Artificial intelligence (AI) makes decisions impacting our daily lives in an increasingly autonomous manner. Their actions might cause accidents, harm, or, more generally, violate regulations. Determining whether an AI caused a specific event and, if so, what triggered the AI's action, are key forensic questions. We provide a conceptualization of the problems and strategies for forensic investigation. We focus on AI that is potentially ``malicious by design'' and grey box analysis. Our evaluation using convolutional neural networks illustrates challenges and ideas for identifying malicious AI.
In many applications of forensic image analysis, state-of-the-art results are nowadays achieved with machine learning methods. However, concerns about their reliability and opaqueness raise the question whether such methods can be used in criminal investigations. So far, this question of legal compliance has hardly been discussed, also because legal regulations for machine learning methods were not defined explicitly. To this end, the European Commission recently proposed the artificial intelligence (AI) act, a regulatory framework for the trustworthy use of AI. Under the draft AI act, high-ri…
XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensics
Department of Information Security, College of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi Arabia（信息安全系，计算机科学与工程学院，哈伊尔大学，沙特阿拉伯）
University of Bristol（布里斯托尔大学）；RMIT University（皇家墨尔本理工大学）
Today, artificial intelligence systems driven by machine learning algorithms can be in a position to take important, and sometimes legally binding, decisions about our everyday lives. In many cases, however, these systems and their actions are neither regulated nor certified. To help counter the potential harm that such algorithms can cause we developed an open source toolbox that can analyse selected fairness, accountability and transparency aspects of the machine learning process: data (and their features), models and predictions, allowing to automatically and objectively report them to rele…
AI Institute, University of South Carolina（南卡罗来纳大学人工智能学院）
Artificial Intelligence (AI) technologies have long been positioned as a tool to provide crucial data-driven decision support to people. In this survey paper, we look at how AI in general, and collaboration assistants (CAs or chatbots for short) in particular, have been used during a true global exigency - the COVID-19 pandemic. The key observation is that chatbots missed their "Apollo moment" when they could have really provided contextual, personalized, reliable decision support at scale that the state-of-the-art makes possible. We review the existing capabilities that are feasible and metho…
This work provides a starting point for researchers interested in gaining a deeper understanding of the big picture of artificial intelligence (AI). To this end, a narrative is conveyed that allows the reader to develop an objective view on current developments that is free from false promises that dominate public communication. An essential takeaway for the reader is that AI must be understood as an umbrella term encompassing a plethora of different methods, schools of thought, and their respective historical movements. Consequently, a bottom-up strategy is pursued in which the field of AI is…
Towards Automation of Human Stage of Decay Identification: An Artificial Intelligence Approach
University of Tennessee（田纳西大学）；The Bredesen Center, University of Tennessee（田纳西大学布雷德森中心）；Department of Anthropology, University of Tennessee（田纳西大学人类学系）
Research Center for Information Technology Innovation, Academia Sinica（中央研究院資訊科技創新研究中心）；Chang Gung University（長庚大學）
In countries that enabled patients to choose their own providers, a common problem is that the patients did not make rational decisions, and hence, fail to use healthcare resources efficiently. This might cause problems such as overwhelming tertiary facilities with mild condition patients, thus limiting their capacity of treating acute and critical patients. To address such maldistributed patient volume, it is essential to oversee patients choices before further evaluation of a policy or resource allocation. This study used nationwide insurance data, accumulated possible features discussed in …
Artificial Intelligence (AI) is necessary to address the large and growing deficit in retina and healthcare access globally. And mobile AI diagnostic platforms running in the Cloud may effectively and efficiently distribute such AI capability. Here we sought to evaluate the feasibility of Cloud-based mobile artificial intelligence for detection of retinal disease. And to evaluate the accuracy of a particular such system for detection of subretinal fluid (SRF) and macula edema (ME) on OCT scans. A multicenter retrospective image analysis was conducted in which board-certified ophthalmologists w…
The Alan Turing Institute（艾伦·图灵研究所）；University College London（伦敦大学学院）；University of Warwick（华威大学）；Kings College Hospital（国王学院医院）
Objective: To determine the completeness of argumentative steps necessary to conclude effectiveness of an algorithm in a sample of current ML/AI supervised learning literature. Data Sources: Papers published in the Neural Information Processing Systems (NeurIPS, née NIPS) journal where the official record showed a 2017 year of publication. Eligibility Criteria: Studies reporting a (semi-)supervised model, or pre-processing fused with (semi-)supervised models for tabular data. Study Appraisal: Three reviewers applied the assessment criteria to determine argumentative completeness. The criteria …
Performance of a large language model-Artificial Intelligence based chatbot for counseling patients with sexually transmitted infections and genital diseases
University College of Medical Sciences and Guru Teg Bahadur Hospital（大学医学院与古鲁·特格·巴哈杜尔医院）；HeHealth Inc.（HeHealth公司）
Prepared Or Unprepared? Evaluating Healthcare Workforce Readiness for Clinical Adoption of Artificial Intelligence in Nigeria
College of Health Sciences, Bayero University Kano（贝耶罗大学卡诺分校健康科学学院）；Federal University of Health Sciences, Azare（阿扎雷联邦健康科学大学）；African Institute for Research Advancement & Innovation（非洲研究促进与创新研究所）；Medical Artificial Intelligence Laboratory (MAI Lab)（医学人工智能实验室）；Aminu Kano Teaching Hospital（阿米努·卡诺教学医院）；Nnamdi Azikiwe University Teaching Hospital（纳姆迪·阿齐基韦大学教学医院）；Lagos State Teaching Hospital（拉各斯州立教学医院）
Warsaw University of Technology（华沙理工大学）；Nanyang Technological University（南洋理工大学）；Jagiellonian University of Cracow（克拉科夫雅盖隆大学）；Tooploox（Tooploox公司）
Artificial Intelligence in higher education opens new possibilities for improving the lecturing process, such as enriching didactic materials, helping in assessing students' works or even providing directions to the teachers on how to enhance the lectures. We follow this research path, and in this work, we explore how an academic lecture can be assessed automatically by quantitative features. First, we prepare a set of qualitative features based on teaching practices and then annotate the dataset of academic lecture videos collected for this purpose. We then show how these features could be de…
