AI Research Research Articles

Enhancing AI Research for Breast Cancer: A Comprehensive Review of Tumor-Infiltrating Lymphocyte Datasets.

The field of immunology is fundamental to our understanding of the intricate dynamics of the tumor microenvironment. In particular, tumor-infiltrating lymphocyte (TIL) assessment emerges as essential aspect in breast cancer cases. To gain comprehensive insights, the quantification of TILs through computer-assisted pathology (CAP) tools has become a prominent approach, employing advanced artificial intelligence models based on deep learning techniques. The successful recognition of TILs requires the models to be trained, a process that demands access to annotated datasets. Unfortunately, this task is hampered not only by the scarcity of such datasets, but also by the time-consuming nature of the annotation phase required to create them. Our review endeavors to examine publicly accessible datasets pertaining to the TIL domain and thereby become a valuable resource for the TIL community. The overall aim of the present review is thus to make it easier to train and validate current and upcoming CAP tools for TIL assessment by inspecting and evaluating existing publicly available online datasets.

REAL-Colon: A dataset for developing real-world AI applications in colonoscopy

Detection and diagnosis of colon polyps are key to preventing colorectal cancer. Recent evidence suggests that AI-based computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems can enhance endoscopists' performance and boost colonoscopy effectiveness. However, most available public datasets primarily consist of still images or video clips, often at a down-sampled resolution, and do not accurately represent real-world colonoscopy procedures. We introduce the REAL-Colon (Real-world multi-center Endoscopy Annotated video Library) dataset: a compilation of 2.7 M native video frames from sixty full-resolution, real-world colonoscopy recordings across multiple centers. The dataset contains 350k bounding-box annotations, each created under the supervision of expert gastroenterologists. Comprehensive patient clinical data, colonoscopy acquisition information, and polyp histopathological information are also included in each video. With its unprecedented size, quality, and heterogeneity, the REAL-Colon dataset is a unique resource for researchers and developers aiming to advance AI research in colonoscopy. Its openness and transparency facilitate rigorous and reproducible research, fostering the development and benchmarking of more accurate and reliable colonoscopy-related algorithms and models.

Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines.

This article describes the Cell Maps for Artificial Intelligence (CM4AI) project and its goals, methods, standards, current datasets, software tools , status, and future directions. CM4AI is the Functional Genomics Data Generation Project in the U.S. National Institute of Health's (NIH) Bridge2AI program. Its overarching mission is to produce ethical, AI-ready datasets of cell architecture, inferred from multimodal data collected for human cell lines, to enable transformative biomedical AI research.

Now, Later, and Lasting: 10 Priorities for AI Research, Policy, and Practice

Shaping the future of artificial intelligence.

The Impacts of Text-to-Image Generative AI on Creative Professionals According to Prospective Generative AI Researchers: Insights from Japan

The growing interest in Japan to implement text-to-image (T2I) generative artificial intelligence (GenAI) technologies in creative workflows has raised concern over what ethical and social implications these technologies will have on creative professionals. Our pilot study is the first to discuss what social and ethical oversights may emerge regarding such issues from prospective Japanese researchers – computer science (CS) graduate students studying in Japan. Given that these students are the primary demographic hired to work at research and development (R&D) labs at the forefront of such innovations in Japan, any social and ethical oversight on such issues may unequip them as future knowledge experts who will play a pivotal role in helping shape Japan’s policies regarding image generating AI technologies.

The extimate core of understanding: absolute metaphors, psychosis and large language models

AbstractThis paper delves into the striking parallels between the linguistic patterns of Large Language Models (LLMs) and the concepts of psychosis in Lacanian psychoanalysis. Lacanian theory, with its focus on the formal and logical underpinnings of psychosis, provides a compelling lens to juxtapose human cognition and AI mechanisms. LLMs, such as GPT-4, appear to replicate the intricate metaphorical and metonymical frameworks inherent in human language. Although grounded in mathematical logic and probabilistic analysis, the outputs of LLMs echo the nuanced linguistic associations found in metaphor and metonymy, suggesting a mirroring of human linguistic structures. A pivotal point in this discourse is the exploration of “absolute metaphors”—core gaps in reasoning discernible in both AI models and human thought processes and central to the Lacanian conceptualization of psychosis. Despite the traditional divide between AI research and continental philosophy, this analysis embarks on an innovative journey, utilizing Lacanian philosophy to unravel the logic of AI, using concepts established in the continental discourse on logic, rather than the analytical tradition.

