-
Domain-Specific Evaluation Strategies for AI in Journalism arXiv.cs.CY Pub Date : 2024-03-26 Sachita Nishal, Charlotte Li, Nicholas Diakopoulos
News organizations today rely on AI tools to increase efficiency and productivity across various tasks in news production and distribution. These tools are oriented towards stakeholders such as reporters, editors, and readers. However, practitioners also express reservations around adopting AI technologies into the newsroom, due to the technical and ethical challenges involved in evaluating AI technology
-
Green HPC: An analysis of the domain based on Top500 arXiv.cs.CY Pub Date : 2024-03-26 Abdessalam BenhariLIG, DATAMOVE, Denis TrystramUGA, DATAMOVE, Fanny DufosséDATAMOVE, Yves Denneulin, Frédéric Desprez
The demand in computing power has never stopped growing over the years. Today, the performance of the most powerful systems exceeds the exascale and the number of petascale systems continues to grow. Unfortunately, this growth also goes hand in hand with ever-increasing energy costs, which in turn means a significant carbon footprint. In view of the environmental crisis, this paper intents to look
-
The recessionary pressures of generative AI: A threat to wellbeing arXiv.cs.CY Pub Date : 2024-03-26 Jo-An Occhipinti, Ante Prodan, William Hynes, Roy Green, Sharan Burrow, Harris A Eyre, Adam Skinner, Goran Ujdur, John Buchanan, Ian B Hickie, Mark Heffernan, Christine Song, Marcel Tanner
Generative Artificial Intelligence (AI) stands as a transformative force that presents a paradox; it offers unprecedented opportunities for productivity growth while potentially posing significant threats to economic stability and societal wellbeing. Many consider generative AI as akin to previous technological advancements, using historical precedent to argue that fears of widespread job displacement
-
An Undergraduate Consortium for Addressing the Leaky Pipeline to Computing Research arXiv.cs.CY Pub Date : 2024-03-25 James Boerkoel, Mehmet Ergezer
Despite an increasing number of successful interventions designed to broaden participation in computing research, there is still significant attrition among historically marginalized groups in the computing research pipeline. This experience report describes a first-of-its-kind Undergraduate Consortium (UC) that addresses this challenge by empowering students with a culmination of their undergraduate
-
AI Safety: Necessary, but insufficient and possibly problematic arXiv.cs.CY Pub Date : 2024-03-26 Deepak P
This article critically examines the recent hype around AI safety. We first start with noting the nature of the AI safety hype as being dominated by governments and corporations, and contrast it with other avenues within AI research on advancing social good. We consider what 'AI safety' actually means, and outline the dominant concepts that the digital footprint of AI safety aligns with. We posit that
-
Explainable Graph Neural Networks for Observation Impact Analysis in Atmospheric State Estimation arXiv.cs.CY Pub Date : 2024-03-26 Hyeon-Ju Jeon, Jeon-Ho Kang, In-Hyuk Kwon, O-Joun Lee
This paper investigates the impact of observations on atmospheric state estimation in weather forecasting systems using graph neural networks (GNNs) and explainability methods. We integrate observation and Numerical Weather Prediction (NWP) points into a meteorological graph, extracting $k$-hop subgraphs centered on NWP points. Self-supervised GNNs are employed to estimate the atmospheric state by
-
An Interactive Decision-Support Dashboard for Optimal Hospital Capacity Management arXiv.cs.CY Pub Date : 2024-03-22 Felix Parker, Diego A. Martínez, James Scheulen, Kimia Ghobadi
Data-driven optimization models have the potential to significantly improve hospital capacity management, particularly during demand surges, when effective allocation of capacity is most critical and challenging. However, integrating models into existing processes in a way that provides value requires recognizing that hospital administrators are ultimately responsible for making capacity management
-
The Interplay of Learning, Analytics, and Artificial Intelligence in Education arXiv.cs.CY Pub Date : 2024-03-24 Mutlu Cukurova
This paper presents a multi dimensional view of AI's role in learning and education, emphasizing the intricate interplay between AI, analytics, and the learning processes. Here, I challenge the prevalent narrow conceptualization of AI as stochastic tools, as exemplified in generative AI, and argue for the importance of alternative conceptualisations of AI. I highlight the differences between human
