A Fortiori Case-Based Reasoning

Artificial intelligence (AI) systems are increasingly being used in consumer as well and business applications. Companies are using them, Governments are using them, public security, medical researchers, you and I – everybody seems to be somehow touched. As it becomes more advanced and ubiquitous in our lives, it is critical that AI developers and users understand the ethical implications and challenges posed by this powerful technology. This paper explores the key ethical skills and considerations that are essential for navigating the new world of AI. Artificial Intelligence (AI) presents significant ethical dimensions across various sectors, including research, business, and daily life. It is important to appreciate the major ethical dimensions of AI and how they impact AI systems. Policies and guidelines needs to be laid out for development and usage of such systems. Automated Decisions/Responsibility: No matter how intelligent a AI system might perceived to be, it is always prone to making errors. So, AI system that make automated decisions will always be dangerous. Therefore, it is important to ensure that there is always "human intervention" or "human review" for outcomes/decisions made by AI systems. Determining accountability for AI-generated actions or content remains a significant challenge, emphasizing the need for ethical governance frameworks. There has to be human accountability.Fairness/Bias: AI systems can perpetuate existing biases present in training data, leading to discriminatory outcomes.Transparency: The opaque nature of many AI algorithms complicates accountability, making it difficult to understand decision-making processes.Privacy: AI's capability to process vast datasets can infringe on individual privacy rights, necessitating stringent data protection measuresHuman Dignity: The risk of AI-generated content resembling existing works raises ethical issues regarding originality and authorship.

The primary focus of this article is a general discussion of the theory and development of artificial intelligence (AI) systems and their expanding implications for human interactions and socioeconomic issues. We provide an overview of AI origins and the ubiquitous forms of AI that now saturate our culture, and we discuss the many ways AI is likely to continue to have a pervasive influence on every aspect of our lives. In a separate but directly related article in this issue, we provide an experimental analysis of a current version of an AI in the form of a deep neural network, and we describe how it is able to model and forecast human learning. Throughout this article, we attempt to link common elements in both articles. However, in this article, the emphasis is on a comparison of how cloud-based AI systems impact U.S. citizens and citizens of the European Union in terms of individual rights, security, equitable access, inadvertent machine discrimination, and corporate responsibilities. We note that although AI offers researchers throughout academic disciplines fascinating and previously unimagined explorations and critically important research possibilities, there are areas in which AI systems may be unscrupulously misused.

