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Surveillance capitalism is a concept that describes the practice of collecting and analyzing massive amounts of user data for the purpose of targeted advertising and other forms of monetization. The phenomenon has become increasingly prevalent in recent years, with tech companies like Google and Facebook using users' personal information to deliver personalized content and advertisements. Another example of surveillance capitalism is the use of military technology to collect and analyze data for national security purposes. In this context, surveillance capitalism involves the use of technologies like facial recognition and social media monitoring to gather information on individuals and groups deemed to be potential threats to national security. This information is then used to inform military operations and decision-making. This paper wants to analyze in a critical way the phenomenon of surveillance capitalism, proposed under two different ethical framework perspectives. Utilitarianism, a consequentialist ethical theory that judges actions based on their ability to bring about the greatest amount of happiness or pleasure for the greatest number of people, and Kantian deontology, a non-consequentialist ethical theory that emphasizes the importance of individual autonomy, freedom, and dignity. On one side, the utilitarian framework enlightens how Information Technology (IT) and the features provided offer, at first sight, all the positive perceptions to the majority of people, happiness, entertainment, and pleasure. On the other side, the Kantian deontology framework mostly focuses on the aspect of freedom and free will of the individual. This topic is particularly related to the concession of permissions to access data in change of services and the degree of influence that manipulation performed by surveillance capitalism can generate.

The rationale of this work is based on the current user trust discourse of Artificial Intelligence (AI). We aim to produce novel HCI approaches that use trust as a facilitator for the uptake (or appropriation) of current technologies. We propose a framework (HCTFrame) to guide non-experts to unlock the full potential of user trust in AI design. Results derived from a data triangulation of findings from three literature reviews demystify some misconceptions of user trust in computer science and AI discourse, and three case studies are conducted to assess the effectiveness of a psychometric scale in mapping potential users' trust breakdowns and concerns. This work primarily contributes to the fight against the tendency to design technical-centered vulnerable interactions, which can eventually lead to additional real and perceived breaches of trust. The proposed framework can be used to guide system designers on how to map and define user trust and the socioethical and organisational needs and characteristics of AI system design. It can also guide AI system designers on how to develop a prototype and operationalise a solution that meets user trust requirements. The article ends by providing some user research tools that can be employed to measure users' trust intentions and behaviours towards a proposed solution.

We consider the problem of how a platform designer, owner, or operator can improve the design and operation of a digital platform by leveraging a computational cognitive model that represents users's folk theories about a platform as a sociotechnical system. We do so in the context of Reddit, a social media platform whose owners and administrators make extensive use of shadowbanning, a non-transparent content moderation mechanism that filters a user's posts and comments so that they cannot be seen by fellow community members or the public. After demonstrating that the design and operation of Reddit have led to an abundance of spurious suspicions of shadowbanning in case the mechanism was not in fact invoked, we develop a computational cognitive model of users's folk theories about the antecedents and consequences of shadowbanning that predicts when users will attribute their on-platform observations to a shadowban. The model is then used to evaluate the capacity of interventions available to a platform designer, owner, and operator to reduce the incidence of these false suspicions. We conclude by considering the implications of this approach for the design and operation of digital platforms at large.

Link recommendation algorithms contribute to shaping human relations of billions of users worldwide in social networks. To maximize relevance, they typically propose connecting users that are similar to each other. This has been found to create information silos, exacerbating the isolation suffered by vulnerable salient groups and perpetuating societal stereotypes. To mitigate these limitations, a significant body of work has been devoted to the implementation of fair link recommendation methods. However, most approaches do not question the ultimate goal of link recommendation algorithms, namely the monetization of users' engagement in intricate business models of data trade. This paper advocates for a diversification of players and purposes of social network platforms, aligned with the pursue of social justice. To illustrate this conceptual goal, we present ERA-Link, a novel link recommendation algorithm based on spectral graph theory that counteracts the systemic societal discrimination suffered by vulnerable groups by explicitly implementing affirmative action. We propose four principled evaluation measures, derived from effective resistance, to quantitatively analyze the behavior of the proposed method and compare it to three alternative approaches. Experiments with synthetic and real-world networks illustrate how ERA-Link generates better outcomes according to all evaluation measures, not only for the vulnerable group but for the whole network. In other words, ERA-Link recommends connections that mitigate the structural discrimination of a vulnerable group, improves social cohesion and increases the social capital of all network users. Furthermore, by promoting the access to a diversity of users, ERA-Link facilitates innovation opportunities.