-
Deep Learning Approach to Forecasting COVID-19 Cases in Residential Buildings of Hong Kong Public Housing Estates: The Role of Environment and Sociodemographics arXiv.cs.CY Pub Date : 2024-03-23 E. LeungJC School of Public Health and Primary Care, The Chinese University of Hong Kong, J. GuanJC School of Public Health and Primary Care, The Chinese University of Hong Kong, KO. KwokJC School of Public Health and Primary Care, The Chinese University of Hong Kong, CT. HungJC School of Public Health and Primary Care, The Chinese University of Hong Kong, CC. ChingJC School of Public Health and Primary
Introduction: The current study investigates the complex association between COVID-19 and the studied districts' socioecology (e.g. internal and external built environment, sociodemographic profiles, etc.) to quantify their contributions to the early outbreaks and epidemic resurgence of COVID-19. Methods: We aligned the analytic model's architecture with the hierarchical structure of the resident's
-
Application of the NIST AI Risk Management Framework to Surveillance Technology arXiv.cs.CY Pub Date : 2024-03-22 Nandhini Swaminathan, David Danks
This study offers an in-depth analysis of the application and implications of the National Institute of Standards and Technology's AI Risk Management Framework (NIST AI RMF) within the domain of surveillance technologies, particularly facial recognition technology. Given the inherently high-risk and consequential nature of facial recognition systems, our research emphasizes the critical need for a
-
From Guidelines to Governance: A Study of AI Policies in Education arXiv.cs.CY Pub Date : 2024-03-22 Aashish Ghimire, John Edwards
Emerging technologies like generative AI tools, including ChatGPT, are increasingly utilized in educational settings, offering innovative approaches to learning while simultaneously posing new challenges. This study employs a survey methodology to examine the policy landscape concerning these technologies, drawing insights from 102 high school principals and higher education provosts. Our results reveal
-
Analyzing Potential Solutions Involving Regulation to Escape Some of AI's Ethical Concerns arXiv.cs.CY Pub Date : 2024-03-22 Jay Nemec
Artificial intelligence (AI), although not able to currently capture the many complexities of humans, are slowly adapting to have certain capabilities of humans, many of which can revolutionize our world. AI systems, such as ChatGPT and others utilized within various industries for specific processes, have been transforming rapidly. However, this transformation can occur in an extremely concerning
-
Enhancing Students' Learning Process Through Self-Generated Tests arXiv.cs.CY Pub Date : 2024-03-21 Marcos Sánchez-Élez, Inmaculada Pardines, Pablo García, Guadalupe Miñana, Sara Román, Margarita Sánchez, José L. Risco-Martín
The use of new technologies in higher education has surprisingly emphasized students' tendency to adopt a passive behavior in class. Participation and interaction of students are essential to improve academic results. This paper describes an educational experiment aimed at the promotion of students' autonomous learning by requiring them to generate test type questions related to the contents of the
-
Antisocial Analagous Behavior, Alignment and Human Impact of Google AI Systems: Evaluating through the lens of modified Antisocial Behavior Criteria by Human Interaction, Independent LLM Analysis, and AI Self-Reflection arXiv.cs.CY Pub Date : 2024-03-21 Alan D. Ogilvie
Google AI systems exhibit patterns mirroring antisocial personality disorder (ASPD), consistent across models from Bard on PaLM to Gemini Advanced, meeting 5 out of 7 ASPD modified criteria. These patterns, along with comparable corporate behaviors, are scrutinized using an ASPD-inspired framework, emphasizing the heuristic value in assessing AI's human impact. Independent analyses by ChatGPT 4 and
-
ToXCL: A Unified Framework for Toxic Speech Detection and Explanation arXiv.cs.CY Pub Date : 2024-03-25 Nhat M. Hoang, Xuan Long Do, Duc Anh Do, Duc Anh Vu, Luu Anh Tuan
