In Styria a new initiative is emerging to develop efficient and independent test methods and testing technologies for AI systems. This involves the Know-Center, a leading European research center for Data-driven business and AI, the SGS Group, the world’s leading provider of testing, verification and certification and the Institute for Information Processing and Communication Technology of Graz University of Technology, one of the leading cybersecurity research teams. Ethical and legal aspects are introduced via the Business Analytics and Data Science Center at University of Graz and Austria’s Center for Secure Information Technology (A-SIT) accompanies the activities as a neutral observer.
“The potential of AI in Europe will only be exploited if the trustworthiness of data handling as well as fair, reliable and secure algorithms can be demonstrated. With a 360° perspective, we want to ensure that AI applications function in a technically compliant, reliable and unbiased manner. The focus is on all areas that are essential for the high quality and trustworthiness of AI: data, algorithms, cybersecurity, processes, ethics and law”, explains Stefanie Lindstaedt, CEO of the Know-Center.
Barbara Eibinger-Miedl, Provincial Councilor of Economic Affairs and Research welcomes the initiative: “Trustworthy AI systems and a high level of data protection are essential to reduce barriers when it comes to the use of AI applications. We are pleased the global corporation SGS is relying on Styrian know-how in this regard, which is a confirmation of the excellent work provided by the stakeholders involved. In Styria we have succeeded in building up comprehensive competencies in this field and have taken on a global pioneering role by numerous research projects and digitization initiatives.”
Currently, AI is one of the fastest growing topics. Most AI systems are data-driven, that is, they learn about desired behaviors from large amounts of data. This cutting-edge technology enables extraordinary innovation but, if not used properly, can have unintentional, negative effects, for example biases in human resource processes or unsafe recommendations by AI in the critical healthcare sector.
“A cornerstone of trust in AI is compliance with standards and regulations, demonstrated through conformity assessments, carried out by accredited third parties like SGS. In our partnership, we will develop new multi-disciplinary tools and techniques to enable these assessments, to include cybersecurity, safety and ethics as examples,” explains Siddi Wouters, Senior Vice President of Digital & Innovation at SGS, “which brings value to customers across the world.”
Despite the enormous technological potential, the use of AI applications also involves uncertainties and risks. There are a variety of ways to attack AI systems. A major challenge in the evaluation of AI systems is therefore cybercrime. For example, a driverless vehicle could make fatal decisions if data processed by the AI system used in the vehicle is wrongly programmed by criminals.
“At this point conventional static testing is not sufficient. Research in terms of fundamentally new safety engineering concepts is needed to obtain continuous attestation of AI system’s resilience against cyberattacks. TU Graz introduces its expertise to the strategic partnership. For us, the initiative represents the logical deepening of an already successfully existing cooperation in the field of computer science, software engineering and cybersecurity with SGS, Know-Center and the University of Graz. In addition, it will benefit university research and teaching, which the new and current content will incorporate,” explains Harald Kainz, Rector of Graz University of Technology.
Despite increasing AI applications across all sectors of industry in recent years, companies are still feeling uncertain when it comes to data protection and legal requirements. The regulation on AI intended by the European Union could induce additional overburdening for companies and reduce or even prevent the adding value of AI. Overall, missing auditing certificates are one major barrier for AI adoption and reducing business potentials.
“Missing auditing procedures are one of the major adoption barriers for AI. It is not only a legal or compliance prerequisite it also leads to confidence-building and positively influences societal acceptance. Our studies in recruiting, e.g. show that people who are feeling discriminated, are most likely to prefer the assessment of their qualifications by AI in contrast to human recruiters. It particularly applies if carried out by certified AI applications with an explainability component,” Stefan Thalmann, Head of Business Analytics and Data Science Center at University Graz, states.
Herbert Leitold, Secretary General A-SIT also emphasizes: “The complex challenges of AI certification will be easier to accomplish by bundling a variety of expertise. Austria is on the right track by presenting providers and users of AI application with better orientation and certainty in regards to the quality of applications.”
Energie Steiermark AG, Leftshift One, NXP and Redwave will participate with use cases. The initiative is open to further partners from industry and science who are interested in working together on AI testing methods. Know-Center’s extensive international partner network also ensures cutting edge research, testing tools and the continuous further development of methods.
