Transforming the Digital Forschung und Kollaboration in algorithmischen Räumen

20th of March 2023  17 Uhr/ 5 pm   HS 3/  Hörsaal 3

Antrittsvorlesung von Frau Univ.-Prof. Dr. Katharina Kinder-Kurlanda

 

 

Miriam Fahimi im Standard-Interview über Gefahren von Künstlicher Intelligenz

Miriam Fahimi (vom UZ D!ARC), Doktorandin im Projekt NoBIAS – AI without Bias (NoBIAS ), erzählt im Standard-Interview von den Gefahren von Künstlicher Intelligenz. Ihre Einsichten sind Teil ihrer laufenden Doktorarbeit zu Diskriminierung und Fairness in KI-Systemen (Visitenkarte ) im Fach Wissenschafts- und Technikforschung.

“Die Welt und unsere Gesellschaft sind von Ungleichheit geprägt, wie sollen da die Daten für eine künstliche Intelligenz völlig objektiv sein. Das ist utopisch”, erklärt Miriam Fahimi, die am Digital Age Research Center der Universität Klagenfurt zum Thema ‚faire Algorithmen‘ forscht. (Auszug aus dem Interview)

Den gesamten Artikel gibt es hier zu lesen: Zeitalter der KI 

Wir gratulieren Miriam Fahimi zu Ihrem Interview und freuen uns über weitere Erkenntnisse aus ihrer laufenden Arbeit.

D!ARC Lectures: Cryptographic Engineering Research: Navigating Responsibility Univ.-Prof.Dr. Elisabeth Oswald

12th January 2023    17:30 Uhr/ 5.30pm     Hörsaal 2/ HS 2

 

Cryptographic Engineering Research: Navigating Responsibility

Univ.-Prof. Dr. Elisabeth Oswald

 

Abstract

This talk is about challenges that arise when engineering systems in such a way that as little information as possible is leaked about cryptographic secrets. Over the years a range of mathematical and engineering techniques have been researched (and in part deployed) to account for, and mitigate, information leakage. Research in this area requires to carefully consider how developed techniques (that describe and analyse information leakage) not only help developers and evaluators, but if and how these can play into the hands of potential adversaries.

 

CV

Elisabeth Oswald completed her PhD in Technical Mathematics at the Technical University in Graz. Thereafter she took up a lecturing position in the Computer Science Department at the University of Bristol, where she established a research group in the area of applied cryptography, with an emphasis on analysing side channels. Eventually she was promoted to the first female chair in the Bristol Computer Science department. Her scientific accomplishments were honoured by an EPSRC Leadership Fellowship, an ERC Consolidator grant, and a number of best paper awards. She serves as associate editor of the two most influential journals in the are of cryptography, and participates regularly in leading functions for research funding institutions. Since 2019 she holds a chair in Cybersecurity research at the University of Klagenfurt.

 

For those who can only participate in this D!ARC Lecture online, see added the corresponding link for the live stream:

Für jene, denen nur eine online-Teilnahme an dieser D!ARC Lecture möglich ist, finden Sie anbei den entsprechenden Link für den Livestream:

https://classroom.aau.at/b/sag-893-nqz-qhq

D!ARC Lectures: Erkennung von Beleidigungen mithilfe computerlinguistischer Verfahren_Prof. Dr. Michael Wiegand

 

14th Dezember 2022    17 Uhr/ 5pm     Hörsaal 2/ HS 2

Erkennung von Beleidigungen mithilfe computerlinguistischer Verfahren

Prof. Dr. Michael Wiegand

 

Abstract

In this presentation, a brief overview of the state of the art in abusive language detection will be given. One key difficulty is to build appropriate gold standards for the task which serve as
a basis for machine learning methods. In that context, the phenomenon of “spurious correlations” will be illustrated. In terms of classifiers, a lexicon-based approach will be outlined. Unlike
previous off-the-shelf methods that are usually treated as a black box, lexicon-based approaches are more explainable, less susceptible to overfitting and more stable across different domains. Predictive word lists can be compiled in a resource-intensive way combining various sources of linguistic information. As an alternative, a data-driven less resource-intensive induction method relying on emojis with an abusive connotation will also be presented.

(Please note that the presentation will be held
in German.)

CV

Michael Wiegand obtained his PhD at Saarland University in 2011. Until 2018, he had been a postdoctoral researcher at the Department for Spoken Language Systems at Saarland University. In 2019, he was research group leader in the Leibniz ScienceCampus Empirical Linguistics and Computational Language Modeling (Leibniz Institute for the German Language, Mannheim/Heidelberg University). Since 2020, he has been a professor for Computational Linguistics in the Digital Age Research Center (D!ARC) at University of Klagenfurt.