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Lessons Learned from Assessing Trustworthy AI in Practice

Digital Society 2023年第3期2卷 1-25页

作者： Vetter, Dennis Amann, Julia Bruneault, Frédérick Coffee, Megan Düdder, Boris Gallucci, Alessio Gilbert, Thomas Krendl Hagendorff, Thilo van Halem, Irmhild Hickman, Eleanore Hildt, Elisabeth Holm, Sune Kararigas, Georgios Kringen, Pedro Madai, Vince I. Wiinblad Mathez, Emilie Tithi, Jesmin Jahan Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Computational Vision and Artificial Intelligence Lab Goethe University Frankfurt Frankfurt Am Main Germany Z-Inspection® Initiative Venice Italy Health Ethics and Policy Lab ETH Zurich Zurich Switzerland Strategy and Innovation Careum Foundation Zurich Switzerland Philosophie Departement Collège André-Laurendeau Montréal Canada École Des Médias Université du Québec À Montréal Montréal Canada Department of Medicine Division of Infectious Diseases and Immunology New York University Grossman School of Medicine New York City USA Department of Computer Science University of Copenhagen Copenhagen Denmark Digital Life Initiative Cornell Tech New York City USA Cluster of Excellence “Machine Learning: New Perspectives for Science” University of Tuebingen Tuebingen Germany School of Law University of Bristol Bristol UK Center for the Study of Ethics in the Professions Illinois Institute of Technology Chicago USA Department of Business Management and Analytics Arcada University of Applied Sciences Helsinki Finland Department of Food & Resource Economics University of Copenhagen Copenhagen Denmark Department of Physiology Faculty of Medicine University of Iceland Reykjavik Iceland QUEST Centre for Responsible Research Berlin Institute of Health Charité Universitätsmedizin Berlin Berlin Germany Faculty of Computing Engineering and the Built Environment School of Computing and Digital Technology Birmingham City University Birmingham UK Parallel Computing Labs Intel Santa Clara USA School of Economics Innovation and Technology Kristiania University College Oslo Norway Data Science Graduate School Seoul National University Seoul South Korea

Building artificial intelligence (AI) systems that adhere to ethical standards is a complex problem. Even though a multitude of guidelines for the design and development of such trustworthy AI systems exist, these guidelines focus on high-level and abstract requirements for AI systems, and it is often very difficult to assess if a specific system fulfills these requirements. The Z-Inspection® process provides a holistic and dynamic framework to evaluate the trustworthiness of specific AI systems at different stages of the AI lifecycle, including intended use, design, and development. It focuses, in particular, on the discussion and identification of ethical issues and tensions through the analysis of socio-technical scenarios and a requirement-based framework for ethical and trustworthy AI. This article is a methodological reflection on the Z-Inspection® process. We illustrate how high-level guidelines for ethical and trustworthy AI can be applied in practice and provide insights for both AI researchers and AI practitioners. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of real-world AI systems, as well as key recommendations and practical suggestions on how to ensure a rigorous trustworthiness assessment throughout the lifecycle of an AI system. The results presented in this article are based on our assessments of AI systems in the healthcare sector and environmental monitoring, where we used the framework for trustworthy AI proposed in the Ethics Guidelines for Trustworthy AI by the European Commission’s High-Level Expert Group on AI. However, the assessment process and the lessons learned can be adapted to other domains and include additional frameworks.

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Epitopedia: identifying molecular mimicry between pathogens and known immune epitopes

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ImmunoInformatics 2023年 9卷

作者： Christian A Balbin Janelle Nunez-Castilla Vitalii Stebliankin Prabin Baral Masrur Sobhan Trevor Cickovski Ananda Mohan Mondal Giri Narasimhan Prem Chapagain Kalai Mathee Jessica Siltberg-Liberles Department of Biological Sciences College of Arts Science and Education Florida International University Miami United States Bioinformatics Research Group (BioRG) Knight Foundation School of Computing and Information Sciences Florida International University Miami United States Department of Physics College of Arts Science and Education Florida International University Miami United States Machine Learning and Data Analytics Group (MLDAG) Knight Foundation School of Computing and Information Sciences Florida International University Miami United States Biomolecular Sciences Institute Florida International University Miami United States Department of Human and Molecular Genetics Herbert Wertheim College of Medicine Florida International University Miami United States