Although the categorization of ultrasound using the Breast Imaging Reporting and Data System (BI-RADS) has become widespread worldwide, the problem of inter-observer variability remains. To maintain uniformity in diagnostic accuracy, we have developed a novel artificial intelligence (AI) system in which AI can distinguish whether a static image obtained using a breast ultrasound represents BI-RADS3 or lower, or BI-RADS4a or higher, to determine the medical management that should be performed on a patient whose breast ultrasound shows abnormalities. To establish and validate the AI system, a training dataset consisting of 4,028 images containing 5,014 lesions and a test dataset consisting of 3,166 images containing 3,656 lesions were collected and annotated. We selected a setting that maximized the area under the curve (AUC) and minimized the difference in sensitivity and specificity by adjusting the internal parameters of the AI system, achieving an AUC, sensitivity, and specificity of 0.95, 90.0%, and 88.5%, respectively. Furthermore, based on 30 images extracted from the test data, the diagnostic accuracy of 20 clinicians and the AI system was compared, and the AI system was found to be significantly superior to the clinicians (McNemar test, p < 0.001). Then, we conducted a trial to introduce the system for use in clinical practice. Physicians reviewed the images and determined whether they were BI-RADS3 or lower, or BI-RADS4a or higher. Next, the classification was performed again for the same images concerning the AI diagnosis. At this time, the initial judgment was allowed to be overturned. We checked whether there was any difference in the diagnostic accuracy, sensitivity, and specificity before and after reviewing to the AI diagnosis. Reviews by 24 physicians were evaluated: 4 Japanese Breast Cancer Society breast specialists, 5 non-specialists and physicians with experience treating more than 40 cases of breast cancer, and 15 non-specialists and physicians with no experience treating more than 40 cases of breast cancer. The average rate of accuracy before confirming the AI diagnosis increased to 73.1% after confirming the AI diagnosis (p=0.00548), compared to 69.3% on average before the AI diagnosis. Compared to practice experience, the accuracy increased from an average of 77.1% to 79.6% for the 9 physicians who were breast specialists or who had treated 40 or more cases of breast cancer. For the 15 physicians with less than 40 breast cancer cases, the average rate of accuracy increased from 64.7% to 69.2%. Furthermore, sensitivity increased significantly to an average of 99.7% after reviewing of the AI diagnosis from an average of 88.8% prior to reviewing the AI-diagnosis.(p< 0.01). Specificity increased from an average of 62.4% to 63.8% (p=0.433) after reviewing AI diagnosis. We showed that our AI system, when applied to clinical practice and used by physicians, contributes to the improvement of diagnostic accuracy. Our results indicated that our AI diagnostic system was sufficiently accurate to be used in the clinical practice. Citation Format: Erina Odani, Tetsu Hayashida, masayuki kikuchi, Aiko Nagayama, tomoko seki, maiko takahashi, Akiko Matsumoto, Takeshi Murata, Rurina Watanuki, Takamichi Yokoe, Ayako Nakashoji, Hinako Maeda, Tatsuya Onishi, Sota Asaga, Takashi Hojo, Hiromitsu Jinno, Keiichi Sotome, Akira Matsui, Akihiko Suto, Shigeru Imoto, Yuko Kitagawa. Establishment of the breast ultrasound support system using deep-learning system [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-05-06.

To compare the morphological characteristics of full crowns designed by an experienced dental technician and a data-driven artificial intelligence (AI) system, with particular attention to their similarity to natural teeth. Twelve digital casts, including mandibular right second premolars, were collected, three-dimensional (3D) printed, and prepared by an experienced dentist. For each case, two crowns were independently designed by an experienced dental technician (ET) and the AI system. Morphological characteristics, including buccal and lingual cusp angle, buccal-lingual diameter, mesial-distal diameter, functional cusp wear facets, 3D similarity, and occlusal contact point counts, were compared among natural teeth, ET crowns, and AI crowns. Functional cusp wear and occlusal contacts were evaluated qualitatively, based on morphological observation and visual analysis under virtual articulating paper conditions. Statistical analysis was performed using one-way analysis of variance (ANOVA) followed by least significant difference (LSD) post-hoc testing, with the significance level set at α = 0.05. Both AI crowns (56.2° ± 5.1°) and ET crowns (57.5° ± 6.3°) exhibited larger buccal cusp angles than natural teeth (45.7° ± 4.9°) (p < 0.05), while no significant differences were found in lingual cusp angles among the groups (p = 0.434, 0.607, 0.787). The buccal-lingual diameter of AI crowns closely matched natural teeth (p = 0.094), whereas ET crowns were significantly smaller than both AI crowns (p = 0.034) and natural teeth (p = 0.044). The mesial-distal diameter of AI crowns also showed no significant difference from natural teeth (p = 0.223), while ET crowns differed significantly from both groups (p = 0.047, 0.002). Although AI crowns exhibited slightly lower 3D discrepancy (0.43 ± 0.07) than ET crowns (0.48 ± 0.06), the difference was not statistically significant (p = 0.089). AI did not accurately reproduce functional cusp wear facets, whereas technician designs tended to overcompensate. Occlusal contact counts showed no significant differences among groups. AI systems can reproduce overall crown morphology to a degree comparable with experienced technicians but remain limited in replicating functional wear facets. AI offers a practical and efficient tool for crown design, with potential to improve consistency and reduce manual workload in prosthodontics. However, further refinement is required before these systems can fully replicate functional morphological details for widespread clinical application.

Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study

We discuss how over the last 30 to 50 years, Artificial Intelligence (AI)\nsystems that focused only on data have been handicapped, and how knowledge has\nbeen critical in developing smarter, intelligent, and more effective systems.\nIn fact, the vast progress in AI can be viewed in terms of the three waves of\nAI as identified by DARPA. During the first wave, handcrafted knowledge has\nbeen at the center-piece, while during the second wave, the data-driven\napproaches supplanted knowledge. Now we see a strong role and resurgence of\nknowledge fueling major breakthroughs in the third wave of AI underpinning\nfuture intelligent systems as they attempt human-like decision making, and seek\nto become trusted assistants and companions for humans. We find a wider\navailability of knowledge created from diverse sources, using manual to\nautomated means both by repurposing as well as by extraction. Using knowledge\nwith statistical learning is becoming increasingly indispensable to help make\nAI systems more transparent and auditable. We will draw a parallel with the\nrole of knowledge and experience in human intelligence based on cognitive\nscience, and discuss emerging neuro-symbolic or hybrid AI systems in which\nknowledge is the critical enabler for combining capabilities of the\ndata-intensive statistical AI systems with those of symbolic AI systems,\nresulting in more capable AI systems that support more human-like intelligence.\n

This chapter explores relationships between the science and profession of psychology and the development of artificial intelligence (AI) and robotic systems. It briefly examines the role of psychology in the development of AI systems and theories. There is a long and intimate history of such a relationship, exemplified in the arts and the social sciences as well as in computer science and engineering. The chapter examines the role that psychology has played in the understanding of the ways in which humans interact with contemporary AI systems, to influence the behaviour of the systems and, in turn, to be influenced by them. Human decision making has been shown to be improved by factors other than intelligence and thereby appears to be a quality which would be retained by humans rather than machines, for example motivational processes, emotions and experience. The chapter examines the state of the profession of psychology and helping professions in the light of developments in AI.

Artificial Intelligence (AI) is revolutionizing the accounting profession, offering transformative capabilities for automating tasks, enhancing decision-making, and improving financial accuracy. As AI becomes integral to accounting practices, it brings both significant opportunities and notable ethical challenges. This review examines the intersection of AI and ethical accounting, providing insights into how professionals can navigate the evolving landscape. The adoption of AI in accounting introduces opportunities for increased efficiency and accuracy. AI systems can handle repetitive tasks such as data entry, reconciliation, and transaction categorization, freeing accountants to focus on strategic activities. Advanced AI algorithms can analyze large volumes of financial data to identify patterns, detect anomalies, and provide real-time insights, enhancing decision-making and forecasting accuracy. Moreover, AI-driven predictive analytics can aid in risk assessment and management, helping organizations to anticipate and mitigate potential financial threats. However, the integration of AI in accounting also raises significant ethical concerns. One of the primary challenges is ensuring transparency and accountability in AI decision-making processes. As AI systems often operate as "black boxes," understanding and explaining their outputs can be difficult, potentially leading to issues of trust and compliance. Ethical accounting necessitates that AI systems be designed with transparency in mind, providing clear explanations for their decisions and actions. Data privacy and security represent another critical ethical consideration. The extensive use of financial data by AI systems necessitates robust measures to protect sensitive information from breaches and unauthorized access. Accountants must ensure that AI systems comply with data protection regulations and ethical standards, safeguarding the confidentiality and integrity of financial data. Bias and fairness in AI algorithms are also pressing ethical issues. If not properly addressed, biases in AI systems can lead to unfair outcomes, such as biased financial recommendations or discriminatory practices. Ensuring fairness requires ongoing monitoring and evaluation of AI systems to identify and mitigate biases. In conclusion, while AI offers substantial benefits for the accounting profession, it also presents ethical challenges that must be carefully managed. Accountants must navigate these challenges by promoting transparency, ensuring data privacy and security, and addressing biases in AI systems. By doing so, the accounting profession can harness the potential of AI while upholding ethical standards and maintaining public trust. Keywords: AI, Ethical Accounting, Navigating, Challenges, Opportunities.

