Explainability as fig leaf? An exploration of experts’ ethical expectations towards machine learning in psychiatry

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As artificial intelligence (AI) becomes increasingly integrated into various aspects of daily life, the need for AI literacy continues to grow. Accordingly, both educators and students must develop a critical understanding of AI systems and acquire the ski ...

With recent innovations in artificial intelligence (AI), machines can now recognize architectural patterns and generate new forms in ways that far exceed human capacity. The integration of machines in the design process has ushered in a substantial shift i ...

In recent years, the rapid advancement of artificial intelligence (AI) technologies has significantly impacted various sectors, including public governance at the local level. However, there exists a limited understanding of the overarching narrative surro ...

Nonlinearity of dynamic systems plays a key role in neuromorphic computing, which is expected to reduce the ever-increasing power consumption of machine learning and artificial intelligence applications. For spin waves (magnons), nonlinearity combined with ...

Learning manipulation from demonstration is a key way for humans to teach complex tasks. However, this domain mainly focuses on kinetic teaching, and does not consider imitation of interaction forces which is essential for more contact rich tasks. We propo ...

Artificial intelligence (AI) is increasingly used in an ever larger number of industries. Alongside this development, however, abundant works argue that AI-driven systems are lacking in terms of safety, ethics and transparency. As a direct consequence, the ...

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

Graph-based representation approaches have been proven to be successful in the analysis of biomedical data due to their capability of capturing intricate dependencies between biological entities, such as the spatial organization of different cell types in ...

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

The use of machine learning for predicting ecotoxicological outcomes is promising, but underutilized. The curation of data with informative features requires both expertise in machine learning as well as a strong biological and ecotoxicological background, ...

Deepfakes - synthetic videos generated by machine learning models - are becoming increasingly sophisticated. While they have several positive use cases, their potential for harm is also high. Deepfake production involves input from multiple engineers, maki ...

The increasing integration of Machine Learning (ML) techniques into clinical care, driven in particular by Deep Learning (DL) using Artificial Neural Nets (ANNs), promises to reshape medical practice on various levels and across multiple medical fields. Mu ...

Aims: Understanding the correlations between underlying medical and personal characteristics of a patient with cancer and the risk of lung metastasis may improve clinical management and outcomes. We used machine learning methodologies to predict the risk o ...

While road obstacle detection techniques have become increasingly effective, they typically ignore the fact that, in practice, the apparent size of the obstacles decreases as their distance to the vehicle increases. In this letter, we account for this by c ...

This paper reports on the development of a brief scenario-based challenge to prompt engineering students’ reflection about the broader impacts of their design decisions, and thereby increase their ethical sensitivity and motivation. The game scenario asks ...

Ethical concerns in the digital domain are growing with the extremely fast evolution of technology and the increasing scale at which software is deployed, potentially affecting our societies globally. It is crucial that engineers evaluate more systematical ...

Abstractive summarization has seen big improvements in recent years, mostly due to advances in neural language modeling, language model pretraining, and scaling models and datasets. While large language models generate summaries that are fluent, coherent, ...

Amyloidosis refers to a range of medical conditions in which misshapen proteins accumulate in various organs and tissues, forming insoluble fibrils. Cardiac amyloidosis is frequently linked to the buildup of misfolded transthyretin (TTR) or immunoglobulin ...

Background: There has been an increase in interest in emotion in engineering and science ethics education. There is also evidence that emotional content in case studies may improve students’ learning and enhance awareness, understanding, and motivation con ...

Explainability as fig leaf? An exploration of experts’ ethical expectations towards machine learning in psychiatry

Chattez avec Graph Search

Teachers’ Perspectives on Using and Teaching Artificial Intelligence in Early Primary Education

Deep Architectural Form

Artificial intelligence and the local government: A five-decade scientometric analysis on the evolution, state-of-the-art, and emerging trends

Emergent coherent modes in nonlinear magnonic waveguides detected at ultrahigh frequency resolution

Learning Motion Reconstruction from Demonstration via Multi-Modal Soft Tactile Sensing

Voices from the Frontline: Revealing the AI Practitioners' viewpoint on the European AI Act

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Tertiary Lymphoid Structures Generation Through Graph-Based Diffusion

Machine learning-aided generative molecular design

A benchmark dataset for machine learning in ecotoxicology

A multimodal measurement of the impact of deepfakes on the ethical reasoning and affective reactions of students

Machine learning and its impact on psychiatric nosology: Findings from a qualitative study among German and Swiss experts

Medical and Personal Characteristics Can Predict the Risk of Lung Metastasis

Perspective Aware Road Obstacle Detection

DRONE GAME THAT HIGHLIGHTS ETHICAL AND SUSTAINABILITY IMPLICATIONS OF DESIGN DECISIONS

The Digital Ethics Canvas: A Guide For Ethical Risk Assessment And Mitigation In The Digital Domain

Interpretable Representation Learning and Evaluation for Abstractive Summarization

Classification of patients with cardiac amyloidosis using machine learning models on Italian electronic clinical health records

Compassion and Engineering Students’ Moral Reasoning: The Emotional Experience of Engineering Ethics Cases