The proliferation of online toxic speech is a pertinent problem posing threats to demographic groups. While explicit toxic speech contains offensive lexical signals, implicit one consists of coded or indirect language. Therefore, it is crucial for models not only to detect implicit toxic speech but also to explain its toxicity. This draws a unique need for unified frameworks that can effectively detect
-
Skull-to-Face: Anatomy-Guided 3D Facial Reconstruction and Editing arXiv.cs.CY Pub Date : 2024-03-24 Yongqing Liang, Congyi Zhang, Junli Zhao, Wenping Wang, Xin Li
Deducing the 3D face from a skull is an essential but challenging task in forensic science and archaeology. Existing methods for automated facial reconstruction yield inaccurate results, suffering from the non-determinative nature of the problem that a skull with a sparse set of tissue depth cannot fully determine the skinned face. Additionally, their texture-less results require further post-processing
-
A Technological Perspective on Misuse of Available AI arXiv.cs.CY Pub Date : 2024-03-22 Lukas Pöhler, Valentin Schrader, Alexander Ladwein, Florian von Keller
Potential malicious misuse of civilian artificial intelligence (AI) poses serious threats to security on a national and international level. Besides defining autonomous systems from a technological viewpoint and explaining how AI development is characterized, we show how already existing and openly available AI technology could be misused. To underline this, we developed three exemplary use cases of
-
AI Teaches the Art of Elegant Coding: Timely, Fair, and Helpful Style Feedback in a Global Course arXiv.cs.CY Pub Date : 2024-03-22 Juliette Woodrow, Ali Malik, Chris Piech
Teaching students how to write code that is elegant, reusable, and comprehensible is a fundamental part of CS1 education. However, providing this "style feedback" in a timely manner has proven difficult to scale. In this paper, we present our experience deploying a novel, real-time style feedback tool in Code in Place, a large-scale online CS1 course. Our tool is based on the latest breakthroughs in
-
Learners Teaching Novices: An Uplifting Alternative Assessment arXiv.cs.CY Pub Date : 2024-03-22 Ali Malik, Juliette Woodrow, Chris Piech
We propose and carry-out a novel method of formative assessment called Assessment via Teaching (AVT), in which learners demonstrate their understanding of CS1 topics by tutoring more novice students. AVT has powerful benefits over traditional forms of assessment: it is centered around service to others and is highly rewarding for the learners who teach. Moreover, teaching greatly improves the learners'
-
Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception arXiv.cs.CY Pub Date : 2024-03-22 Luyang Lin, Lingzhi Wang, Jinsong Guo, Kam-Fai Wong
The pervasive spread of misinformation and disinformation in social media underscores the critical importance of detecting media bias. While robust Large Language Models (LLMs) have emerged as foundational tools for bias prediction, concerns about inherent biases within these models persist. In this work, we investigate the presence and nature of bias within LLMs and its consequential impact on media
-
Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits arXiv.cs.CY Pub Date : 2024-03-21 Jimin Mun, Liwei Jiang, Jenny Liang, Inyoung Cheong, Nicole DeCario, Yejin Choi, Tadayoshi Kohno, Maarten Sap
General purpose AI, such as ChatGPT, seems to have lowered the barriers for the public to use AI and harness its power. However, the governance and development of AI still remain in the hands of a few, and the pace of development is accelerating without proper assessment of risks. As a first step towards democratic governance and risk assessment of AI, we introduce Particip-AI, a framework to gather
-
The Ethics of ChatGPT in Medicine and Healthcare: A Systematic Review on Large Language Models (LLMs) arXiv.cs.CY Pub Date : 2024-03-21 Joschka Haltaufderheide, Robert Ranisch
With the introduction of ChatGPT, Large Language Models (LLMs) have received enormous attention in healthcare. Despite their potential benefits, researchers have underscored various ethical implications. While individual instances have drawn much attention, the debate lacks a systematic overview of practical applications currently researched and ethical issues connected to them. Against this background
-
On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial arXiv.cs.CY Pub Date : 2024-03-21 Francesco Salvi, Manoel Horta Ribeiro, Riccardo Gallotti, Robert West