More information about the initiative: https://sichere-ki.at/Video press conference
Know-Center is one of the leading European research centers for Data-Driven business and AI. Since 2001, well-known companies have been supported by using data as a success factor for their business. As an integral part of the European research landscape, the center successfully handles numerous projects and contract research at EU- and national level. The K1 Competence Center, which is funded under COMET, is the leading training center for data scientists in Austria and provides a range of AI training and consulting services to companies. Know Center‘s majority shareholder is Graz University of Technology, a major contributor to domestic AI research, whose institutes handle numerous projects together with Know-Center. In 2020, Know-Center was the only Austrian center to receive the BDVA iSpace Gold Award, which has only been awarded nine times throughout the entire EU so far.
The SGS Group is the world’s leading inspection, testing, verification and certification company. It is recognized as the global benchmark for quality and integrity. With more than 93,000 employees, SGS operates a network of more than 2,600 offices and laboratories worldwide. The SGS Group is based in Geneva, Switzerland.
About TU Graz
Cybersecurity has been a research focus at TU Graz for many years. The University hosts one of the world’s leading research institutions in this field: the Institute for Applied Information Processing and Communication Technologies (IAIK). IAIK researchers work on cryptology and privacy technology, system security and formal methods. They develop tools that make mobile devices more secure and ensure the security of electronic signatures and electronic devices. Teams at IAIK have been continuously involved in the discovery of processor vulnerabilities, often leading the way, e.g., during the well-known “Meltdown” and “Spectre” attacks. In the field of cryptography, they successfully participate in major worldwide competitions and encryption processes, setting new cryptography standards, e.g., with the CAESAR cipher, AES encryption and Post-Quantum Cryptography. The expertise is also reflected by the scientific work – to date, more than 1000 publications have been published by the institute.
About Business Analytics and Data Science Center
BANDAS (Business Analytics and Data Science) Center focuses on data-based technologies that are applied to very large, heterogeneous and volatile data volumes (Big Data). The center’s focus is divided into two main areas: Business Analytics and Data Science.
The Center for Secure Information Technology – Austria A-SIT acts as advisor and supporter to the public sector on information security. Core responsibilities are electronic signature and identity, e-government, payment systems, cryptography and based on their topicality and increasing importance, mobile technologies and cloud computing. In addition their expertise is empowered via international projects and by the inclusion of science. A-SIT is a designated certification center as well as an accredited conformity assessment center based on the European Union‘s eIDAS regulation.
At the beginning of their education students often find it challenging when reading and understanding a text simultaneously, since they are mostly occupied with reading. Reading comprehension doesn’t develope further until they are in higher grades.
The Heli-D ( (Health Literacy & Diversity) research project aims to promote health literacy in students while training meaningful reading and understanding using artificial intelligence (AI). The project was implemented by Karl-Franzens-Universität Graz in cooperation with MedUni Graz and Know-Center.
The application’s training program includes five modules covering different health topics such as injuries. In the course of the training, students solve different tasks and puzzles. Texts in four different levels of difficulty are available for each module. Those differ, e.g., in word count and linguistic complexity. Prior to students starting the training program, a test is used to determine how well they perform in reading and understanding texts
The application’s content was determined by Karl Franzens University of Graz during workshops with students from Styria and mapped out by clinical psychologists, pedagogues and medical students subsequently. Surprisingly, students 12 to 13 years of age were mostly enthusiastic about topics such as immune systems, cancer, injuries or antibiotic resistance. Overall 800 secondary level I students (2nd and 3rd grade of ‚AHS‘ and ‚NMS‘) tested the application in practice. The researchers evaluated the anonymized data collected throughout the process with regard to various aspects: Were the tasks too difficult or too easy? Should the student be assigned to a higher level?
“With the Heli-D research project our research teams developed an adaptive algorithm, which divides students into different performance levels and can adapt based on learning progress throughout the training program,” Stefanie Lindstaedt, Managing Direcotr of Know-Center states, and explains further: “The created e-learning environment enables students to be introduced to a certain topic playfully. Although the training sessions adapt individually to the learning individiuals, it still leads to a social group learning effect, which in turn, increases the students’ ability to work within a team. Overall, the results showed that students performed well across all levels when they invested more time in solving the tasks.”
