=Paper=
{{Paper
|id=Vol-2796/preface
|storemode=property
|title=Preface - Proceedings of the 1st International Workshop on
Explainable and Interpretable Machine Learning (XI-ML)
|pdfUrl=https://ceur-ws.org/Vol-2796/preface_xi-ml-2020.pdf
|volume=Vol-2796
|authors=Martin Atzmueller,Tomas Kliegr,Ute Schmid
|dblpUrl=https://dblp.org/rec/conf/ki/X20
}}
==Preface - Proceedings of the 1st International Workshop on
Explainable and Interpretable Machine Learning (XI-ML)==
https://ceur-ws.org/Vol-2796/preface_xi-ml-2020.pdf
Proceeding of he 1 In erna ional Work hop on
E plainable and In erpre able Machine Learning (XI-ML)
(h p:// .c lab.cc/ i-ml-2020/)
- Preface -
Recen l , cien ific di co r e in ar ificial in elligence and da a cience ha foc ed on
e plainable AI (XAI) i h re pec o algori hmic ran parenc , in erpre abili , acco n abili
and finall e plainabili of algori hmic model and deci ion . In machine learning,
approache can be cla ified a hi e-bo and black-bo . Whi e-bo approache , ch a r le
learner and ind c i e programming, re l in e plici model hich are inheren l
in erpre able (R din, 2019). On he o her hand, black-bo approache , ch a (deep) ne ral
ne ork , re l in opaq e model . For hi econd pe of model , o er he la ear ,
differen approache for e -po e plana ion genera ion ha e been propo ed.
In hi ork hop, e an o bring oge her re earch from in erpre able and e plana or
machine learning. In erpre able ML can profi from recen l propo ed e plana ion genera ion
echniq e o make comple learned model more comprehen ible, e peciall o end- er
(M ggle on, Schmid e al., 2018; F rnkran , Kliegr, Pa lheim, 2020; Lonjarre e al., 2020).
In par ic lar, in erpre able learning can be in egra ed in o he con r c ion of comple
model , e.g., for g iding heir con r c ion (A m eller e al., 2017), a ell a o refine he
re pec i e model (Weidner, A m eller, Seipel, 2019). F r hermore, i can pro ide rich
r le-ba ed echniq e o genera e in erpre able rroga e model for black-bo learner
(Schmid, 2018). S ch rroga e model can be global model genera ed b r le-e rac ion
mechani m (Haile ila ie, 2016) or local model hich allo richer local e plana ion han
imple linear r le a , for in ance, propo ed b LIME (Rabold e al., 2019). Al o, a fron ier
direc ion i in e iga ing p chological phenomena ha can affec he nder anding of
machine learning model , ch a cogni i e bia e and con er a ional ma im (Kliegr,
Bahnik, F rnkran , 2018). Thi in erdi ciplinar in pira ion, ch a debia ing echniq e
long died b p chologi , ill hopef ll con rib e o a be er comprehen ibili of he
re l of model crea ed b he ne genera ion of machine learning algori hm .
XI-ML (E plainable and In erpre able Machine Learning) aim a bringing oge her re earch
from in erpre able and e plainable machine learning. Hopef ll , in egra ing bo h area ,
allo ne per pec i e on q e ion on appropria e learning formali m , in erpre a ion and
e plana ion echniq e , heir me ric , a ell a he re pec i e a e men op ion ari e.
Copyright (C) 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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 of
r le-ba ed model . Mac L a , 109(4), 853-898.
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.
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 akia
● 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