While memes enhance social interaction on social media, they can raise privacy and security concerns. Despite research on overtly toxic or unsafe memes, little attention has been given to users’ experiences with seemingly safe memes and how contextual factors trigger privacy concerns. This study explores users’ comfort levels, influencing factors, underlying reasons for discomfort, and unmet needs when engaging with such memes. We first collected and analyzed 2,317 Reddit posts describing real-world meme experiences, then conducted an online survey with 324 participants to evaluate comfort across curated scenarios. Our findings reveal that perceived-safe memes can cause harm when shared inappropriately, with comfort shaped by content and context. Privacy concerns intensify with deeper involvement, strangers, and sensitive meme topics. We identified users’ desire for consent and control in meme interactions. Based on our study, we make recommendations for users, developers of social media platforms and policymakers to address meme-related privacy and contextual concerns.

When taking photos in a crowd, unintended individuals, such as bystanders, are often captured alongside the main subject(s). In an effort to protect bystanders' privacy, existing methods have been developed to automatically detect bystanders. However, inconsistent definitions of who qualifies as a bystander limit their effectiveness. To better understand bystanders' perceptions, we conducted an online survey with 486 participants, analyzing their responses to 864 image-based scenarios and their comfort with sharing these images online. Our results revealed no significant correlation between comfort with public photo sharing and bystander status. We identified limitations in current bystander detection methodologies, as they often fail to recognize bystanders who are not clearly in the background, hence missing individuals with privacy concerns. Moreover, comfort with public sharing varied significantly depending on the image context. Our findings highlight the importance of considering the context of captured images to address privacy concerns in image sharing.

Providing accurate and actionable advice about phishing emails is challenging. The majority of advice is generic and hard to implement. Phishing emails that pass through filters and land in user inboxes are usually sophisticated and exploit differences between how humans and computers interpret emails. Therefore, users need accurate and relevant guidance to take the right action. This study investigates the effectiveness of guidance based on features extracted from emails, which even in AI-driven systems can sometimes be inaccurate, leading to poor advice. We examined three conditions: control (generic advice), perfect advice, and realistic advice, through an online survey of 489 participants on Prolific, and measured user accuracy and confidence in phishing detection with and without guidance. Our findings indicate that having advice specific to the email is more effective than generic guidance (control). Inaccuracies in the guidance can also impact user decisions and reduce detection accuracy.

In recent years, we have seen an increase in the number of applications where artificial intelligence plays a critical role in decision-making. While everyone is talking about the importance of responsibility, we still lack of computational methods that could successfully enable this. If we want to make AI responsible, it is essential to begin by equipping humans with the appropriate tools to oversee and control these technologies effectively. In this talk, I argue that we should provide usable tools for humans and also organizations. So I invite everyone to focus more on research on human-centered AI, which should prioritize the development of systems that support human decision-making while also enhancing human analytical capabilities. I will present a series of studies illustrating the potential of AI to augment human decision-making processes. These studies explore how AI can be structured not only to perform tasks but also to foster and enhance human critical thinking abilities, thereby contributing to the broader discourse on responsible AI.

Online users often post facial images of themselves and other people on online social networks (OSNs) and other Web 2.0 platforms, which can lead to potential privacy leakage of people whose faces are included in such images. There is limited research on understanding face privacy in social media while considering user behavior. It is crucial to consider privacy of subjects and bystanders separately. This calls for the development of privacy-aware face detection classifiers that can distinguish between subjects and bystanders automatically. This paper introduces such a classifier trained on face-based features, which outperforms the two state-of-the-art methods with a significant margin (by 13.1% and 3.1% for OSN images, and by 17.9% and 5.9% for non-OSN images). We developed a semi-automated framework for conducting a large-scale analysis of the face privacy problem by using our novel bystander-subject classifier. We collected 27,800 images, each including at least one face, shared by 6,423 Twitter users. We then applied our framework to analyze this dataset thoroughly. Our analysis reveals eight key findings of different aspects of Twitter users' real-world behaviors on face privacy, and we provide quantitative and qualitative results to better explain these findings. We share the practical implications of our study to empower online platforms and users in addressing the face privacy problem efficiently.