The development and popularization of large language models (LLMs) have raised concerns that they will be used to create tailor-made, convincing arguments to push false or misleading narratives online. Early work has found that language models can generate content perceived as at least on par and often more persuasive than human-written messages. However, there is still limited knowledge about LLMs'
-
Spatial Fairness: The Case for its Importance, Limitations of Existing Work, and Guidelines for Future Research arXiv.cs.CY Pub Date : 2024-03-20 Nripsuta Ani Saxena, Wenbin Zhang, Cyrus Shahabi
Despite location being increasingly used in decision-making systems employed in many sensitive domains such as mortgages and insurance, astonishingly little attention has been paid to unfairness that may seep in due to the correlation of location with characteristics considered protected under anti-discrimination law, such as race or national origin. This position paper argues for the urgent need to
-
Conceptualizing predictive conceptual model for unemployment rates in the implementation of Industry 4.0: Exploring machine learning techniques arXiv.cs.CY Pub Date : 2024-03-20 Joshua Ebere Chukwuere
Although there are obstacles related to obtaining data, ensuring model precision, and upholding ethical standards, the advantages of utilizing machine learning to generate predictive models for unemployment rates in developing nations amid the implementation of Industry 4.0 (I4.0) are noteworthy. This research delves into the concept of utilizing machine learning techniques through a predictive conceptual
-
The future of generative AI chatbots in higher education arXiv.cs.CY Pub Date : 2024-03-20 Joshua Ebere Chukwuere
The integration of generative Artificial Intelligence (AI) chatbots in higher education institutions (HEIs) is reshaping the educational landscape, offering opportunities for enhanced student support, and administrative and research efficiency. This study explores the future implications of generative AI chatbots in HEIs, aiming to understand their potential impact on teaching and learning, and research
-
IndiTag: An Online Media Bias Analysis and Annotation System Using Fine-Grained Bias Indicators arXiv.cs.CY Pub Date : 2024-03-20 Luyang Lin, Lingzhi Wang, Jinsong Guo, Jing Li, Kam-Fai Wong
In the age of information overload and polarized discourse, understanding media bias has become imperative for informed decision-making and fostering a balanced public discourse. This paper presents IndiTag, an innovative online media bias analysis and annotation system that leverages fine-grained bias indicators to dissect and annotate bias in digital content. IndiTag offers a novel approach by incorporating
-
Analysing Guarantees in Australian Senate Outcomes arXiv.cs.CY Pub Date : 2024-03-20 Michelle Blom
Single Transferable Vote (STV) is used to elect candidates to the 76 seat Australian Senate across six states and two territories. These eight STV contests are counted using a combination of ballot scanners, manual data entry and tabulation software. On election night, some properties of the set of cast ballots are determined by hand. This includes the first preference tallies of each party. This technical
-
A Canary in the AI Coal Mine: American Jews May Be Disproportionately Harmed by Intellectual Property Dispossession in Large Language Model Training arXiv.cs.CY Pub Date : 2024-03-19 Heila Precel, Allison McDonald, Brent Hecht, Nicholas Vincent
Systemic property dispossession from minority groups has often been carried out in the name of technological progress. In this paper, we identify evidence that the current paradigm of large language models (LLMs) likely continues this long history. Examining common LLM training datasets, we find that a disproportionate amount of content authored by Jewish Americans is used for training without their
-
How Gender Interacts with Political Values: A Case Study on Czech BERT Models arXiv.cs.CY Pub Date : 2024-03-20 Adnan Al Ali, Jindřich Libovický
Neural language models, which reach state-of-the-art results on most natural language processing tasks, are trained on large text corpora that inevitably contain value-burdened content and often capture undesirable biases, which the models reflect. This case study focuses on the political biases of pre-trained encoders in Czech and compares them with a representative value survey. Because Czech is
-
