Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness remains elusive and constitutes a key issue that impedes large-scale adoption. Besides, robustness is interpreted differently across domains and contexts of AI. In this work, we systematically survey recent progress to provide a reconciled terminology of concepts around AI robustness. We introduce three taxonomies to organize and describe the literature both from a fundamental and applied point of view: (1) methods and approaches that address robustness in different phases of the machine learning pipeline; (2) methods improving robustness in specific model architectures, tasks, and systems; and in addition, (3) methodologies and insights around evaluating the robustness of AI systems, particularly the tradeoffs with other trustworthiness properties. Finally, we identify and discuss research gaps and opportunities and give an outlook on the field. We highlight the central role of humans in evaluating and enhancing AI robustness, considering the necessary knowledge they can provide, and discuss the need for better understanding practices and developing supportive tools in the future.

Digitally-supported participatory methods are often used in policy-making to develop inclusive policies by collecting and integrating citizen's opinions. However, these methods fail to capture the complexity and nuances in citizen's needs, i.e., citizens are generally unaware of other's needs, perspectives, and experiences. Consequently, policies developed with this underlying gap tend to overlook the alignment of multistakeholder perspectives, and design policies based on the optimization of high-level demographic features. In our contribution, we propose a method to enable citizens understand other's perspectives and calibrate their positions. First, we collected requirements and design principles to develop our approach by involving stakeholders and experts in policymaking in a series of workshops. Then, we conducted a crowdsourcing study with 420 participants to compare the effect of different text and images, on people's initial and final motivations and their willingness to change opinions. We observed that both influence participant's opinion change, however, the effect is more pronounced for textual modality. Finally, we discuss overarching implications of designing with empathy to mediate alignment of citizen's perspectives.

Machine learning (ML) practitioners and organizations are building model repositories of pre-trained models, referred to as model zoos. These model zoos contain metadata describing the properties of the ML models and datasets. The metadata serves crucial roles for reporting, auditing, ensuring reproducibility, and enhancing interpretability. Despite the growing adoption of descriptive formats like datasheets and model cards, the metadata available in existing model zoos remains notably limited. Moreover, existing formats have limited expressiveness, thus constraining the potential use of model repositories, extending their purpose beyond mere storage for pre-trained models. This paper proposes a unified metadata representation format for model zoos. We illustrate that comprehensive metadata enables a diverse range of applications, encompassing model search, reuse, comparison, and composition of ML models. We also detail the design and highlight the implementation of an advanced model zoo system built on top of our proposed metadata representation.

Background:
There is increasing evidence that a complex interplay of factors within environments in which children grows up, contributes to children’s suboptimal mental health and cognitive development. The concept of the life-course exposome helps to study the impact of the physical and social environment, including social inequities, on cognitive development and mental health over time.

Methods:
Equal-Life develops and tests combined exposures and their effects on children’s mental health and cognitive development. Data from eight birth-cohorts and three school studies (N = 240.000) linked to exposure data, will provide insights and policy guidance into aspects of physical and social exposures hitherto untapped, at different scale levels and timeframes, while accounting for social inequities. Reasoning from the outcome point of view, relevant stakeholders participate in the formulation and validation of research questions, and in the formulation of environmental hazards. Exposure assessment combines GIS-based environmental indicators with omics approaches and new data sources, forming the early-life exposome. Statistical tools integrate data at different spatial and temporal granularity and combine exploratory machine learning models with hypothesis-driven causal modeling.

Conclusions:
Equal-Life contributes to the development and utilization of the exposome concept by (1) integrating the internal, physical and social exposomes, (2) studying a distinct set of life-course effects on a child’s development and mental health (3) characterizing the child’s environment at different developmental stages and in different activity spaces, (4) looking at supportive environments for child development, rather than merely pollutants, and (5) combining physical, social indicators with novel effect markers and using new data sources describing child activity patterns and environments.