Revisiting Aristotle’s Master-Slave Relationship: A Casual Evaluation in the Context of Human-AI Dynamics

This paper sought to explore Aristotle's handling of the notion of natural slavery and its relevance in ongoing discussions concerning the ethics of artificial intelligence. Using an extensive literature review covering his discussions on slavery in the Politics and the Nicomachean Ethics, this study delved into the philosophical underpinnings of slavery within Aristotle's political theory and examines its ramifications within the domain of ethical technology. Consequently, while Aristotle's theory of natural slavery suggests that certain individuals are predisposed to service due to deficiencies in rational capacity, this concept was analysed in the context of modern AI technology, raising ethical concerns about autonomy, control and social hierarchy in human AI interactions. By relating Aristotle's philosophical framework to contemporary AI ethics, this study offered an understanding of the ethical implications of advancing technology and encouraged interdisciplinary dialogue between philosophy, ethics and AI research.

2760: Data-centric AI and cancer research: constructing a research data access pipeline using XNAT

2760: Data-centric AI and cancer research: constructing a research data access pipeline using XNAT

Why do we need to employ exemplars in moral education? Insights from recent advances in research on artificial intelligence

ABSTRACT In this paper, I examine why moral exemplars are useful and even necessary in moral education despite several critiques. To support my point, I review recent AI research demonstrating that exemplar-based learning is superior to rule-based learning in model performance in training neural networks, such as large language models. I particularly focus on why education aiming at promoting the development of multifaceted moral functioning can be done effectively by using exemplars, which is like exemplar-based learning in AI model training. Furthermore, I discuss the potential limitations and issues related to exemplar-applied moral education with findings from recent AI research raising concerns about model biases and toxic outcomes. I propose ways to address the concerns regarding employing moral exemplars as well. As remedies, I suggest that autonomy-supporting deliberative and reflective learning should be utilized. Furthermore, based on the discussion, I examine how macroscopic socio-cultural aspects influence the effectiveness of exemplar-applied education.

Current Status and Emerging Trends of Generative Artificial Intelligence Technology: A Bibliometric Analysis

<>With the widespread application of ChatGPT (Chat Generative Pre-trained Transformer), its superordinate concept, generative artificial intelligence (Gen AI), has received increasing attention from researchers. The current study attempted to explore the current status and emerging trends of Gen AI technology research by visualizing the relevant published articles using the Biblioshiny tool. A total of 1,902 academic articles in the Web of Science (WOS) database published between 2014-2022 were analyzed. Annual publications, most productive journals and countries, co-authors, co-occurring keywords, document co-citations, and emerging trends of Gen AI research were analyzed. The following are the main findings of the study: the current status of Gen AI research is reflected in the following aspects: (1) the volume of documents produced is increasing year on year; (2) the publication of practical applications such as ChatGPT has brought a high level of interest to related research exploring the application of Gen AI in various research fields, such as education, medicine, drug discovery, and so on; and (3) two influential co-citation clusters have been formed. The emerging trends of the application of Gen AI research are also summarized as follows: (1) Gen AI has powerful technical advantages; (2) the application of Gen AI has great potential in the field of medicine, education and so on for the future; and (3) the updating and development of relevant technologies will always be the focus of Gen AI research.<>

Analysis of the benefits of artificial intelligence and human personality study on online fraud detection

