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As artificial intelligence (AI) further embeds itself into many settings across personal and professional contexts, increasing attention must be paid not only to AI ethics, but also to the governance and regulation of AI technologies…

Is it possible to evaluate the moral cognition of complex artificial agents? In this work, we take a look at one aspect of morality: `doing the right thing for the right reasons.' We propose a behavior-based analysis of artificial moral…

With the rapid growth of Large Language Models (LLMs), criticism of their societal impact has also grown. Work in Responsible AI (RAI) has focused on the development of AI systems aimed at reducing harm. Responding to RAI's criticisms and…

The problem of corrupted data, missing features, or missing modalities continues to plague the modern machine learning landscape. To address this issue, a class of regularization methods that enforce consistency between imputed and fully…

Significant work has been done on learning regular expressions from a set of data values. Depending on the domain, this approach can be very successful. However, significant time is required to learn these expressions and the resulting…

The AI landscape demands a broad set of legal, ethical, and societal considerations to be accounted for in order to develop ethical AI (eAI) solutions which sustain human values and rights. Currently, a variety of guidelines and a handful…

Learning analytics (LA) draws from the learning sciences to interpret learner behavior and inform system design. Yet, past personalization remains largely at the content or performance level (during learner-system interactions), overlooking…

A growing body of work shows that many problems in fairness, accountability, transparency, and ethics in machine learning systems are rooted in decisions surrounding the data collection and annotation process. In spite of its fundamental…

We introduce a new computational model of moral decision making, drawing on a recent theory of commonsense moral learning via social dynamics. Our model describes moral dilemmas as a utility function that computes trade-offs in values over…

Computing technologies have become pervasive in daily life, sometimes bringing unintended but harmful consequences. For students to learn to think not only about what technology they could create, but also about what technology they should…

The ethics of human-robot interaction (HRI) have been discussed extensively based on three traditional frameworks: deontology, consequentialism, and virtue ethics. We conducted a mixed within/between experiment to investigate Sparrow's…

The ability to make decisions based on data, with its inherent uncertainties and variability, is a complex and vital skill in the modern world. The need for such quantitative critical thinking occurs in many different contexts, and while it…

The aim of this workshop is to give researchers from academia and industry the possibility to discuss the inter-and multi-disciplinary nature of the relationships between people and robots towards effective and long-lasting collaborations.…

Work on morality in large language models (LLMs) has progressed via constitutional AI, reinforcement learning from human feedback (RLHF) and systematic benchmarking, yet it still lacks tools to connect internal moral representations to…

Human-centric computer vision (HCCV) data curation practices often neglect privacy and bias concerns, leading to dataset retractions and unfair models. HCCV datasets constructed through nonconsensual web scraping lack crucial metadata for…

The widespread use of machine learning in credit scoring has brought significant advancements in risk assessment and decision-making. However, it has also raised concerns about potential biases, discrimination, and lack of transparency in…

The reliable prediction of the temporal behavior of complex systems is key in numerous scientific fields. This strong interest is however hindered by modeling issues: often, the governing equations describing the physics of the system under…

Imitation learning offers a promising path for robots to learn general-purpose behaviors, but traditionally has exhibited limited scalability due to high data supervision requirements and brittle generalization. Inspired by recent advances…

Recent advances in AI applications have raised growing concerns about the need for ethical guidelines and regulations to mitigate the risks posed by these technologies. In this paper, we present a mixed-methods survey study - combining…

Data sharing partnerships are increasingly an imperative for research institutions and, at the same time, a challenge for established models of data governance and ethical research oversight. We analyse four cases of data partnership…