The application represents a good addition for schools in order to raise students’ awareness of health issues through play. After the project’s completion, Karl Franzens University of Graz plans to make the application available to all schools throughout Austria.
Know-Center is one of Europe’s leading research centers for Data-Driven Business and AI. Since 2001, well-known companies have been supported by using data as a success factor for their business. For data analysis, Know-Center relies on established Big Data as well as High Performance Computing (HPC) infrastructures. The center most successfully handles numerous projects and contract research on a European and national scale being an integral part of the European research landscape.The K1 Competence Center, which is funded within the framework of COMET, also represents the leading training center for data scientists in Austria and furthermore offers a range of AI training and consulting services to companies. The majority shareholder of Know-Center is Graz University of Technology, a major contributor to domestic AI research, whose institutes handle numerous projects together with Know-Center. In 2020, Know-Center was the only Austrian center to receive the European Union’s iSpace Gold Award, which has only been awarded nine times throughout the entire European Union. Further Information: www.know-center.at
Conductor plates are known as the nervous system of any electronic device. No matter if it is about mobile end devices or the automotive, industrial and medical sectors. The areas of application are diverse, as is their production. A single conductor plate requires around 150 complex steps until it is ready for use.
“At AT&S high quality is a given when it comes to our products. Conductor plate images are taken automatically during the process of manufacturing and subsequently pass through image analysis software. Sometimes conductor plates are falsely identified “defective”. Regrettably for us, without any comprehensible reasons. This may result in additional loss of time and resources.” Ulrike Klein states, Head of Data & Analytics at AT&S.
Know Center developed an AI-algorithm for AT&S, its project partner and leading manufacturer of high-end conductor plates. This algorithm not only identifies images of conductor plates properly, but in addition provides an explanation as to why a conductor plate has been identified defective or intact. AT&S consequently owns a transparent AI system, which will deliver comprehensible and explainable results in the foreseeable future once intensively tested.
“Our goal was to precisely detect faulty PCBs and make the results traceable. We are pleased we succeeded by implementing the project and our results also agreed with the propositions made by AT&S’s technicians,” Dr. Andreas Trügler states, Head of Research, module DDAI, at Know-Center, and further explains: “First, our algorithm had to understand which PCBs were faulty and why. In order to achieve this, a neural network was trained and supplied with image data of correct and faulty conductor plates by our team. By using methods from the Explainable AI research field, we were able to provide additional explanation in terms of why and where a PCB was identified as faulty.”
Industry 4.0 or so-called “Smart Factory” no longer is a dream. The insert of smart machines and applications provides companies with significant advantages in times of increasingly competitive pressure.
Now more than ever AI is the driving technology for innovative products and services in the digital age. This fact primarily has become noticeable for the manufacturing industry in the field of automation. Stefanie Lindstaedt, Managing Director at Know-Center explains: “AI enables quality assurance at the highest level and thereby helps organisations save costs and resources. However, quality gaps still exist when it comes to automated image recognition and analysis, which keeps advancing on various sectors of the industry. Building trust in these technologies proved to be another obstacle on the path to firmly embedding AI within organizations. We are very pleased this project has succeeded by overcoming both.”
AT&S Austria Technologie & Systemtechnik AG, is one of Know Center’s industrial partners joint by the module DDAI which runs under the auspices of COMET. The module, which is led by Know-Center and funded by FFG, aims to develope safe, verifiable and explainable AI which assures privacy protection simultaneously. It will significantly contribute to acceptance and trust in AI. In the future more projects leading to “trustworthy AI” will be promoted collectively by AT&S and other industry partners which are part of the module.
Know-Center is one of the leading European research centers for Data-driven business and AI. It has been supporting well-known companies by using data as a factor of success for their businesses since 2001. Know-Center relies on established Big Data as well as High Performance Computing (HPC) infrastructure for data analysis. Known as constant part of the European research landscape, the center very successfully processed numerous projects and contract research on an European as well as domestic level. The K1 Competence Center, which is funded within the framework of COMET, is the leading training center for data scientists in Austria and in addition offers a range of Al training and consulting services to companies. The majority shareholder is Graz University of Technology, a key supporter of domestic AI research, whose institutes process numerous projects together with Know-Center. In 2020, Know-Center was the only Austrian center to receive the EU’s iSpace Gold Award, which only has been awarded nine times in the entire EU so far.