Purpose Technology advancement has changed how banks operate. Modernizing technology has, on the one hand, made it simpler for banks to do their daily business, but it has also increased cyberattacks. The purpose of the study is to to determine the factors that have the most effects on online fraud detection and to evaluate the advantages of AI and human psychology research in preventing online transaction fraud. Artificial intelligence has been used to create new techniques for both detecting and preventing cybercrimes. Fraud has also been facilitated in some organizations via employee participation. Design/methodology/approach The main objective of the research approach is to guide the researcher at every stage to realize the main objectives of the study. This quantitative study used a survey-based methodology. Because it allows for both unbiased analysis of the relationship between components and prediction, a quantitative approach was adopted. The study of the body of literature, the design of research questions and the development of instruments and procedures for data collection, analysis and modeling are all part of the research process. The study evaluated the data using Matlab and a structured model analysis method. For reliability analysis and descriptive statistics, IBM SPSS Statistics was used. Reliability and validity were assessed using the measurement model, and the postulated relationship was investigated using the structural model. Findings There is a risk in scaling at a fast pace, 3D secure is used payer authentication has a maximum mean of 3.830 with SD of 0.7587 and 0.7638, and (CE2). Originality/value This study focused on investigating the benefits of artificial intelligence and human personality study in online transaction fraud and to determine the factors that affect something most strongly on online fraud detection. Artificial intelligence and human personality in the Indian banking industry have been emphasized by the current research. The study revealed the benefits of artificial intelligence and human personality like awareness, subjective norms, faster and more efficient detection and cost-effectiveness significantly impact (accept) online fraud detection in the Indian banking industry. Also, security measures and better prediction do not significantly impact (reject) online fraud detection in the Indian banking industry.

Tracing Class and Capital in Critical AI Research

This article explores the rapidly developing field of Critical AI Studies and its relation to issues of class and capitalism through a hybrid approach based on distant reading of a newly collected corpus of 300 full-text scientific articles, the creation of which is itself a first attempt at properly delineating the field. We find that words related to issues of class are predominantly but not exclusively confined to a set of studies that make up their own distinct subfield of Critical AI Studies, in contrast to, e.g., issues of race and gender, which are more broadly present in the corpus.

Green and sustainable AI research: an integrated thematic and topic modeling analysis

This investigation delves into Green AI and Sustainable AI literature through a dual-analytical approach, combining thematic analysis with BERTopic modeling to reveal both broad thematic clusters and nuanced emerging topics. It identifies three major thematic clusters: (1) Responsible AI for Sustainable Development, focusing on integrating sustainability and ethics within AI technologies; (2) Advancements in Green AI for Energy Optimization, centering on energy efficiency; and (3) Big Data-Driven Computational Advances, emphasizing AI’s influence on socio-economic and environmental aspects. Concurrently, BERTopic modeling uncovers five emerging topics: Ethical Eco-Intelligence, Sustainable Neural Computing, Ethical Healthcare Intelligence, AI Learning Quest, and Cognitive AI Innovation, indicating a trend toward embedding ethical and sustainability considerations into AI research. The study reveals novel intersections between Sustainable and Ethical AI and Green Computing, indicating significant research trends and identifying Ethical Healthcare Intelligence and AI Learning Quest as evolving areas within AI’s socio-economic and societal impacts. The study advocates for a unified approach to innovation in AI, promoting environmental sustainability and ethical integrity to foster responsible AI development. This aligns with the Sustainable Development Goals, emphasizing the need for ecological balance, societal welfare, and responsible innovation. This refined focus underscores the critical need for integrating ethical and environmental considerations into the AI development lifecycle, offering insights for future research directions and policy interventions.

Is Artificial Intelligence Gender-Free? What Does Feminist Epistemology Say About That?