The spread of AI and black-box machine learning models made it necessary to explain their behavior. Consequently, the research field of Explainable AI was born. The main objective of an Explainable AI system is to be understood by a human as the final beneficiary of the model. In our research, we frame the explainability problem from the crowds point of view and engage both users and AI researchers through a gamified crowdsourcing framework. We research whether it's possible to improve the crowds understanding of black-box models and the quality of the crowdsourced content by engaging users in a set of gamified activities through a gamified crowdsourcing framework named EXP-Crowd. While users engage in such activities, AI researchers organize and share AI- and explainability-related knowledge to educate users. We present the preliminary design of a game with a purpose (G.W.A.P.) to collect features describing real-world entities which can be used for explainability purposes. Future works will concretise and improve the current design of the framework to cover specific explainability-related needs.

Traditional approaches to data-informed policymaking are often tailored to specific contexts and lack strong citizen involvement and collaboration, which are required to design sustainable policies. We argue the importance of empathy-based methods in the policymaking domain given the successes in diverse settings, such as healthcare and education. In this paper, we introduce COCTEAU (Co-Creating The European Union), a novel framework built on the combination of empathy and gamification to create a tool aimed at strengthening interactions between citizens and policy-makers. We describe our design process and our concrete implementation, which has already undergone preliminary assessments with different stakeholders. Moreover, we briefly report pilot results from the assessment. Finally, we describe the structure and goals of our demonstration regarding the newfound formats and organizational aspects of academic conferences.

One year after the outbreak of the SARS-CoV-2, several vaccines have been successfully developed to prevent its spreading, and vaccine roll-out campaigns are taking place worldwide. However, an increasing number of individuals is still hesitant towards getting vaccinated, and this poses a serious threat to reaching herd immunity.We collect and analyze Italian online conversations about COVID-19 vaccines on Twitter. We define a hashtag-based semi-automatic approach to label large volumes of tweets as supporters or skeptical about the vaccine. We investigate the geographical, temporal and lexical distribution of data, and we train an accurate binary classifier that predicts the stance of tweets towards vaccines, i.e., it applies a "Pro-vax" or "No-vax" label. This classification approach can be used, in parallel with other affirmed techniques, to promptly detect and prevent the spread of negative and misleading messages about vaccines, ensuring higher rates of vaccine uptake.

We present VaccinItaly, a project which monitors Italian online conversations around vaccines, on Twitter and Facebook. We describe the ongoing data collection, which follows the SARS-CoV-2 vaccination campaign roll-out in Italy and we provide public access to the data collected. We show results from a preliminary analysis of the spread of low- and highcredibility news shared alongside vaccine-related conversations on both social media platforms. We also investigate the content of most popular YouTube videos and encounter several cases of harmful and misleading content about vaccines. Finally, we geolocate Twitter users who discuss vaccines and correlate their activity with open data statistics on vaccine uptake. We make up-to-date results available to the public through an interactive online dashboard associated with the project. The goal of our project is to gain further understanding of the interplay between the public discourse on online social media and the dynamics of vaccine uptake in the real world.

In recent years, new methods to engage citizens in deliberative processes of governments and institutions have been studied. Such methodologies have become a necessity to assure the efficacy and sustainability of policies. Several tools and solutions have been proposed while trying to achieve such a goal. The dual problem to citizen engagement is how to provide policymakers with useful and actionable insights and data stemming from those processes. The following paper has the aim to share with the audience of the Data for Policy Conference 2021 an innovative tool based on the concept of participatory policymaking with the scope of collecting feedback and comments to enhance the consistency and the usefulness of the tool. We propose research featuring a method and implementation of a crowdsourcing and co-creation technique that can provide value to both citizens and policymakers engaged in the policy-making process. Thanks to our methodology, policymakers can design challenges for citizens to take part, cooperate and provide their input to policymakers. We also propose a web-based tool that allows citizens to participate and produce content to support the policymaking processes through a gamified interface that focuses on emotional and vision-oriented content.