As artificial intelligence (AI) systems continue to be developed, humans will increasingly participate in human-AI interactions. Humans interact with AI systems to achieve particular goals. To ensure that AI systems contribute positively to human-AI interactions, it is important to examine human-AI tasks with an emphasis on human goals and outcomes. The AI Use Taxonomy aims to provide a flexible means of classifying how an AI system contributes to an outcome. The taxonomy sets forward 16 AI use “activities” which are independent of AI techniques and domains. Tasks are combinations of one or more AI use activities. Future research includes applying the taxonomy to better understand measurement challenges for each activity. The taxonomy can contribute to an improved understanding of the architecture of human-AI tasks and help to foster positive, human-centered interactions with AI systems and optimal outcomes in the following ways: • Provides common terminology for describing outcome-based human-AI activities independent of AI techniques and domains • Enables cross-domain insights based on shared human-AI activities • Highlights commonalities in measurement and evaluation needs across disparate AI techniques • Facilitates the development of use cases • Facilitates the evaluation of trustworthiness characteristics and usability.

The quest to develop Artificial Intelligence (AI) systems that possess human-like consciousness necessitates a deep dive into both theoretical and practical aspects underpinning this ambitious goal. This article builds on initial philosophical explorations of AI consciousness by examining the intricate and often hidden structures that may facilitate conscious experiences in AI. Drawing from concepts in cognitive science and neuroscience, the article elucidates how AI systems can be designed to replicate the structural and functional aspects of human consciousness. The discussion includes the Hierarchy of Spatial Belongings proposed by Forti (2024), frameworks like the Integrated Information Theory (IIT), and models linking consciousness with metacognitive processes. Through case studies of advanced AI systems such as IBM Watson, AlphaGo, GPT-3, and Sophia the Robot, the article explores practical implementations and their alignment with theoretical models of consciousness. The potential for AI to achieve states analogous to human consciousness raises profound ethical, societal, and legal considerations. Ethical guidelines and legal frameworks are urgently needed to address the moral status and rights of conscious AI systems, ensure their ethical treatment, and delineate accountability. The societal impacts of conscious AI, including job displacement and the need for equitable access to AI technologies, are also examined. Future research directions highlight the necessity for developing sophisticated theoretical models, enhancing practical implementations, establishing comprehensive ethical frameworks, fostering interdisciplinary collaboration, and engaging the public. By addressing these areas, the scientific community can significantly advance the development of conscious AI, ensuring it is both technically feasible and ethically sound

The use of artificial intelligence (AI) systems is significantly increased in the past few years. AI system is expected to provide accurate predictions and it is also crucial that the decisions made by the AI systems are humanly interpretable i.e. anyone must be able to understand and comprehend the results produced by the AI system. AI systems are being implemented even for simple decision support and are easily accessible to the common man on the tip of their fingers. The increase in usage of AI has come with its own limitation, i.e. its interpretability. This work contributes towards the use of explainability methods such as local interpretable model-agnostic explanations (LIME) to interpret the results of various black box models. The conclusion is that, the bidirectional long short-term memory (LSTM) model is superior for sentiment analysis. The operations of a random forest classifier, a black box model, using explainable artificial intelligence (XAI) techniques like LIME is used in this work. The features used by the random forest model for classification are not entirely correct. The use of LIME made this possible. The proposed model can be used to enhance performance, which raises the trustworthiness and legitimacy of AI systems.