I start my contribution with some general questions: Is AI gendered? Is AI sexist or can it be? Does AI include gendered knowledge and suppositions? If so, how? After that, I proceed to develop my theoretical (i.e., qualitatively based) starting points with the main referential authors, Donna Haraway and Alison Adam. The fact that impersonal does not mean observer-independent (as Haraway described it in a slightly different context [1997]) is a good reason to turn to feminist epistemology, especially its concept of situated knowledges. Since knowledge (or the representation of knowledge) lies at the very centre of AI research, this makes it an appropriate vehicle for a gendered critique of AI (Adam, 2000). The concept of situated knowledges entails knowledge that reflects a perspective on a subject which is necessarily partial and limited, not universal (this is Haraway’s famous critique of the “view from nowhere”). Namely, there is no way to be simultaneously in all of the epistemologically privileged positions structured by gender, class, nation, etc. I then proceed to AI research. The knowledge engineers build systems that contain knowledge reflecting their own interests and competencies. While this representation of knowledge is usually regarded as being universal (Adam, 2000), it is hierarchical since it does not grant epistemic authority to all. Most importantly, social exclusivism and biological essentialism are re-inscribed in the ontology of AI (Adam, 2000). I address the question of which effects social and political contaminations and prejudices can bring for the development of AI. I suggest that unless we commit to deconstructing the harmful essentialisms that govern our human lives, we might just be perpetuating the same (i.e., our own and others’) practices of domination and unequal parts of privilege and oppression (Haraway, 1991) in developing AI.

Missed Opportunities for Human-Centered AI Research: Understanding Stakeholder Collaboration in Mental Health AI Research

In the mental health domain, patient engagement is key to designing human-centered technologies. CSCW and HCI researchers have delved into various facets of collaboration in AI research; however, previous research neglects the individuals who both produce the data and will be most impacted by the resulting technologies, such as patients. This study examines how interdisciplinary researchers and mental health patients who donate their data for AI research collaborate and how we can improve human-centeredness in mental health AI research. We interviewed patient participants, AI researchers, and clinical researchers in a federally funded mental health AI research project. We used the concept of boundary objects to understand stakeholder collaboration. Our findings reveal that the social media data provided by patient participants functioned as boundary objects that facilitated stakeholder collaboration. Although the collaboration appeared to be successful, we argue that building consensus, or understanding each other's perspectives, can improve the human-centeredness of mental health AI research. Based on the findings, we provide suggestions for human-centered mental health AI research, working with data donors as domain experts, making invisible work visible, and privacy implications.

Emerging leaders or persistent gaps? Generative AI research may foster women in STEM

The primary aim of this study is to explore the gender dynamics within Generative AI (GAI) research through an analysis of 5092 publications post the inception of ChatGPT in November 2022. This investigation seeks to comprehend how gender distribution varies across different research areas and geographical locations in GAI, assess thematic differences in contributions by male and female authors, identify areas of significant female activity, evaluate the impact of policies on gender dynamics, devise strategies to enhance gender diversity and explore how GAI research can propel women's advancement in STEM. The study reveals a dual narrative: a promising rise in female leadership within highly cited GAI research papers, juxtaposed with ongoing gender disparities in single-authored works and primary authorship positions. These findings underscore the unique position of GAI as distinct from traditional STEM fields, owing to its integration of technology with societal demands and its potential to foster increased female participation in STEM through its interdisciplinary approach and societal impact. By aligning with Sustainable Development Goal 5, this research champions inclusive development practices and policy reforms aimed at bolstering gender diversity and inclusivity within GAI research. The insights derived from addressing the research questions show how GAI can make STEM fields more inclusive, diverse, and attuned to societal needs, thereby maximizing the value of female researchers' contributions.