In many board games and other abstract games, patterns have been used as features that can guide automated game-playing agents. Such patterns or features often represent particular configurations of pieces, empty positions, etc., which may be relevant for a game's strategies. Their use has been particularly prevalent in the game of Go, but also many other games used as benchmarks for AI research. In this paper, we formulate a design and efficient implementation of spatial state-action features for general games. These are patterns that can be trained to incentivise or disincentivise actions based on whether or not they match variables of the state in a local area around action variables. We provide extensive details on several design and implementation choices, with a primary focus on achieving a high degree of generality to support a wide variety of different games using different board geometries or other graphs. Secondly, we propose an efficient approach for evaluating active features for any given set of features. In this approach, we take inspiration from heuristics used in problems such as SAT to optimise the order in which parts of patterns are matched and prune unnecessary evaluations. This approach is defined for a highly general and abstract description of the problem -- phrased as optimising the order in which propositions of formulas in disjunctive normal form are evaluated -- and may therefore also be of interest to other types of problems than board games. An empirical evaluation on 33 distinct games in the Ludii general game system demonstrates the efficiency of this approach in comparison to a naive baseline, as well as a baseline based on prefix trees, and demonstrates that the additional efficiency significantly improves the playing strength of agents using the features to guide search.

This is a speculative essay on interface design and artificial intelligence. Recently there has been a surge of attention to chatbots based on large language models, including widely reported unsavory interactions. We contend that part of the problem is that text is not all you need: sophisticated AI systems should have dashboards, just like all other complicated devices. Assuming the hypothesis that AI systems based on neural networks will contain interpretable models of aspects of the world around them, we discuss what data such dashboards might display. We conjecture that, for many systems, the two most important models will be of the user and of the system itself. We call these the System Model and User Model. We argue that, for usability and safety, interfaces to dialogue-based AI systems should have a parallel display based on the state of the System Model and the User Model. Finding ways to identify, interpret, and display these two models should be a core part of interface research for AI.

Fast developing artificial intelligence (AI) technology has enabled various applied systems deployed in the real world, impacting people's everyday lives. However, many current AI systems were found vulnerable to imperceptible attacks, biased against underrepresented groups, lacking in user privacy protection, etc., which not only degrades user experience but erodes the society's trust in all AI systems. In this review, we strive to provide AI practitioners a comprehensive guide towards building trustworthy AI systems. We first introduce the theoretical framework of important aspects of AI trustworthiness, including robustness, generalization, explainability, transparency, reproducibility, fairness, privacy preservation, alignment with human values, and accountability. We then survey leading approaches in these aspects in the industry. To unify the current fragmented approaches towards trustworthy AI, we propose a systematic approach that considers the entire lifecycle of AI systems, ranging from data acquisition to model development, to development and deployment, finally to continuous monitoring and governance. In this framework, we offer concrete action items to practitioners and societal stakeholders (e.g., researchers and regulators) to improve AI trustworthiness. Finally, we identify key opportunities and challenges in the future development of trustworthy AI systems, where we identify the need for paradigm shift towards comprehensive trustworthy AI systems.

Although measuring held-out accuracy has been the primary approach to evaluate generalization, it often overestimates the performance of NLP models, while alternative approaches for evaluating models either focus on individual tasks or on specific behaviors. Inspired by principles of behavioral testing in software engineering, we introduce CheckList, a task-agnostic methodology for testing NLP models. CheckList includes a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideation, as well as a software tool to generate a large and diverse number of test cases quickly. We illustrate the utility of CheckList with tests for three tasks, identifying critical failures in both commercial and state-of-art models. In a user study, a team responsible for a commercial sentiment analysis model found new and actionable bugs in an extensively tested model. In another user study, NLP practitioners with CheckList created twice as many tests, and found almost three times as many bugs as users without it.

In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key. However, building social recommender systems based on GNNs faces challenges. For example, the user-item graph encodes both interactions and their associated opinions; social relations have heterogeneous strengths; users involve in two graphs (e.g., the user-user social graph and the user-item graph). To address the three aforementioned challenges simultaneously, in this paper, we present a novel graph neural network framework (GraphRec) for social recommendations. In particular, we provide a principled approach to jointly capture interactions and opinions in the user-item graph and propose the framework GraphRec, which coherently models two graphs and heterogeneous strengths. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework GraphRec. Our code is available at \url{//github.com/wenqifan03/GraphRec-WWW19}

In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview examines the existing literature in the field of XAI, including a prospect toward what is yet to be reached. We summarize previous efforts to define explainability in Machine Learning, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought. We then propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at Deep Learning methods for which a second taxonomy is built. This literature analysis serves as the background for a series of challenges faced by XAI, such as the crossroads between data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to XAI with a reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.