Background: Trustworthy AI serves as a foundational pillar for two major AI ethics conferences: AIES and FAccT. Current research often adopts techno-centric approaches, focusing primarily on technical attributes such as accuracy, reliability, robustness, and fairness, while overlooking the sociotechnical dimensions critical to understanding AI trustworthiness in real-world contexts. Objectives: This scoping review aims to examine how the AIES and FAccT communities conceptualize, measure, and validate AI trustworthiness, identifying major gaps and opportunities for advancing a holistic understanding of trustworthy AI systems. Methods: We conduct a scoping review of the AIES and FAccT conference proceedings to date, systematically analyzing how trustworthiness is defined, operationalized, and applied across different research domains. Our analysis focuses on conceptualization approaches, measurement methods, verification and validation techniques, application areas, and underlying values. Results: While significant progress has been made in defining technical attributes such as transparency, accountability, and robustness, our findings reveal critical gaps. Current research often predominantly emphasizes technical precision at the expense of social and ethical considerations. The sociotechnical nature of AI systems remains less explored and trustworthiness emerges as a contested concept shaped by those with the power to define it. Conclusions: An interdisciplinary approach combining technical rigor with social, cultural, and institutional considerations is essential for advancing trustworthy AI. We propose actionable measures for the AI ethics community to adopt holistic frameworks that genuinely address the complex interplay between AI systems and society, ultimately promoting responsible technological development that benefits all stakeholders.

Type II diabetes is a complex health condition requiring patients to closely and continuously collaborate with healthcare professionals and other caretakers on lifestyle changes. While intelligent products have tremendous potential to support such Diabetes Lifestyle Management (DLM), existing products are typically conceived from a technology-centered perspective that insufficiently acknowledges the degree to which collaboration and inclusion of stakeholders is required. In this article, we argue that the emergent design philosophy of Hybrid Intelligence (HI) forms a suitable alternative lens for research and development. In particular, we (1) highlight a series of pragmatic challenges for effective AI-based DLM support based on results from an expert focus group, and (2) argue for HI’s potential to address these by outlining relevant research trajectories.

The implementation of AI systems has led to new forms of harm in various sensitive social domains. We analyze these as problems How to address these harms remains at the center of controversial debate. In this paper, we discuss the inherent normative uncertainty and political debates surrounding the safety of AI systems.of vagueness to illustrate the shortcomings of current technical approaches in the AI Safety literature, crystallized in three dilemmas that remain in the design, training and deployment of AI systems. We argue that resolving normative uncertainty to render a system 'safe' requires a sociotechnical orientation that combines quantitative and qualitative methods and that assigns design and decision power across affected stakeholders to navigate these dilemmas through distinct channels for dissent. We propose a set of sociotechnical commitments and related virtues to set a bar for declaring an AI system 'human-compatible', implicating broader interdisciplinary design approaches.

Learning cooperative policies for multi-agent systems is often challenged by partial observability and a lack of coordination. In some settings, the structure of a problem allows a distributed solution with limited communication. Here, we consider a scenario where no communication is available, and instead we learn local policies for all agents that collectively mimic the solution to a centralized multi-agent static optimization problem. Our main contribution is an information theoretic framework based on rate distortion theory which facilitates analysis of how well the resulting fully decentralized policies are able to reconstruct the optimal solution. Moreover, this framework provides a natural extension that addresses which nodes an agent should communicate with to improve the performance of its individual policy.

With the growth of high-throughput proteomic data, in particular time series gene expression data from various perturbations, a general question that has arisen is how to organize inherently heterogenous data into meaningful structures. Since biological systems such as breast cancer tumors respond differently to various treatments, little is known about exactly how these gene regulatory networks (GRNs) operate under different stimuli. Challenges due to the lack of knowledge not only occur in modeling the dynamics of a GRN but also cause bias or uncertainties in identifying parameters or inferring the GRN structure. This paper describes a new algorithm which enables us to estimate bias error due to the effect of perturbations and correctly identify the common graph structure among biased inferred graph structures. To do this, we retrieve common dynamics of the GRN subject to various perturbations. We refer to the task as "repairing" inspired by "image repairing" in computer vision. The method can automatically correctly repair the common graph structure across perturbed GRNs, even without precise information about the effect of the perturbations. We evaluate the method on synthetic data sets and demonstrate an application to the DREAM data sets and discuss its implications to experiment design.

Epithelia are sheets of connected cells that are essential across the animal kingdom. Experimental observations suggest that the dynamical behavior of many single-layered epithelial tissues has strong analogies with that of specific mechanical systems, namely large networks consisting of point masses connected through spring-damper elements and undergoing the influence of active and dissipating forces. Based on this analogy, this work develops a modeling framework to enable the study of the mechanical properties and of the dynamic behavior of large epithelial cellular networks. The model is built first by creating a network topology that is extracted from the actual cellular geometry as obtained from experiments, then by associating a mechanical structure and dynamics to the network via spring-damper elements. This scalable approach enables running simulations of large network dynamics: the derived modeling framework in particular is predisposed to be tailored to study general dynamics (for example, morphogenesis) of various classes of single-layered epithelial cellular networks. In this contribution, we test the model on a case study of the dorsal epithelium of the Drosophila melanogaster embryo during early dorsal closure (and, less conspicuously, germband retraction).