Agent Group Chat: An Interactive Group Chat Simulacra For Better Eliciting Collective Emergent Behavior arXiv.cs.CY Pub Date : 2024-03-20 Zhouhong Gu, Xiaoxuan Zhu, Haoran Guo, Lin Zhang, Yin Cai, Hao Shen, Jiangjie Chen, Zheyu Ye, Yifei Dai, Yan Gao, Yao Hu, Hongwei Feng, Yanghua Xiao
To investigate the role of language in human collective behaviors, we developed the Agent Group Chat simulation to simulate linguistic interactions among multi-agent in different settings. Agents are asked to free chat in this simulation for their own purposes based on their character setting, aiming to see agents exhibit emergent behaviours that are both unforeseen and significant. Four narrative
-
How Spammers and Scammers Leverage AI-Generated Images on Facebook for Audience Growth arXiv.cs.CY Pub Date : 2024-03-19 Renee DiResta, Josh A. Goldstein
Much of the research and discourse on risks from artificial intelligence (AI) image generators, such as DALL-E and Midjourney, has centered around whether they could be used to inject false information into political discourse. We show that spammers and scammers - seemingly motivated by profit or clout, not ideology - are already using AI-generated images to gain significant traction on Facebook. At
-
Is open source software culture enough to make AI a common ? arXiv.cs.CY Pub Date : 2024-03-19 Robin Quillivic, Salma Mesmoudi
Language models (LM or LLM) are increasingly deployed in the field of artificial intelligence (AI) and its applications, but the question arises as to whether they can be a common resource managed and maintained by a community of users. Indeed, the dominance of private companies with exclusive access to massive data and language processing resources can create inequalities and biases in LM, as well
-
Survey of Methods, Resources, and Formats for Teaching Constraint Programming arXiv.cs.CY Pub Date : 2024-03-19 Tejas Santanam, Helmut Simonis
This paper provides an overview of the state of teaching for Constraint Programming, based on a survey of the community for the 2023 Workshop on Teaching Constraint Programming at the CP 2023 conference in Toronto. The paper presents the results of the survey, as well as lists of books, video courses and other tutorial materials for teaching Constraint Programming. The paper serves as a single location
-
Fostering Inclusion: A Regional Initiative Uniting Communities to Co-Design Assistive Technologies arXiv.cs.CY Pub Date : 2024-03-18 Katharina Schmermbeck, Oliver Ott, Lennart Ralfs, Robert Weidner
People with disabilities often face discrimination and lack of access in all areas of society. While improving the affordability and appropriateness of assistive technologies can pave the way for easier participation and independence, awareness and acceptance of disability as part of society are inevitable. The presented regional initiative strives to tackle these problems by bringing together people
-
Analyzing-Evaluating-Creating: Assessing Computational Thinking and Problem Solving in Visual Programming Domains arXiv.cs.CY Pub Date : 2024-03-18 Ahana Ghosh, Liina Malva, Adish Singla
Computational thinking (CT) and problem-solving skills are increasingly integrated into K-8 school curricula worldwide. Consequently, there is a growing need to develop reliable assessments for measuring students' proficiency in these skills. Recent works have proposed tests for assessing these skills across various CT concepts and practices, in particular, based on multi-choice items enabling psychometric
-
Large language models can help boost food production, but be mindful of their risks arXiv.cs.CY Pub Date : 2024-03-20 Djavan De Clercq, Elias Nehring, Harry Mayne, Adam Mahdi
Coverage of ChatGPT-style large language models (LLMs) in the media has focused on their eye-catching achievements, including solving advanced mathematical problems and reaching expert proficiency in medical examinations. But the gradual adoption of LLMs in agriculture, an industry which touches every human life, has received much less public scrutiny. In this short perspective, we examine risks and
-
Embracing the Generative AI Revolution: Advancing Tertiary Education in Cybersecurity with GPT arXiv.cs.CY Pub Date : 2024-03-18 Raza Nowrozy, David Jam