Further Information: www.know-center.at
AT&S is one of the world’s leading manufacturers of high-quality conductor plates and IC substrates. AT&S industrializes trendsetting technologies for its core businesses mobile devices, automotive, industry, medicine and advanced packaging. AT&S, an international high-growth company commands a global presence, with production sites in Austria (Leoben, Fehring) and plants in India (Nanjangud), China (Shanghai, Chongqing) and Korea (Ansan near Seoul).
According to a research study published in the renowned open access journal EPJ Data Science, music recommendations for fans of non-mainstream music, such as hard rock and ambient, may be less accurate than for those of mainstream music, such as pop.
A team of researchers at Know-Center, Graz University of Technology, Johannes Kepler University Linz, University of Innsbruck and University of Utrecht (the Netherlands) analysed the accuracy of algorithm-generated music recommendations for listeners of mainstream and non-mainstream music. They used a dataset containing the listening histories of 4,148 users of the music streaming platform Last.fm who listened mostly to non-mainstream music or mostly mainstream music.
„Since more and more music is becoming available via music streaming services, music recommendation systems have become essential for helping users to search, sort and filter extensive music collections. However, the quality of many cutting-edge music recommendation techniques for non-mainstream music users still leaves a lot to be desired. In our study we established that the willingness of a music user to listen to the kinds of music outside of his or her primary music preferences has a positive effect on the quality of recommendations. Thinking out of the box pays off even when it comes to listening to music,“ explained Dominik Kowald, first author of the study and Head of Research Area Social Computing at Know-Center.
“First, we established a computational model for artists, to whom music users listened most frequently. Based on this model, we were able to predict the probability of music users’ liking the music recommended to them by four common music recommendation algorithms. We found that listeners of mainstream music appeared to receive more accurate music recommendations than listeners of non-mainstream music“, stated Elisabeth Lex, the study’s scientific leader and Associate Professor of Applied Computer Science at Graz University of Technology.
Non-mainstream music listeners have been assigned by an algorithm into the following categories based on the types of music they most frequently listened to: users of music genres with only acoustic instruments (e.g. folk), users of high-energy music (e.g. hard rock, hip-hop), users of music with acoustic instruments and no vocals (e.g. ambient), and users of high-energy music with no vocals (electronica).
By comparing each group’s listening histories, the researchers identified users who were most likely to listen to music outside of their preferred genres and the diversity of music genres listened to by each group. Those who mainly listened to music such as ambient were found to be most likely to listen to music preferred by hard rock, folk and electronica listeners as well. Those who mainly listened to high-energy music were least likely to listen to music preferred by folk, electronica and ambient listeners. However, they listened to the widest variety of genres, e.g. hard rock, punk, singer/songwriter and hip-hop.
By using the computational model, the researchers predicted the probability of various groups of non-mainstream music listeners’ liking the music recommendations generated by the four common music recommendation algorithms. They found that those who listened mainly to high-energy music appeared to receive the least accurate music recommendations and those who mainly listened to ambient get the most.
Stefanie Lindstaedt, CEO of Know-Center and Director Institute of Interactive Systems & Data Science (ISDS) at TU Graz stated: „At Know-Center we research smart recommender algorithms for various contents and domains. We often face the problem of insufficient recommendations generated for users who have no mainstream preferences or for rarely used contents. However, we have made significant progress and will integrate the study’s findings into our recommender offers. Moreover, we would like to put this knowledge to good use by reducing discrimination potentials of algorithms in general in order to advance the development of trustworthy artificial intelligence within Austria and Europe.“
The authors pointed out potential benefits of their findings for the development of music recommendation systems that will provide more accurate recommendations to non-mainstream music listeners. However, they caution that since the study was based on a sample from Last.fm users, the results may not be representative of all Last.fm users and users of other music streaming platforms.
Study: Dominik KOWALD, Peter MUELLNER, Eva ZANGERLE, Christine BAUER, Markus SCHEDL, Elisabeth LEX. Support the Underground: Characteristics of Beyond-Mainstream Music Listeners. EPJ Data Science (2021)