Upon infection, foreign antigenic proteins stimulate the host's immune system to produce antibodies targeting the pathogen. These antibodies bind to regions on the antigen called epitopes. Structural similarity (molecular mimicry) of epitopes between an infecting pathogen and host proteins or other pathogenic proteins the host has previously encountered can impact the host immune response to the pathogen and may lead to cross-reactive antibodies. The ability to identify potential regions of molecular mimicry in a pathogen can illuminate immune effects which are especially important to pathogen treatment and vaccine design. Here we present Epitopedia, a software pipeline that facilitates the identification of regions that may exhibit potential three-dimensional molecular mimicry between an antigenic pathogen protein and known immune epitopes as catalogued by the Immune Epitope database (IEDB). Epitopedia is open-source software released under the MIT license and is freely available on GitHub, including a Docker container with all other software dependencies preinstalled. We performed an analysis describing how various secondary structure states, identity between pentapeptide pairs, and identity between the parent sequences of pentapeptide pairs affects RMSD. We found that pentapeptides pairs in a helical conformation had considerably lower RMSD values than those in extended or coil conformations. We also found that RMSD is significantly increased when pentapeptide pairs are from non-homologous sequences.

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Analyzing the Structure of Attention in a Transformer Language Model

arXiv

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arXiv 2019年

作者： Vig, Jesse Belinkov, Yonatan Palo Alto Research Center Machine Learning and Data Science Group Interaction and Analytics Lab Palo AltoCA United States Harvard John A. Paulson School of Engineering and Applied Sciences MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA United States

The Transformer is a fully attention-based alternative to recurrent networks that has achieved state-of-the-art results across a range of NLP tasks. In this paper, we analyze the structure of attention in a Transformer language model, the GPT-2 small pretrained model. We visualize attention for individual instances and analyze the interaction between attention and syntax over a large corpus. We find that attention targets different parts of speech at different layer depths within the model, and that attention aligns with dependency relations most strongly in the middle layers. We also find that the deepest layers of the model capture the most distant relationships. Finally, we extract exemplar sentences that reveal highly specific patterns targeted by particular attention heads. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

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VulnerVAN: A Vulnerable Network Generation Tool

VulnerVAN: A Vulnerable Network Generation Tool

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MILCOM, Military Communications Conference

作者： Sridhar Venkatesan Jason A. Youzwak Shridatt Sugrim Cho-Yu J. Chiang Alexander Poylisher Matthew Witkowski Gary Walther Michelle Wolberg Ritu Chadha E. Allison Newcomb Blaine Hoffman Norbou Buchler Machine Learning and Data Analytics Research Perspecta Labs Inc. Basking Ridge NJ USA Computational and Information Science Directorate U.S. CCDC Army Research Laboratory Aberdeen MD USA Human Research and Engineering Directorate U.S. CCDC Army Research Laboratory

ISBN: (数字)9781728142807

ISBN: (纸本)9781728142814

Cyber training, security testing, and research and development activities are vital to improve the security posture of a network. Currently, many institutions use cyber security testbeds to conduct these activities in an isolated virtual environment. One of the important requirements for such an environment is to provide organizers (or experimenters) with a library of vulnerable network scenarios and capabilities to mount attacks against them. However, the task of preparing a vulnerable network scenario in current testbed environments is costly in time and labor, requires significant support from the testbed staff. To this end, we present a toolset called VulnerVAN that creates a vulnerable network scenario to realize an attack sequence. In this paper, we discuss the design of VulnerVAn - our proof-of-concept implementation on CyberVAN - and present a new high-level attack specification language that enables users to chain attack steps into an attack sequence. For a given attack sequence and network scenario, VulnerVAN identifies all possible attack paths through the network that can realize the attack sequence, and provides instructions to configure machines on an attack path selected by the user. VulnerVAn also provides an attack blueprint that can guide a Red team or an automated attacker to execute the attack sequence. To demonstrate VulnerVAN's capability, we consider the use-case of a typical data exfiltration attack sequence conducted by APTs and study the performance of VulnerVAn in mapping the attack sequence to different networks.