Introduction The potential of AI systems to support pre-hospital clinical decision-making is significant. However, whilst such algorithms are increasingly available, far less attention has been paid to understanding the impact of such systems on clinical performance. This study had two aims; first, to compare the performance of expert clinicians against an AI system in a real-world clinical setting and second, to assess the impact of augmenting expert clinical prediction with an AI system. Method We performed a prospective study at two UK Air Ambulances services over a six-month period. Expert pre-hospital clinicians’ judgement of the risk of Trauma Induced Coagulopathy (TIC) in injured patients was assessed and compared to the performance of an AI system. Two TIC risk predictions were generated for every patient: an AI prediction and a human prediction. Results Overall, 51 expert clinicians were enrolled in the study providing 184 patient interactions for analysis. Aim 1 The AI system performed better than clinicians; higher discrimination [AUROC 0.87 (0.79, 0.95) versus 0.83 (0.74, 0.92)] better calibration [0.37 (-0.14, 0.89) versus -1.19 (-1.73, -0.65)] and more accurate [Brier Skill Score 0.34 (0.19, 0.48) versus 0.00 (-0.41, 0.30)]. Aim 2 Risk prediction was better in all performance metrics when clinicians were assisted with the AI system [AUROC 0.88 (0.80, 0.95) versus 0.83 (0.74, 0.92)] Conclusions AI systems can improve human risk prediction in the pre-hospital setting. In settings of low resources where a lack of senior clinical expertise may affect outcomes, the benefit of implementing predictive AI is substantial.

The framework of Input and Output Privacy aids in conceptualization of data privacy protections, providing considerations for situations where multiple parties are collaborating in a compute system (Input Privacy) as well as considerations when releasing data from a compute process (Output Privacy). Similar frameworks for conceptualization of privacy protections at a systems design level are lacking within the Artificial Intelligence space, which can lead to mischaracterizations and incorrect implementations of privacy protections. In this paper, we apply the Input and Output Privacy framework to Artificial Intelligence (AI) systems, establishing parallels between traditional data systems and newer AI systems to help privacy professionals and AI developers and deployers conceptualize and determine the places in those systems where privacy protections have the greatest effect. We discuss why the Input and Output Privacy framework is useful when evaluating privacy protections for AI systems, examine the similarities and differences of Input and Output privacy between traditional data systems and AI systems, and provide considerations on how to protect Input and Output Privacy for systems utilizing AI models. This framework offers developers and deployers of AI systems common ground for conceptualizing where and how privacy protections can be applied in their systems and for minimizing risk of misaligned implementations of privacy protection.