In recent years, new methods to engage citizens in deliberative processes of governments and institutions have been studied. Such methodologies have become a necessity to assure the efficacy and longevity of policies. Several tools and solutions have been proposed while trying to achieve such a goal. The dual problem to citizen engagement is how to provide policy-makers with useful and actionable insights stemming from those processes. In this paper, we propose a research featuring a method and implementation of a crowdsourcing and co-creation technique that can provide value to both citizens and policy-makers engaged in the policy-making process. Thanks to our methodology, policy-makers can design challenges for citizens to partake, cooperate and provide their input. We also propose a web-based tool that allow citizens to participate and produce content to support the policy-making processes through a gamified interface that focuses on emotional and vision-oriented content.

This paper presents a user modeling pipeline to analyze discussions and opinions shared on social media regarding polarized political events (e.g., public polls). The pipeline follows a four-step methodology. First, social media posts and users metadata are crawled. Second, a filtering mechanism is applied to filter spammers and bot users. As a third step, demographics information is extracted out of the valid users, namely gender, age, ethnicity and location information. Finally, the political polarity of the users with respect to the analyzed event is predicted. In the scope of this work, our proposed pipeline is applied to two referendum scenarios (independence of Catalonia in Spain and autonomy of Lombardy in Italy) in order to assess the performance of the approach with respect to the capability of collecting correct insights on the demographics of social media users and of predicting the poll results based on the opinions shared by the users. Experiments show that the method was effective in predicting the political trends for the Catalonia case, but not for the Lombardy case. Among the various motivations for this, we noticed that in general Twitter was more representative of the users opposing the referendum than the ones in favor.

This paper presents a novel approach to the design of Human Machine Interface (HMI) systems to be adopted in the supervision of industrial plants. We describe how well known technologies and practices can be transferred from internet-based architectures to embedded systems. We propose a mix of technologies that can be fruitfully used in the implementation of HMI architectures and provide a sketch of the design of a real industrial HMI system that exploit internet communication protocols and Web-based architectures. A set of new features can be achieved thanks to this architecture, such as application adaptivity, interface personalization, control remotization, and multi-channel notification. Finally, we evaluate the resulting platform in terms of performance, reliability, and usability.

Expert selection is an important aspect of many Web applications, e.g., when they aim at matching contents, tasks or advertisement based on user profiles, possibly retrieved from social networks.

This paper focuses on selecting experts within the population of social networks, according to the information about the social activities of their users. We consider the following problem: given an expertise need (expressed for instance as a natural language query) and a set of social network members, who are the most knowledgeable people for addressing that need? We considers social networks both as a source of expertise information and as a route to reach expert users, and define models and methods for evaluating people's expertise by considering their profiles and by tracing their activities in social networks. For matching queries to social resources, we use both text analysis and semantic annotation. An extensive set of experiments shows that the analysis of social activities, social relationships, and socially shared contents helps improving the effectiveness of an expert finding system.

n essential aspect for building effective crowdsourcing com- putations is the ability of "controlling the crowd", i.e. of dynamically adapting the behaviour of the crowdsourcing systems as response to the quantity and quality of completed tasks or to the availability and reliability of performers. Most crowdsourcing systems only provide limited and predefined controls; in contrast, we present an approach to crowdsourcing which provides fine-level, powerful and flexible controls. We model each crowdsourcing application as composition of elementary task types and we progressively transform these high level specifications into the features of a reactive execution environment that supports task planning, assignment and completion as well as performer monitoring and exclusion. Controls are specified as active rules on top of data structures which are derived from the model of the application; rules can be added, dropped or modified, thus guaranteeing maximal flexibility with limited effort.

We also report on our prototype platform that implements the proposed framework and we show the results of our experimentations with different rule sets, demonstrating how simple changes to the rules can substantially affect time, effort and quality involved in crowdsourcing activities.

