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A multitude of explainability methods and associated fidelity performance metrics have been proposed to help better understand how modern AI systems make decisions. However, much of the current work has remained theoretical -- without much…
Frontier AI developers operate at the intersection of rapid technical progress, extreme risk exposure, and growing regulatory scrutiny. While a range of external evaluations and safety frameworks have emerged, comparatively little attention…
Argumentation is a central subarea of Artificial Intelligence (AI) for modeling and reasoning about arguments. The semantics of abstract argumentation frameworks (AFs) is given by sets of arguments (extensions) and conditions on the…
Developing robust and fair AI systems require datasets with comprehensive set of labels that can help ensure the validity and legitimacy of relevant measurements. Recent efforts, therefore, focus on collecting person-related datasets that…
The article proposes a method for forming a dedicated operational context in course of development and implementation of autonomous corporate management systems based on example of autonomous systems for a board of directors. The…
Going beyond simple text processing, financial auditing requires detecting semantic, structural, and numerical inconsistencies across large-scale disclosures. As financial reports are filed in XBRL, a structured XML format governed by…
Decision-making systems based on AI and machine learning have been used throughout a wide range of real-world scenarios, including healthcare, law enforcement, education, and finance. It is no longer far-fetched to envision a future where…
In this paper, we develop the position that current frameworks for evaluating emotional intelligence (EI) in artificial intelligence (AI) systems need refinement because they do not adequately or comprehensively measure the various aspects…
Autonomous vehicles (AVs) must be both safe and trustworthy to gain social acceptance and become a viable option for everyday public transportation. Explanations about the system behaviour can increase safety and trust in AVs.…
Agentic AI systems plan, use tools, maintain state, and act across multi-step workflows with external effects, meaning trustworthy deployment can no longer be judged by task completion alone. The current literature remains fragmented across…
Background: Good API documentation facilities the development process, improving productivity and quality. While the topic of API documentation quality has been of interest for the last two decades, there have been few studies to map the…
Reputation is crucial to enabling human or software agents to select among alternative providers. Although several effective reputation assessment methods exist, they typically distil reputation into a numerical representation, with no…
Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize various sectors, from healthcare to finance, education, and beyond. However, successfully implementing AI systems remains a complex…
Speech technologies are deployed in high-stakes settings, yet fairness concerns remain fragmented across tasks and disciplines. Existing surveys either adopt a general machine-learning perspective that overlooks speech-specific properties…
It is well recognised that ensuring fair AI systems is a complex sociotechnical challenge, which requires careful deliberation and continuous oversight across all stages of a system's lifecycle, from defining requirements to model…
Our main contribution is that we are using AI to discern the key drivers of variation of ESG mentions in the corporate filings. With AI, we are able to separate "dimensions" along which the corporate management presents their ESG policies…
Many decision-making processes have begun to incorporate an AI element, including prison sentence recommendations, college admissions, hiring, and mortgage approval. In all of these cases, AI models are being trained to help human decision…
Using cloud-based computer vision services is gaining traction, where developers access AI-powered components through familiar RESTful APIs, not needing to orchestrate large training and inference infrastructures or curate/label training…
Frontier AI systems require governance mechanisms that can verify internal alignment, not just behavioral compliance. Private governance mechanisms audits, certification, insurance, and procurement are emerging to complement public…
The implementation of responsible AI in an organization is inherently complex due to the involvement of multiple stakeholders, each with their unique set of goals and responsibilities across the entire AI lifecycle. These responsibilities…