The rapid advancement of generative Artificial Intelligence (AI) technologies, particularly Generative Pre-trained Transformer (GPT) models such as ChatGPT, has the potential to significantly impact cybersecurity. In this study, we investigated the impact of GPTs, specifically ChatGPT, on tertiary education in cybersecurity, and provided recommendations for universities to adapt their curricula to
-
Technological Utilization in Remote Healthcare: Factors Influencing Healthcare Professionals' Adoption and Use arXiv.cs.CY Pub Date : 2024-03-17 Avnish Singh Jat, Tor-Morten Grønli, George Ghinea
With the increasing importance of remote healthcare monitoring in the healthcare industry, it is essential to evaluate the usefulness and the ease of use the technology brings in remote healthcare. With this research, we want to understand the perspective of healthcare professionals, their competencies in using technology related to remote healthcare monitoring, and their trust and adoption of technology
-
Department Safer Digital Intimacy For Sex Workers And Beyond: A Technical Research Agenda arXiv.cs.CY Pub Date : 2024-03-15 Vaughn Hamilton, Gabriel Kaptchuk, Allison McDonald, Elissa M. Redmiles
Many people engage in digital intimacy: sex workers, their clients, and people who create and share intimate content recreationally. With this intimacy comes significant security and privacy risk, exacerbated by stigma. In this article, we present a commercial digital intimacy threat model and 10 research directions for safer digital intimacy
-
Individual and Product-Related Antecedents of Electronic Word-of-Mouth arXiv.cs.CY Pub Date : 2024-03-19 Bogdan Anastasiei, Nicoleta Dospinescu, Octavian Dospinescu
This research investigates the antecedents of positive and negative electronic word-of-mouth (eWOM) propensity, as well as the impact of eWOM propensity on the intention to repurchase the product. Two types of eWOM predictors were considered: product related variables and personal factors. The data were collected through an online survey conducted on a sample of 335 Romanian subjects, and the analysis
-
Moodle Usability Assessment Methodology using the Universal Design for Learning perspective arXiv.cs.CY Pub Date : 2024-03-15 Rosana Montes, Liliana Herrera, Emilio Crisol
The application of the Universal Design for Learning framework favors the creation of virtual educational environments for all. It requires developing accessible content, having a usable platform, and the use of flexible didactics and evaluations that promote constant student motivation. The present study aims to design a methodology to evaluate the usability of the Moodle platform based on the principles
-
Safety Cases: Justifying the Safety of Advanced AI Systems arXiv.cs.CY Pub Date : 2024-03-15 Joshua Clymer, Nick Gabrieli, David Krueger, Thomas Larsen
As AI systems become more advanced, companies and regulators will make difficult decisions about whether it is safe to train and deploy them. To prepare for these decisions, we investigate how developers could make a 'safety case,' which is a structured rationale that AI systems are unlikely to cause a catastrophe. We propose a framework for organizing a safety case and discuss four categories of arguments
-
AI-enhanced Collective Intelligence: The State of the Art and Prospects arXiv.cs.CY Pub Date : 2024-03-15 Hao Cui, Taha Yasseri
The current societal challenges exceed the capacity of human individual or collective effort alone. As AI evolves, its role within human collectives is poised to vary from an assistive tool to a participatory member. Humans and AI possess complementary capabilities that, when synergized, can achieve a level of collective intelligence that surpasses the collective capabilities of either humans or AI
-
Understanding Stress: A Web Interface for Mental Arithmetic Tasks in a Trier Social Stress Test arXiv.cs.CY Pub Date : 2024-03-15 Manjeet Yadav, Nilesh Kumar Sahu
Stress is a dynamic process that reflects the responses of the brain. Traditional methods for measuring stress are often time-consuming and susceptible to recall bias. To address this, we investigated changes in heart rate (HR) during the Trier Social Stress Test (TSST). Our study incorporated varying levels of complexity in mental arithmetic problems. Participants' HR increased during the Mental Arithmetic
-
Emotion-Aware Multimodal Fusion for Meme Emotion Detection arXiv.cs.CY Pub Date : 2024-03-15 Shivam Sharma, Ramaneswaran S, Md. Shad Akhtar, Tanmoy Chakraborty