Mindful Explanations: Prevalence and Impact of Mind Attribution in XAI Research

When users perceive AI systems as mindful, independent agents, they hold them responsible instead of the AI experts who created and designed these systems. So far, it has not been studied whether explanations support this shift in responsibility through the use of mind-attributing verbs like "to think". To better understand the prevalence of mind-attributing explanations we analyse AI explanations in 3,533 explainable AI (XAI) research articles from the Semantic Scholar Open Research Corpus (S2ORC). Using methods from semantic shift detection, we identify three dominant types of mind attribution: (1) metaphorical (e.g. "to learn" or "to predict"), (2) awareness (e.g. "to consider"), and (3) agency (e.g. "to make decisions"). We then analyse the impact of mind-attributing explanations on awareness and responsibility in a vignette-based experiment with 199 participants. We find that participants who were given a mind-attributing explanation were more likely to rate the AI system as aware of the harm it caused. Moreover, the mind-attributing explanation had a responsibility-concealing effect: Considering the AI experts' involvement lead to reduced ratings of AI responsibility for participants who were given a non-mind-attributing or no explanation. In contrast, participants who read the mind-attributing explanation still held the AI system responsible despite considering the AI experts' involvement. Taken together, our work underlines the need to carefully phrase explanations about AI systems in scientific writing to reduce mind attribution and clearly communicate human responsibility.

An Approach towards to Real Time Face Detection using Haar Cascade Algorithm and Machine Learning Technique

Human visual awareness is currently a trending subject within the realm of machine vision. In domains like video monitoring, human-machine interaction, facial identification, and image organization, the initial step often involves locating and recognizing human faces. However, this task can prove challenging. Despite the availability of generic facial images, the processes of facial recognition and expression analysis are necessary. The exploration of computer-based recognition of unbiased facial data remains a relatively uncharted area of study. Facial recognition stands out as one of the remarkable achievements of AI research, captivating the interest of many tech enthusiasts eager to comprehend its underlying mechanisms. Let's delve deeper into this topic to gain insight into its functioning. This study elucidates how deep learning, a crucial component of computer science, can be employed to detect faces using various libraries within OpenCV and Python. The article will propose a cutting-edge technology for real-time human face detection, applicable across a spectrum of platforms including computers, smartphones, and software applications. This approach proves both efficient and effective in detecting faces within images. Furthermore, the article delves into popular OpenCV applications and classifiers utilized in tasks such as image manipulation, facial identification, object detection, and facial recognition. Lastly, various literary assessments are discussed, focusing on applications within computer vision disciplines like facial detection and recognition, as well as identifying emotions such as sorrow, anger, and joy, along with determining gender and age

Introduction for the special issue of “Ideologies of AI and the consolidation of power”: Naming power

This introductory essay for the special issue of First Monday, “Ideologies of AI and the consolidation of power,” considers how power operates in AI and machine learning research and publication. Drawing on themes from the seven contributions to this special issue, we argue that what can and cannot be said inside of mainstream computer science publications appears to be constrained by the power, wealth, and ideology of a small cohort of industrialists. The result is that shaping discourse about the AI industry is itself a form of power that cannot be named inside of computer science. We argue that naming and grappling with this power, and the troubled history of core commitments behind the pursuit of general artificial intelligence, is necessary for the integrity of the field and the well-being of the people whose lives are impacted by AI.

A noise audit of human-labeled benchmarks for machine commonsense reasoning

With the advent of large language models, evaluating and benchmarking these systems on important AI problems has taken on newfound importance. Such benchmarking typically involves comparing the predictions of a system against human labels (or a single ‘ground-truth’). However, much recent work in psychology has suggested that most tasks involving significant human judgment can have non-trivial degrees of noise. In his book, Kahneman suggests that noise may be a much more significant component of inaccuracy compared to bias, which has been studied more extensively in the AI community. This article proposes a detailed noise audit of human-labeled benchmarks in machine commonsense reasoning, an important current area of AI research. We conduct noise audits under two important experimental conditions: one in a smaller-scale but higher-quality labeling setting, and another in a larger-scale, more realistic online crowdsourced setting. Using Kahneman’s framework of noise, our results consistently show non-trivial amounts of level, pattern, and system noise, even in the higher-quality setting, with comparable results in the crowdsourced setting. We find that noise can significantly influence the performance estimates that we obtain of commonsense reasoning systems, even if the ‘system’ is a human; in some cases, by almost 10 percent. Labeling noise also affects performance estimates of systems like ChatGPT by more than 4 percent. Our results suggest that the default practice in the AI community of assuming and using a ‘single’ ground-truth, even on problems requiring seemingly straightforward human judgment, may warrant empirical and methodological re-visiting.