Dementia is one of the most pressing health problems in the world. Still, the good news is that it is much better preventable than (advanced-stage) treatable. Over the years, a new narrative has come up: heart health = brain health. But its translation into healthcare interventions has been slow. In this design approach, we propose two empowerment options for patients, caregivers, and their health professionals. Firstly, we describe how cardiac health successes in enticing senior citizens to large lifestyle improvements may be used for treating early-stage dementia and cognitive decline. Biologically, this uses causality between blood pressure and cardiovascular health on the one hand and dementia outcomes on the other. Practically, it enables daily success feedback, which empowers patients in their health improvement experiments. Secondly, we describe and user-test an AI Health Research Assistant to extract the best available lifestyle findings from literature, to keep up with over 100,000 new health publications flooding us every year. Our user test highlights challenges and opportunities for a Health AI, especially regarding claim transparency, data quality, and risks of hallucinations. We suggest research metadata criteria to evaluate ambiguous or conflicting health science claims.

Hybrid Intelligence (HI) is an emerging paradigm in which artificial intelligence (AI) augments human intelligence. The current literature lacks systematic models that guide the design and evaluation of HI systems. Further, discussions around HI primarily focus on technology, neglecting the holistic human-AI ensemble. In this paper, we take the initial steps toward the development of a quality model for characterizing and evaluating HI systems from a human-AI teams perspective. We first conducted a study investigating the adequacy of properties commonly associated with effective human teams to describe HI. The study features the insights of 50 HI researchers, and shows that various human team properties, including boundedness, interdependence, competency, purposefulness, initiative, normativity, and effectiveness, are important for HI systems. Based on these results, we developed a quality model for HI teams composed of seven high-level quality attributes, further refined into 16 specific ones. To evaluate the relevance and understanding of the proposed attributes, we conducted a second empirical investigation by staging competitions in which participants used the quality model to develop and analyze HI usage scenarios. Our analysis of 48 collected scenarios, which we openly release, confirms the proposed attributes’ relevance and highlights insights that emerge when designers consider the quality model in HI system design.

Large-scale survey tools enable the collection of citizen feedback in opinion corpora. Extracting the key arguments from a large and noisy set of opinions helps in understanding the opinions quickly and accurately. Fully automated methods can extract arguments but (1) require large labeled datasets that induce large annotation costs and (2) work well for known viewpoints, but not for novel points of view. We propose HyEnA, a hybrid (human + AI) method for extracting arguments from opinionated texts, combining the speed of automated processing with the understanding and reasoning capabilities of humans. We evaluate HyEnA on three citizen feedback corpora. We find that, on the one hand, HyEnA achieves higher coverage and precision than a state-of-The-Art automated method when compared to a common set of diverse opinions, justifying the need for human insight. On the other hand, HyEnA requires less human effort and does not compromise quality compared to (fully manual) expert analysis, demonstrating the benefit of combining human and artificial intelligence.

Presenting high-level arguments is a crucial task for fostering participation in online societal discussions. Current argument summarization approaches miss an important facet of this task-capturing diversity-which is important for accommodating multiple perspectives. We introduce three aspects of diversity: those of opinions, annotators, and sources. We evaluate approaches to a popular argument summarization task called Key Point Analysis, which shows how these approaches struggle to (1) represent arguments shared by few people, (2) deal with data from various sources, and (3) align with subjectivity in human-provided annotations. We find that both general-purpose LLMs and dedicated KPA models exhibit this behavior, but have complementary strengths. Further, we observe that diversification of training data may ameliorate generalization. Addressing diversity in argument summarization requires a mix of strategies to deal with subjectivity.

