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1. Challenges of a Digital Single Market from an Austrian perspective towards Smart Regulations

Author

Peter Egger, Dominik Geringer, Gerwald Gindra-Vady, Christina Gruber, Elisabeth Paar, Lukas Reiter, Karl Stöger, and Stefan Thalmann

Subjects
algorithms, algorithm awareness project, article 22 gdpr, article 101 tfeu, artificial intelligence, automated decision-making, cartels, competition law, digital single gateway, digital single market, digital society, e-government, european commission, highlevel expert group on artificial intelligence, machine learning, postal services, product liability, smart regulation, software, Law, Law of Europe, KJ-KKZ
Abstract

This paper discusses various legal challenges of the “digitisation of the single market”. The question arises to which extent the current regulatory framework appears suitable to deal with the presented challenges of digitisation and where additional regulation is required. In the field of autonomous decision-making by AI, we identified the most pressing need for new regulation. While the EU (and increasingly Austria, as well) is aware of this need, regulation to date remains scarce. Though the EU legislator has already taken specific precautions for the use of algorithms in the GDPR, such regulatory approaches are missing in most other fields of law. In contrast to this, antitrust law and product liability law already appear to be well suited to meet the challenges posed by digitisation. This is especially true for product liability law, which is in principle apt to cover the specific challenges of the convergence of software and hardware in smart products. However, uncertainty about its applicability to incorporeal goods would make clarification of current product liability legislation advisable – a view shared by the European Commission. Two more fields very recently received some legislative attention due to the changing needs of a digital society: the postal sector on the one hand, and e-government on the other hand. In both fields, new legislation – tellingly in the form of (partially) directly applicable regulations – has recently been passed by the EU – a sharp contrast to the case of self-learning AI. However, while the integration of the new regulation on cross-border parcel delivery will probably not pose major challenges for domestic markets, the implementation of the Single Digital Gateway will raise serious organisational and legal challenges for national administrations (especially when taking into account the limited success of the previous related initiative on the points of single contact under the Services Directive).

Published
2019
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2. Künstliche Intelligenz und maschinelles Lernen in der intensivmedizinischen Forschung und klinischen Anwendung.

Author

Peine, A., Lütge, C., Poszler, F., Celi, L., Schöffski, O., Marx, G., and Martin, L.

Abstract

Copyright of Anaesthesiologie & Intensivmedizin is the property of DGAI e.V. - Deutsche Gesellschaft fur Anasthesiologie und Intensivmedizin e.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2020
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3. Algorithmic discrimination in the Austrian labour market

Author

Stuefer, Matthias

Subjects
Digitalisierung, Mediatisierung, Big Data, Arbeitsmarkt, algorithms, artificial intelligence, Diskriminierung, digitalization, labour market, machine learning, Bias, ADM systems, ADM-Systeme, Künstliche Intelligenz, AMAS, AMS, Algorithmen, Maschinelles Lernen, discrimination
Abstract

Die voranschreitende Digitalisierung aller Lebensbereiche gewinnt zunehmend an Bedeutung, auch die Soziale Arbeit ist davon betroffen. Wichtige Grundlage für alle Bereiche der Digitalisierung stellen Algorithmen dar. Lernende Algorithmen können in großen Datenmengen Muster erkennen und auf deren Grundlage Entscheidungsregeln definieren. Dies möchte sich auch das Arbeitsmarktservice Österreich zu Nutze machen, um die Chancen zur Arbeitsaufnahme von Arbeitsuchenden durch ein algorithmisches System prognostizieren zu lassen und auf dessen Basis deren Zugang zu Ressourcen zu bestimmen. Ziel dieser Arbeit ist es zu untersuchen welche Diskriminierungen in diesem System eingebettet sind oder dadurch verursacht werden. Es zeigt sich, dass in der aktuellen Umsetzung bereits marginalisierte Personen Gefahr laufen weitere Benachteiligung zu erfahren und bestehende strukturelle Ungleichheiten verstärkt werden. The increasing digitalization of all areas of life is steadily gaining more significance, which also affects the profession of social work. An important basis for all areas of digitalization are algorithms. Learning algorithms can identify patterns in big amounts of data and derive decision making rules from them. The Austrian employment market service intends to make use of these capabilities to predict chances of job seekers for finding employment and subsequently allocate access to resources based on those predictions. This paper’s aim is to identify discriminatory aspects of this approach. Results show that in its current implementation already marginalized people are at risk of further discrimination while existing structural inequalities get reinforced.

Published
2022

4. Using ultrasound images of the forearm to predict finger positions

Author

Claudio Castellini, Georg Passig, and E. Zarka

Subjects
Adult, Male, Models, Anatomic, Computer science, Movement, Transducers, Biomedical Engineering, Optical flow, Sensation, Image processing, Neuroimaging, Thumb, Rotation, Fingers, Institut für Robotik und Mechatronik (bis 2012), Young Adult, Forearm, Internal Medicine, medicine, Image Processing, Computer-Assisted, Humans, prosthetics, Ultrasonography, business.industry, ultrasound, General Neuroscience, Rehabilitation, Ultrasound, Biomechanics, Reproducibility of Results, Hand, Visualization, body regions, medicine.anatomical_structure, machine learning, Data Interpretation, Statistical, Linear Models, Female, business, Algorithms, Biomedical engineering, Forecasting
Abstract

Medical ultrasound imaging is a well-known technique to gather live views of the interior of the human body. It is totally safe, it provides high spatial and temporal resolution, and it is nowadays available at any hospital. This suggests that it could be used as a human-computer interface. In this paper, we use ultrasound images of the human forearm to predict the finger positions, including thumb adduction and thumb rotation. Our experimental results show that there is a clear linear relationship between the features we extract from the images, and finger positions, expressed as angles at the metacarpo-phalangeal joints. The method is uniformly valid for all subjects considered. The unavoidable movements of the ultrasound probe with respect to the skin and of the skin with respect to the inner musculoskeletal structure are compensated for using the optical flow. Typical applications of this system range from teleoperated fine manipulation to finger stiffness estimation to ergonomy. If successfully applied to transradial amputees, it could be also used to reconstruct the imaginary limb, paving the way to, e.g., fine control of hand prostheses, treatment of neuropathic/phantom limb pain and visualization of the imaginary limb as a tool for the neuroscientist.

Published
2012