Problem (Introduction). The rapid integration of generative artificial intelligence (AI) into education has created unprecedented opportunities for personalised learning, yet it has also raised serious concerns about the reliability of AI-generated content. Large language models (LLMs) optimise for plausibility rather than truth, and they can fabricate facts, citations or even legal cases (When AI Gets It Wrong: Addressing AI Hallucinations and Bias - MIT Sloan Teaching &amp; Learning Technologies, n.d.). Such hallucinations threaten academic integrity: students may unknowingly absorb falsehoods, while teachers could inadvertently reproduce inaccuracies in course materials. Empirical studies reveal that AI systems can hallucinate from less than 1 % up to 15–40 % of cases in educational tasks depending on the model and domain (Figure 1), and systematic reviews note that over‑reliance on AI dialogue systems is linked to diminished critical thinking, increased technology dependence and the spread of misinformation (Zhai et al., 2024). Purpose. This article aims to analyse the scope and causes of AI-generated misinformation in education and to develop evidence-based recommendations for mitigating these risks. It combines technical insights on model architecture and training data with pedagogical strategies to foster AI literacy. The goal is to ensure that AI enhances rather than undermines learning. Methods. A systematic literature review of over 80 sources, including scientific articles, policy documents (AI Act, UNESCO guidelines), and empirical studies, provided a theoretical foundation. Comparative analysis of hallucination rates across models (Makhno et al., 2025; Lelièvre et al., 2025) informed the quantitative assessment. The study also modelled mitigation strategies such as Retrieval‑Augmented Generation (RAG) and Chain‑of‑Verification and evaluated pedagogical interventions like lateral reading and AI literacy programmes. Results. The findings show that hallucinations stem from both internal (model architecture and context limitations) and external (biased or incomplete training data) factors. Even top models misinform 1–3% of the time, whereas widely used free systems can err 15–40% of the time when generating bibliographies or research proposals (Balch &amp; Blanck, 2024). Hallucinations manifest in various forms: logical errors, mathematical mistakes, fabricated sources and factual inaccuracies. Their educational consequences include decreased critical thinking, increased plagiarism (“AI‑giarism”) and a risk of spreading disinformation. Regulatory frameworks classify educational AI systems as high‑risk; Annex III of the EU AI Act lists educational AI systems for admissions, assessment and monitoring as high‑risk and sets obligations for accuracy, transparency and human oversight (Nguyen, 2025). Among mitigation strategies, RAG reduces hallucinations, while Chain‑of‑Verification and self‑consistency improve reliability. Pedagogically, teaching students lateral reading, updating academic policies, and redesigning assessments to require reflection and verification are essential. Originality. Unlike purely technical surveys or broad commentaries, this study bridges AI research with educational practice and policy, providing a holistic perspective tailored to Ukrainian higher education. It synthesises international findings with local realities, offers a taxonomy of AI errors, presents original visualisations (Table 1 and Figure 1), and proposes a multi-level framework combining technical, pedagogical and regulatory solutions. The article emphasises that AI hallucinations are not simply technical bugs but systemic challenges requiring cultural change. Conclusion. To harness AI’s benefits in education, stakeholders must recognise and mitigate the problem of misinformation. Improving models (via RAG, verification chains), enhancing AI literacy, and adhering to high-risk regulatory standards will help ensure that AI supports, rather than sabotages, learning. Future research should focus on domain-specific hallucination rates, real-time fact-checkers for Ukrainian-language content, and longitudinal studies on AI’s cognitive impact. Ultimately, balancing technological innovation with human oversight and ethical principles will determine whether AI becomes a trustworthy educational ally or a source of confusion.

To investigate how an artificial intelligence (AI) system performs at digital mammography (DM) from a screening population with ground truth defined by digital breast tomosynthesis (DBT), and whether AI could detect breast cancers at DM that had originally only been detected at DBT. In this secondary analysis of data from a prospective study, DM examinations from 14 768 women (mean age, 57 years), examined with both DM and DBT with independent double reading in the Malmӧ Breast Tomosynthesis Screening Trial (MBTST) (ClinicalTrials.gov: NCT01091545; data collection, 2010-2015), were analyzed with an AI system. Of 136 screening-detected cancers, 95 cancers were detected at DM and 41 cancers were detected only at DBT. The system identifies suspicious areas in the image, scored 1-100, and provides a risk score of 1 to 10 for the whole examination. A cancer was defined as AI detected if the cancer lesion was correctly localized and scored at least 62 (threshold determined by the AI system developers), therefore resulting in the highest examination risk score of 10. Data were analyzed with descriptive statistics, and detection performance was analyzed with receiver operating characteristics. The highest examination risk score was assigned to 10% (1493 of 14 786) of the examinations. With 90.8% specificity, the AI system detected 75% (71 of 95) of the DM-detected cancers and 44% (18 of 41) of cancers at DM that had originally been detected only at DBT. The majority were invasive cancers (17 of 18). Almost half of the additional DBT-only screening-detected cancers in the MBTST were detected at DM with AI. AI did not reach double reading performance; however, if combined with double reading, AI has the potential to achieve a substantial portion of the benefit of DBT screening.Keywords: Computer-aided Diagnosis, Mammography, Breast, Diagnosis, Classification, Application DomainClinical trial registration no. NCT01091545© RSNA, 2021.