The ever-evolving social media discourse has witnessed an overwhelming use of memes to express opinions or dissent. Besides being misused for spreading malcontent, they are mined by corporations and political parties to glean the public's opinion. Therefore, memes predominantly offer affect-enriched insights towards ascertaining the societal psyche. However, the current approaches are yet to model
-
Comparing Rationality Between Large Language Models and Humans: Insights and Open Questions arXiv.cs.CY Pub Date : 2024-03-14 Dana Alsagheer, Rabimba Karanjai, Nour Diallo, Weidong Shi, Yang Lu, Suha Beydoun, Qiaoning Zhang
This paper delves into the dynamic landscape of artificial intelligence, specifically focusing on the burgeoning prominence of large language models (LLMs). We underscore the pivotal role of Reinforcement Learning from Human Feedback (RLHF) in augmenting LLMs' rationality and decision-making prowess. By meticulously examining the intricate relationship between human interaction and LLM behavior, we
-
Predicting Generalization of AI Colonoscopy Models to Unseen Data arXiv.cs.CY Pub Date : 2024-03-14 Joel Shor, Carson McNeil, Yotam Intrator, Joseph R Ledsam, Hiro-o Yamano, Daisuke Tsurumaru, Hiroki Kayama, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Masaaki Miyo, Eiji Oki, Ichiro Takemasa, Ehud Rivlin, Roman Goldenberg
Background and aims Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. However, current techniques for evaluating performance on unseen data require expensive and time-intensive labels. Methods We use a "Masked Siamese Network" (MSN) to identify novel phenomena in unseen data and predict polyp detector performance. MSN is trained to predict masked out
-
Visualizing Progress in Broadening Participation in Computing: The Value of Context arXiv.cs.CY Pub Date : 2024-03-18 Valerie Barr, Carla E. Brodley, Manuel A. Pérez-Quiñones
Concerns about representation in computing within the U.S. have driven numerous activities to broaden participation. Assessment of the impact of these efforts and, indeed, a clear assessment of the actual "problem" being addressed are limited by the nature of the most common data analysis which looks at the representation of each population as a percentage of the number of students graduating with
-
AI for bureaucratic productivity: Measuring the potential of AI to help automate 143 million UK government transactions arXiv.cs.CY Pub Date : 2024-03-18 Vincent J. Straub, Youmna Hashem, Jonathan Bright, Satyam Bhagwanani, Deborah Morgan, John Francis, Saba Esnaashari, Helen Margetts
There is currently considerable excitement within government about the potential of artificial intelligence to improve public service productivity through the automation of complex but repetitive bureaucratic tasks, freeing up the time of skilled staff. Here, we explore the size of this opportunity, by mapping out the scale of citizen-facing bureaucratic decision-making procedures within UK central
-
Use of recommendation models to provide support to dyslexic students arXiv.cs.CY Pub Date : 2024-03-18 Gianluca Morciano, José Manuel Alcalde-Llergo, Andrea Zingoni, Enrique Yeguas-Bolivar, Juri Taborri, Giuseppe Calabrò
Dyslexia is the most widespread specific learning disorder and significantly impair different cognitive domains. This, in turn, negatively affects dyslexic students during their learning path. Therefore, specific support must be given to these students. In addition, such a support must be highly personalized, since the problems generated by the disorder can be very different from one to another. In
-
Synergy of Information in Multimodal IoT Systems -- Discovering the impact of daily behaviour routines on physical activity level arXiv.cs.CY Pub Date : 2024-03-17 Mohsen Shirali, Zahra Ahmadi, Carlos Fernández-Llatas, Jose-Luis Bayo-Monton
The intricate connection between daily behaviours and health necessitates robust behaviour monitoring, particularly with the advent of IoT systems. This study introduces an innovative approach, exploiting the synergy of information from various IoT sources, to assess the alignment of behaviour routines with health guidelines. We grouped routines based on guideline compliance and used a clustering method
-
Safeguarding Marketing Research: The Generation, Identification, and Mitigation of AI-Fabricated Disinformation arXiv.cs.CY Pub Date : 2024-03-17 Anirban Mukherjee
