Return-Path: Delivered-To: cadet@irif.fr Received: from mailhub.math.univ-paris-diderot.fr ([81.194.30.253]) by mailhost.irif.fr (Dovecot) with LMTP id ceBMDYomOmE9NgAAP9ZUWg for ; Thu, 09 Sep 2021 17:21:46 +0200 Received: from mailhub.math.univ-paris-diderot.fr (localhost [127.0.0.1]) by mailhub.math.univ-paris-diderot.fr (Postfix) with ESMTP id EC24AC8E81 for ; Thu, 9 Sep 2021 17:21:50 +0200 (CEST) X-Virus-Scanned: amavisd-new at math.univ-paris-diderot.fr X-Spam-Flag: NO X-Spam-Score: -2.897 X-Spam-Level: X-Spam-Status: No, score=-2.897 tagged_above=-10000 required=5 tests=[ALL_TRUSTED=-1, BAYES_00=-1.9, HTML_MESSAGE=0.001, MIME_QP_LONG_LINE=0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mailhub.math.univ-paris-diderot.fr ([127.0.0.1]) by mailhub.math.univ-paris-diderot.fr (mailhub.math.univ-paris-diderot.fr [127.0.0.1]) (amavisd-new, port 10023) with ESMTP id APPRGcDVGqJw for ; Thu, 9 Sep 2021 17:21:48 +0200 (CEST) Received: from smtpclient.apple (pop.92-184-116-249.mobile.abo.orange.fr [92.184.116.249]) (Authenticated sender: magniez) by mailhub.math.univ-paris-diderot.fr (Postfix) with ESMTPSA id 3698CC8E7C for ; Thu, 9 Sep 2021 17:21:48 +0200 (CEST) Content-Type: multipart/alternative; boundary=Apple-Mail-4D4D80D0-6496-4BDB-B3AE-06F41B338AF1 Content-Transfer-Encoding: 7bit From: Frederic Magniez Mime-Version: 1.0 (1.0) Date: Thu, 9 Sep 2021 17:21:42 +0200 Subject: Fwd: [diip-perimeter] diiP Distinguished Lecture, October 6, 4pm Message-Id: <31606F04-2873-4A82-9693-E1DC5BD959A8@irif.fr> References: <941c6cad5e8c41088ea43c065e598f5b@parisdescartes.fr> To: lettre@irif.fr X-Mailer: iPhone Mail (18G82) --Apple-Mail-4D4D80D0-6496-4BDB-B3AE-06F41B338AF1 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: quoted-printable Begin forwarded message: > From: Ana=C3=AFs De Muret De Labouret > Date: 9 September 2021 at 16:31:48 CEST > Cc: diip@math-info.univ-paris5.fr > Subject: [diip-perimeter] diiP Distinguished Lecture, October 6, 4pm > Reply-To: Ana=C3=AFs De Muret De Labouret >=20 > =EF=BB=BF > diiP Distinguished Lecture: > Building Data Equity Systems >=20 > Who: Prof. Julia Stoyanovich (New York University) > When: 6 October 2021, 4pm (Paris time) > Where: online: https://u-paris.zoom.us/j/82710527557?pwd=3DWUgvU0tqVGVreVU= zUVhpQW5zNmxJQT09 >=20 > title: Building Data Equity Systems >=20 > abstract: Equity as a social concept =E2=80=94 treating people differently= depending on their endowments and needs to provide equality of outcome rath= er than equality of treatment =E2=80=94 lends a unifying vision for ongoing w= ork to operationalize ethical considerations across technology, law, and soc= iety. In my talk I will present a vision for designing, developing, deployin= g, and overseeing data-intensive systems that consider equity as an essentia= l requirement. I will discuss ongoing technical work in scope of the "Data, R= esponsibly" project, and will place this work into the broader context of po= licy, education, and public outreach activities. >=20 > short bio: Julia Stoyanovich is an Institute Associate Professor of Comput= er Science & Engineering at the Tandon School of Engineering, Associate Prof= essor of Data Science at the Center for Data Science, and Director of the Ce= nter for Responsible AI at New York University (NYU). Her research focuses o= n responsible data management and analysis: on operationalizing fairness, di= versity, transparency, and data protection in all stages of the data science= lifecycle. She established the "Data, Responsibly" consortium and served on= the New York City Automated Decision Systems Task Force, by appointment fro= m Mayor de Blasio. Julia developed and has been teaching courses on Respons= ible Data Science at NYU, and is a co-creator of an award-winning comic book= series on this topic. In addition to data ethics, Julia works on the manage= ment and analysis of preference and voting data, and on querying large evolv= ing graphs. She holds M.S. and Ph.D. degrees in Computer Science from Columb= ia University, and a B.S. in Computer Science and in Mathematics & Statistic= s from the University of Massachusetts at Amherst. She is a recipient of an N= SF CAREER award and a Senior Member of the ACM. >=20 > logistics: > Pour recevoir les annonces du diiP, vous pouvez vous inscrire =C3=A0 notre= mailing list en envoyant un mail =C3=A0 diip@math-info.univ-paris5.fr avec "= follow diiP" dans l'objet du mail. >=20 > Bien =C3=A0 vous, > Ana=C3=AFs pour le diiP >=20 >=20 >=20 > Ana=C3=AFs de Muret >=20 > Manager de projets, Cit=C3=A9 du Genre et Data Intelligence Institute of P= aris >=20 > Universit=C3=A9 de Paris >=20 >=20 >=20 > 5, rue Thomas Mann >=20 > B=C3=A2timent C des Grands Moulins - Bureau 879C >=20 > 75013 Paris >=20 >=20 >=20 > 01 57 27 52 36=20 --Apple-Mail-4D4D80D0-6496-4BDB-B3AE-06F41B338AF1 Content-Type: text/html; charset=utf-8 Content-Transfer-Encoding: quoted-printable