Exploratory search is an information seeking behavior where users progressively learn about one or more topics of interest; it departs quite radically from traditional keyword-based query paradigms, as it combines querying and browsing of resources, and covers activities such as investigating, evaluating, comparing, and synthesizing retrieved information. In most cases, such activities are enabled by a conceptual description of information in terms of entities and their semantic relationships. Customized Web applications, where few applicative entities and their relationships are embedded within the application logics, typically provide some support to exploratory search, which is, however, specific for a given domain. In this paper, we describe a general-purpose exploratory search framework, i.e., a framework which is neutral to the application logic. Our contribution consists of the formalization of the exploratory search paradigm over Web data sources, accessed by means of services; extracted information is described by means of an entity-relationship schema, which masks the service implementations. Exploratory interaction is supported by a general-purpose user interface including a set of widgets for data exploration, from big tables to atomic tables, visual diagrams, and geographic maps; the user interaction is translated to queries defined in SeCoQL SeCoQL , a SQL-like language and protocol specifically designed for supporting exploratory search over data sources. We illustrate the software architecture of our prototype, which uses the interplay of a query and result management system with an orchestrator, capable of incrementally building queries and of walking through the past navigation history. The distinctive feature of the framework is the ability to extract top solutions, which combine top-ranked entity instances. We evaluate exploratory search from the end-user perspective in the context of a cognitive model for search, by studying the user’s behavior and the effectiveness of exploratory search in terms of quality of results produced by the search process; we also compare the effectiveness of interaction in using our multi-domain search system with the use of various replicas of the system, each acting upon a single domain, and with the use of conventional search engines.

Retrieval and management of Web data is becoming a more and more complex problem, due to the amount of information to be dealt with, to the diversity of the information sources and of the data formats, and to the evolving expectations of users. More and more users are increasingly relying on social interaction to complete and validate the results of their online activities. For instance, scouting "interesting" results, or suggesting new, unexpected search directions in information seeking processes, occurs in most times aside of the search systems and processes, possibly instrumented and mediated by a social network. Moreover, tasks such as quality assessment, opinion making, and sense extraction cannot be completely delegated to automatic procedures. In this paper we propose a paradigm that embodies crowds and social network communities as first-class sources for the information management and extraction on the Web. Our approach aims at filling the gap between traditional Web systems (CMS, search engines and others), which operate upon world-wide information, with social systems, capable of interacting with real people, in real time, to capture their opinions, suggestions, and emotions by leveraging crowdsourcing practices and making them viable upon a social network. Thanks to the proposed approach, the data management and manipulation experience of users can be enormously enriched.

Multi-domain search answers to queries spanning multiple entities, like "Find a hotel in Milan close to a concert venue, a museum and a good restaurant", by producing ranked sets of entity combinations that maximize relevance, measured by a function expressing the user's preferences. Due to the combinatorial nature of results, good entity instances (e.g., five stars hotels) tend to appear repeatedly in top-ranked combinations. To improve the quality of the result set, it is important to balance relevance with diversity, which promotes different, yet almost equally relevant, entities in the top-k combinations. This paper explores two different notions of diversity for multi-domain result sets, compares experimentally alternative algorithms for the trade-off between relevance and diversity, and performs a user study for evaluating the utility of diversification in multi-domain queries.

Web users are increasingly relying on social interaction to complete and validate the results of their search activities. While search systems are superior machines to get world-wide information, the opinions collected within friends and expert/local communities can ultimately determine our decisions: human curiosity and creativity is often capable of going much beyond the capabilities of search systems in scouting "interesting" results, or suggesting new, unexpected search directions. Such personalized interaction occurs in most times aside of the search systems and processes, possibly instrumented and mediated by a social network; when such interaction is completed and users resort to the use of search systems, they do it through new queries, loosely related to the previous search or to the social interaction. In this paper we propose CrowdSearcher, a novel search paradigm that embodies crowds as first-class sources for the information seeking process. CrowdSearcher aims at filling the gap between generalized search systems, which operate upon world-wide information - including facts and recommendations as crawled and indexed by computerized systems - with social systems, capable of interacting with real people, in real time, to capture their opinions, suggestions, emotions. The technical contribution of this paper is the discussion of a model and architecture for integrating computerized search with human interaction, by showing how search systems can drive and encapsulate social systems. In particular we show how social platforms, such as Facebook, LinkedIn and Twitter, can be used for crowdsourcing search-related tasks; we demonstrate our approach with several prototypes and we report on our experiment upon real user communities.