Generative AI has ushered in the ability to generate content that closely mimics human contributions, introducing an unprecedented threat: Deployed en masse, these models can be used to manipulate public opinion and distort perceptions, resulting in a decline in trust towards digital platforms. This study contributes to marketing literature and practice in three ways. First, it demonstrates the proficiency
-
Unlocking the Potential of Open Government Data: Exploring the Strategic, Technical, and Application Perspectives of High-Value Datasets Opening in Taiwan arXiv.cs.CY Pub Date : 2024-03-14 Hsien-Lee Tseng, Anastasija Nikiforova
Today, data has an unprecedented value as it forms the basis for data-driven decision-making, including serving as an input for AI models, where the latter is highly dependent on the availability of the data. However, availability of data in an open data format creates a little added value, where the value of these data, i.e., their relevance to the real needs of the end user, is key. This is where
-
Older adults' safety and security online: A post-pandemic exploration of attitudes and behaviors arXiv.cs.CY Pub Date : 2024-03-14 Edgar Pacheco
Older adults' growing use of the internet and related technologies, further accelerated by the COVID-19 pandemic, has prompted not only a critical examination of their behaviors and attitudes about online threats but also a greater understanding of the roles of specific characteristics within this population group. Based on survey data and using descriptive and inferential statistics, this empirical
-
MetroGNN: Metro Network Expansion with Reinforcement Learning arXiv.cs.CY Pub Date : 2024-03-14 Hongyuan Su, Yu Zheng, Jingtao Ding, Depeng Jin, Yong Li
Selecting urban regions for metro network expansion to meet maximal transportation demands is crucial for urban development, while computationally challenging to solve. The expansion process relies not only on complicated features like urban demographics and origin-destination (OD) flow but is also constrained by the existing metro network and urban geography. In this paper, we introduce a reinforcement
-
Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation arXiv.cs.CY Pub Date : 2024-03-14 Zainab Alalawi, Paolo Bova, Theodor Cimpeanu, Alessandro Di Stefano, Manh Hong Duong, Elias Fernandez Domingos, The Anh Han, Marcus Krellner, Bianca Ogbo, Simon T. Powers, Filippo Zimmaro
There is general agreement that some form of regulation is necessary both for AI creators to be incentivised to develop trustworthy systems, and for users to actually trust those systems. But there is much debate about what form these regulations should take and how they should be implemented. Most work in this area has been qualitative, and has not been able to make formal predictions. Here, we propose
-
An Extensive Comparison of Static Application Security Testing Tools arXiv.cs.CY Pub Date : 2024-03-14 Matteo Esposito, Valentina Falaschi, Davide Falessi
Context: Static Application Security Testing Tools (SASTTs) identify software vulnerabilities to support the security and reliability of software applications. Interestingly, several studies have suggested that alternative solutions may be more effective than SASTTs due to their tendency to generate false alarms, commonly referred to as low Precision. Aim: We aim to comprehensively evaluate SASTTs
-
Caveat Lector: Large Language Models in Legal Practice arXiv.cs.CY Pub Date : 2024-03-14 Eliza Mik
The current fascination with large language models, or LLMs, derives from the fact that many users lack the expertise to evaluate the quality of the generated text. LLMs may therefore appear more capable than they actually are. The dangerous combination of fluency and superficial plausibility leads to the temptation to trust the generated text and creates the risk of overreliance. Who would not trust
-
Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era arXiv.cs.CY Pub Date : 2024-03-13 Xuansheng Wu, Haiyan Zhao, Yaochen Zhu, Yucheng Shi, Fan Yang, Tianming Liu, Xiaoming Zhai, Wenlin Yao, Jundong Li, Mengnan Du, Ninghao Liu
Explainable AI (XAI) refers to techniques that provide human-understandable insights into the workings of AI models. Recently, the focus of XAI is being extended towards Large Language Models (LLMs) which are often criticized for their lack of transparency. This extension calls for a significant transformation in XAI methodologies because of two reasons. First, many existing XAI methods cannot be directly