We adopt an emerging and prominent vision of human-centred Artificial Intelligence that requires building trustworthy intelligent systems. Such systems should be capable of dealing with the challenges of an interconnected, globalised world by handling plurality and by abiding by human values. Within this vision, pluralistic value alignment is a core problem for AI– that is, the challenge of creating AI systems that align with a set of diverse individual value systems. So far, most literature on value alignment has considered alignment to a single value system. To address this research gap, we propose a novel method for estimating and aggregating multiple individual value systems. We rely on recent results in the social choice literature and formalise the value system aggregation problem as an optimisation problem. We then cast this problem as an ℓ_p-regression problem. Doing so provides a principled and general theoretical framework to model and solve the aggregation problem. Our aggregation method allows us to consider a range of ethical principles, from utilitarian (maximum utility) to egalitarian (maximum fairness). We illustrate the aggregation of value systems by considering real-world data from two case studies: the Participatory Value Evaluation process and the European Values Study. Our experimental evaluation shows how different consensus value systems can be obtained depending on the ethical principle of choice, leading to practical insights for a decision-maker on how to perform value system aggregation.

Active Learning (AL) addresses the high costs of collecting human annotations by strategically annotating the most informative samples.However, for subjective NLP tasks, incorporating a wide range of perspectives in the annotation process is crucial to capture the variability in human judgments.We introduce Annotator-Centric Active Learning (ACAL), which incorporates an annotator selection strategy following data sampling.Our objective is two-fold: (1) to efficiently approximate the full diversity of human judgments, and (2) to assess model performance using annotator-centric metrics, which value minority and majority perspectives equally.We experiment with multiple annotator selection strategies across seven subjective NLP tasks, employing both traditional and novel, human-centered evaluation metrics.Our findings indicate that ACAL improves data efficiency and excels in annotator-centric performance evaluations.However, its success depends on the availability of a sufficiently large and diverse pool of annotators to sample from.

Disagreements are common in online discussions. Disagreement may foster collaboration and improve the quality of a discussion under some conditions. Although there exist methods for recognizing disagreement, a deeper understanding of factors that influence disagreement is lacking in the literature. We investigate a hypothesis that differences in personal values are indicative of disagreement in online discussions. We show how state-of-the-art models can be used for estimating values in online discussions and how the estimated values can be aggregated into value profiles. We evaluate the estimated value profiles based on human-annotated agreement labels. We find that the dissimilarity of value profiles correlates with disagreement in specific cases. We also find that including value information in agreement prediction improves performance.

Channel allocation in dense, decentralized Wi-Fi networks is a challenging due to the highly nonlinear solution space and the difficulty to estimate the opponent’s utility model. So far, only centralized or mediated approaches have succeeded in applying negotiation to this setting. We propose the first two fully-distributed negotiation approaches for Wi-Fi channel assignment. Both of them leverage a pre-sampling of the utility space with simulated annealing and a noisy estimation of the Wi-Fi utility function. Regarding negotiation protocols, one of the approaches makes use of the Alternating Offers protocol, while the other uses the novel Multiple Offers Protocol for Multilateral Negotiations with Partial Consensus (MOPaC), which naturally matches the problem peculiarities. We compare the performance of our proposed approaches with the previous mediated approach, based on simple text mediation. Our experiments show that our approaches yield better utility outcomes, better fairness and less information disclosure than the mediated approach.

User experience is the ultimate quality of service criterion for modern WLAN networks. However, network configuration approaches are mainly network-centric. We envision a paradigm shift, empowering users in network management. We study how automated negotiation and collective intelligence can support the democratic configuration of a wireless network, leveraging client and provider interests. This new paradigm allows for flexible network configuration, which enables better exploitation of resources considering the clients real usage and needs, and a fair distribution of throughput among users.

Values, such as freedom and safety, are the core motivations that guide us humans. A prerequisite for creating value-aligned multiagent systems that involve humans and artificial agents is value inference, the process of identifying values and reasoning about human value preferences. We introduce a framework that connects the value inference steps, and motivate why a hybrid intelligence approach is instrumental for its success. We also highlight the multidisciplinary research challenges that hybrid value inference entails.