Jayati Deshmukh

Lokesh Singh, Athina Georgara, Jayati Deshmukh et al.

Automatic recognition of affective state from wearable physiology has clear societal impact for public health, preventive care, and stress-aware interventions, but real deployments require robustness to non-stationary dynamics, artefacts, and missing sensors. We study this problem on WESAD, using baseline, stress, and amusement conditions, where common fixed-basis spectral features such as FFT bandpower and Welch PSD can oversmooth short-lived discriminative patterns. We propose a non-stationary pipeline that combines Fourier-Bessel Series Expansion (FBSE) with EWT data-driven spectral segmentation to extract mode-wise transient descriptors. For multimodal integration, we adopt decision-level aggregation over per-modality predictors and weight each modality by predictive uncertainty and modality reliability. Results on WESAD, using 15 subjects and ECG, EDA, BVP, EMG, and ACC signals across three classes, indicate that decision-level aggregation is approximately 84 percent of the time at least as good as feature-level aggregation, and approximately 48 percent of the time strictly better, suggesting improved robustness under heterogeneous and partially reliable sensing.

Sarah Kiden, Bernd Stahl, Beverley Townsend et al.

This report presents a comprehensive response to the United Nation's Interim Report on Governing Artificial Intelligence (AI) for Humanity. It emphasizes the transformative potential of AI in achieving the Sustainable Development Goals (SDGs) while acknowledging the need for robust governance to mitigate associated risks. The response highlights opportunities for promoting equitable, secure, and inclusive AI ecosystems, which should be supported by investments in infrastructure and multi-stakeholder collaborations across jurisdictions. It also underscores challenges, including societal inequalities exacerbated by AI, ethical concerns, and environmental impacts. Recommendations advocate for legally binding norms, transparency, and multi-layered data governance models, alongside fostering AI literacy and capacity-building initiatives. Internationally, the report calls for harmonising AI governance frameworks with established laws, human rights standards, and regulatory approaches. The report concludes with actionable principles for fostering responsible AI governance through collaboration among governments, industry, academia, and civil society, ensuring the development of AI aligns with universal human values and the public good.

Aleksandra Landowska, Aislinn D Gomez Bergin, Ayodeji O. Abioye et al.

This paper introduces and overviews a multidisciplinary project aimed at developing responsible and adaptive multi-human multi-robot (MHMR) systems for complex, dynamic settings. The project integrates co-design, ethical frameworks, and multimodal sensing to create AI-driven robots that are emotionally responsive, context-aware, and aligned with the needs of diverse users. We outline the project's vision, methodology, and early outcomes, demonstrating how embodied AI can support sustainable, ethical, and human-centred futures.

Srinath Srinivasa, Jayati Deshmukh

After several winters, AI is center-stage once again, with current advances enabling a vast array of AI applications. This renewed wave of AI has brought back to the fore several questions from the past, about philosophical foundations of intelligence and common sense -- predominantly motivated by ethical concerns of AI decision-making. In this paper, we address some of the arguments that led to research interest in intelligent agents, and argue for their relevance even in today's context. Specifically we focus on the cognitive sense of "self" and its role in autonomous decision-making leading to responsible behaviour. The authors hope to make a case for greater research interest in building richer computational models of AI agents with a sense of self.

Srinath Srinivasa, Jayati Deshmukh

Agent-based models have emerged as a promising paradigm for addressing ever increasing complexity of information systems. In its initial days in the 1990s when object-oriented modeling was at its peak, an agent was treated as a special kind of "object" that had a persistent state and its own independent thread of execution. Since then, agent-based models have diversified enormously to even open new conceptual insights about the nature of systems in general. This paper presents a perspective on the disparate ways in which our understanding of agency, as well as computational models of agency have evolved. Advances in hardware like GPUs, that brought neural networks back to life, may also similarly infuse new life into agent-based models, as well as pave the way for advancements in research on Artificial General Intelligence (AGI).