关键词： data security Training Specification languages Virtual environments

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machine learning Techniques for Stress Prediction in Working Employees

Machine Learning Techniques for Stress Prediction in Working...

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IEEE International Conference on Computational Intelligence and Computing Research

作者： U Srinivasulu Reddy Aditya Vivek Thota A Dharun Department of Computer Applications Machine Learning and Data Analytics Lab National Institute of Technology Trichy ECE National Institute of Technology Trichy

ISBN: (纸本)9781538615096

Stress disorders are a common issue among working IT professionals in the industry today. With changing lifestyle and work cultures, there is an increase in the risk of stress among the employees. Though many industries and corporates provide mental health related schemes and try to ease the workplace atmosphere, the issue is far from control. In this paper, we would like to apply machine learning techniques to analyze stress patterns in working adults and to narrow down the factors that strongly determine the stress levels. Towards this, data from the OSMI mental health survey 2017 responses of working professionals within the tech-industry was considered. Various machine learning techniques were applied to train our model after due data cleaning and preprocessing. The accuracy of the above models was obtained and studied comparatively. Boosting had the highest accuracy among the models implemented. By using Decision Trees, prominent features that influence stress were identified as gender, family history and availability of health benefits in the workplace. With these results, industries can now narrow down their approach to reduce stress and create a much comfortable workplace for their employees.

关键词： data handling decision trees DP management employee welfare ergonomics gender issues health care learning (artificial intelligence) occupational stress organisational aspects personnel psychology risk analysis computer installation management Decision Trees Organizational aspects gender issue data processing stress at work Ergonomics Psychology Risk Management risk analysis Personnel Employees Health Care data cleaning learning methods

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Supervised learning for Yaw Orientation Estimation

Supervised Learning for Yaw Orientation Estimation

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Tobias Feigl Christopher Mutschler Michael Philippsen Machine Learning and Information Fusion Group Fraunhofer Institute for Integrated Circuits IIS Nuremberg Germany Machine Learning and Data Analytics Lab Friedrich-Alexander University Erlangen-Nürnberg Erlangen Germany Programming Systems Group

With free movement and multi-user capabilities, there is demand to open up Virtual Reality (VR) for large spaces. However, the cost of accurate camera-based tracking grows with the size of the space and the number of users. No-pose (NP) tracking is cheaper, but so far it cannot accurately and stably estimate the yaw orientation of the user's head in the long-run. Our novel yaw orientation estimation combines a single inertial sensor located at the human's head with inaccurate positional tracking. We exploit that humans tend to walk in their viewing direction and that they also tolerate some orientation drift. We classify head and body motion and estimate heading drift to enable low-cost long-time stable head orientation in NP tracking on 100 m×100 m. Our evaluation shows that we estimate heading reasonably well.

关键词： Magnetic heads Feature extraction Acceleration Tracking Head Accelerometers Estimation

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Reducing over-smoothness in speech synthesis using Generative Adversarial Networks

arXiv

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arXiv 2018年

作者： Sheng, Leyuan Pavlovskiy, Evgeniy N. Department of Mathematics and Mechanics Novosibirsk State University Novosibirsk Russia Stream Data Analytics and Machine Learning laboratory Novosibirsk State University Novosibirsk Russia

Speech synthesis is widely used in many practical applications. In recent years, speech synthesis technology has developed rapidly. However, one of the reasons why synthetic speech is unnatural is that it often has over-smoothness. In order to improve the naturalness of synthetic speech, we first extract the mel-spectrogram of speech and convert it into a real image, then take the over-smooth mel-spectrogram image as input, and use image-to-image translation Generative Adversarial Networks(GANs) framework to generate a more realistic mel-spectrogram. Finally, the results show that this method greatly reduces the over-smoothness of synthesized speech and is more close to the mel-spectrogram of real speech. Copyright © 2018, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A Mobile Solution for Rhythmic Auditory Stimulation Gait Training