Begin forwarde= d message:

From:<= /b> Ana=C3=AFs De Muret De Labouret <anais.de-muret-de-labouret@parisdesc= artes.fr>
Date: 9 September 2021 at 16:31:48 CEST
Cc:= diip@math-info.univ-paris5.fr
Subject: [diip-perimeter] diiP D= istinguished Lecture, October 6, 4pm
Reply-To: Ana=C3=AFs De M= uret De Labouret <anais.de-muret-de-labouret@parisdescartes.fr>
=EF=BB=BF

diiP Distinguished Lecture:
= Building Data Equity Systems

= Who: Prof. Julia Stoyanovich (New York University)
= When: 6 October 2021, 4pm (Paris time)

= title: Buil= ding Data Equity Systems

= abstract: <= /span>Equity= as a social concept =E2=80=94 treating people differently depending on thei= r endowments and needs to provide equality of outcome rather than equality o= f treatment =E2=80=94 lends a unifying vision for ongoing work to operationa= lize ethical considerations across technology, law, and society. In my talk I will present a vision for designing, develop= ing, deploying, and overseeing data-intensive systems that consider equity a= s an essential requirement. I will discuss ongoing technical work in scope o= f the "Data, Responsibly" project, and will place this work into the broader context of policy, education, and= public outreach activities.

= short bio: = Julia= Stoyanovich is an Institute Associate Professor of Computer Science & E= ngineering at the Tandon School of Engineering, Associate Professor of Data S= cience at the Center for Data Science, and Director of the Center for Respon= sible AI at New York University (NYU). Her research focuses on responsible data management and analysis: on= operationalizing fairness, diversity, transparency, and data protection in a= ll stages of the data science lifecycle. She established the "Data, Responsi= bly" consortium and served on the New York City Automated Decision Systems Task Force, by appointment fro= m Mayor de Blasio.  Julia developed and has been teaching courses on Re= sponsible Data Science at NYU, and is a co-creator of an award-winning comic= book series on this topic. In addition to data ethics, Julia works on the management and analysis of preference an= d voting data, and on querying large evolving graphs. She holds M.S. and Ph.= D. degrees in Computer Science from Columbia University, and a B.S. in Compu= ter Science and in Mathematics & Statistics from the University of Massachusetts at Amherst. She is a r= ecipient of an NSF CAREER award and a Senior Member of the ACM.

= logistics:
= Pour recevoir les annonces du diiP, vous pouvez vous inscrire =C3=A0 notre mailing list en envoyant un mail =C3=A0 <= /font>diip@math-info.univ-paris5.fr avec "follow diiP" dans l'objet du mail.

= Bien =C3=A0 vous,
= Ana=C3=AFs pour le diiP


Ana=C3=AFs de Muret

Manager de projets, Cit=C3=A9= du Genre et Dat= a Intelligence Institute of Paris

Universit=C3=A9 de Paris


5, rue Thomas Mann

B=C3=A2timent C des Grands Moulins - Bure= au 879C

75013 Paris


01 57 27 52 36 

= --Apple-Mail-4D4D80D0-6496-4BDB-B3AE-06F41B338AF1--