ● ● ● ● ● ●. Kla -Die er Al hoff, Uni er i of Hilde heim Maria Bieliko a, Kem ele I i e f I ellige Tech l gie , Sl Henrik Bo r m, KTH Ro al In i e of Technolog , S eden Ami Dh randhar, IBM TJ Wa on Re earch Cen er, USA Johanne F rnkran , Johanne Kepler Uni er i , Lin Mar in Holena, C ech Academ of Science E ke H llermeier, Uni er i of Paderborn Kri ian Ker ing, TU Darm ad , German Gr egor Nalepa, Jagellonian Uni er i , Poland M kola Pecheni k i, TU Eindho en Marc Plan e i , Uni er i L on Eric Po ma, Tilb rg Uni er i Celine Ro eirol, Uni er i Sorbonne Pari Nord S efano Te o, KU Le en, Belgi m. akia.
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