A Mobile Solution for Rhythmic Auditory Stimulation Gait Tra...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Katharina Aholt Christine F. Martindale Arne Küderle Heiko Gaßner Till Gladow Javier Rojo Samanta Villanueva-Mascato Jochen Klucken Maria Teresa Arredondo Waldmeyer Bjoern M. Eskofier Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany University Hospital Erlangen Germany Escuela Técnica Superior de Ingenieros de Telecomunicación Universidad Politécnica de Madrid Madrid Spain Life Supporting Technologies Universidad Politécnica de Madrid Spain

Recent studies showed that Parkinson's disease (PD) patients improved their gait parameters while walking with rhythmic auditory stimulation (RAS). They achieved a longer stride length, a reduced stride time variability and a higher walking speed. Combining RAS with mobile gait analysis would allow continuous monitoring of RAS effects and gait in natural environments. This paper proposes a mobile solution for home-based assessment of RAS by combining RAS gait training and a mobile system for data acquisition. Existing datasets were used to investigate the cadence of PD patients and to propose suitable frequencies for RAS gait training. The cadence calculation was implemented using a peak detection algorithm, which uses the time difference between two mid-swing events as stride time values. We validated our system as a whole using a cohort of 13 PD patients who performed RAS gait training. The algorithms were also validated against the eGaIT system, a state-of-the-art system, and achieved a mean F 1 score for detected strides of 97.57 % ± 0.86 % and a mean absolute error for the cadence of 0.16 spm ± 0.09 spm. This study lays the ground work for further clinical studies investigating the effectiveness of mobile RAS within a home environment.

关键词： Training Sensors Mobile handsets Parkinson's disease Legged locomotion Gyroscopes data analysis

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Indoor Positioning Using OFDM-Based Visible Light Communication System

Indoor Positioning Using OFDM-Based Visible Light Communicat...

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Birendra Ghimire Jochen Seitz Christopher Mutschler Precise Positioning and Analytics Department Fraunhofer Institute for Integrated Circuits IIS Nürnberg Germany Machine Learning and Data Analytics Lab Friedrich-Alexander University Erlangen-Nürnberg (FAU) Erlangen Germany

Precise indoor positioning is essential to support emerging applications of location aware mobile computing. Current positioning techniques that use signals transmitted in the Gigahertz region of radio-frequency spectrum do not provide highly accurate position estimates due to multipath propagation of signals. This paper proposes a novel positioning system which uses the entities of visible light communication (VLC) system as anchors and tags. Our technique scales with the number of tags and the VLC network provides both positioning and communication capabilities. The anchor points transmit the OFDM-based VLC signals synchronously, and we estimate the time differences of arrival between anchor points and tags using positioning reference signals embedded into the air-interface of the VLC system. Simulation results show that positioning accuracy of 10 cm or better is possible for over 95% of users if the sampling clock offset is better that 10 ppm, clock jitter is below 1 ps, and a bit resolution of at least 16 bits is available.

关键词： Light emitting diodes OFDM Optical receivers Adaptive optics Optical filters Measurement uncertainty

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Convolutional Neural Networks for Position Estimation in TDoA-Based Locating Systems

Convolutional Neural Networks for Position Estimation in TDo...

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Arne Niitsoo Thorsten Edelhäußer Christopher Mutschler Machine Learning and Information Fusion Group Fraunhofer Institute for Integrated Circuits IIS Nuremberg Germany Maching Learning and Data Analytics Lab Friedrich-Alexander University Erlangen-Nürnberg (FAU) Erlangen Germany

Object localization and tracking is essential for many applications including logistics and industry. Many local Time-of-Flight (ToF)-based locating systems use synchronized antennas to receive radio signals emitted by mobile tags. They detect the Time-of-Arrival (TOA) of the signal at each antenna and trilaterate the position from the Time Difference-of-Arrival (TDoA) between antennas. However, in multipath scenarios it is difficult to extract the correct ToA. This causes wrong positions. This paper proposes a signal processing method that uses deep learning to estimate the absolute tag position directly from the raw channel impulse response (CIR) data. We use the CIR together with ground truth positional data to train a convolutional neural network (CNN) that not only estimates non-linearities in the signal propagation space but also analyzes the signal for multipath effects. Our evaluation shows that our position estimation works in multipath environments and also outperforms classical signal processing in line-of-sight situations.

关键词： Feature extraction Neurons Antennas Estimation Convolution Convolutional neural networks

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