Social media users generate vast amounts of content that may contain sensitive information, posing threats to online and real-world privacy and security. Existing automated classifiers focus primarily on preserving privacy in original posts while neglecting the potential privacy risks in comments. This article addresses this gap by presenting a comprehensive study on privacy leaks in textual comments. We curate a real-world dataset of 1250 tweet-comment interactions from Twitter and also introduce features to detect privacy leaks in comments. Using these features, we train various classifiers that achieve an average F-score of 0.86 on the Twitter dataset. We then use our methods to detect privacy leaks on another social media platform, namely Reddit, and we get an average F-score of 0.90, which demonstrates the adaptability of our methods. This research shows the significance of addressing privacy leaks in comments. We also show how our approach could work across two different social media platforms.

In argumentation theory, argument schemes are a characterisation of stereotypical patterns of inference.There has been little work done to develop computational approaches to identify these schemes in natural language.Moreover, advancements in recognizing textual entailment lack a standardized definition of inference, which makes it challenging to compare methods trained on different datasets and rely on the generalisability of their results.In this work, we propose a rigorous approach to align entailment recognition with argumentation theory.Wagemans' Periodic Table of Arguments (PTA), a taxonomy of argument schemes, provides the appropriate framework to unify these two fields.To operationalise the theoretical model, we introduce a tool to assist humans in annotating arguments according to the PTA.Beyond providing insights into non-expert annotator training, we present Kialo-PTA24, the first multi-topic dataset for the PTA.Finally, we benchmark the performance of pre-trained language models on various aspects of argument analysis.Our experiments show that the task of argument canonicalisation poses a significant challenge for state-of-the-art models, suggesting an inability to represent argumentative reasoning and a direction for future investigation.1. We conduct an annotation study to rephrase natural language arguments into structured templates and provide insights into how to train non-expert annotators to perform this analysis.2. We introduce ArgNotator, a tool that assists humans in annotating arguments according to the PTA.3. We construct Kialo-PTA24 -the first multi-topic dataset of argument types annotated according to the PTA.4. We compare the performance of state-of-theart models for two annotation subtasks.For the substance classification task, we benchmark the performance of a number of BERT-based models.For the argument canonicalisation task, we evaluate the performance of two large language models (FLAN-T5, LLAMA2) in both pre-trained and few-shot settings.The dataset, experimental setup, annotation tool and training materials can all be found on GitLab. 1

Sociotechnical systems (STSs) include artificial intelligence (AI) to make (semi-) automated decisions that impact our lives. Their reasoning processes still often remain unclear to people interacting with such systems, which may also harm people by making flawed decisions. The users do not have ways to challenge automated decisions and obtain proper restitution if necessary. Organizations may be willing to provide transparency about their decision-making process, but answering each of the questions people ask could be cumbersome. We propose a mediator agent framework that will bridge the gap between organizations that employ AI and people who were harmed by its automated decisions. Our approach helps the organizations to become answerable data practices, and it empowers people to report the harms, ask for clarifications, as well as remedies through dialogues. We implement a prototype to demonstrate the applicability of our approach through a real-world scenario.

Chatbots are conversational software applications designed to interact dialectically with users for a plethora of different purposes. Surprisingly, these colloquial agents have only recently been coupled with computational models of arguments (i.e. computational argumentation), whose aim is to formalise, in a machine-readable format, the ordinary exchange of information that characterises human communications. Chatbots may employ argumentation with different degrees and in a variety of manners. The present survey sifts through the literature to review papers concerning this kind of argumentation-based bot, drawing conclusions about the benefits and drawbacks that this approach entails in comparison with standard chatbots, while also envisaging possible future development and integration with the Transformer-based architecture and state-of-the-art Large Language models.

Argument mining seeks to extract arguments and their structure from unstructured texts.Identifying relations (such as attack, support, and neutral) between argumentative units is a challenging task because two units may be related to each other via implicit inferences.These inferences often rely on external commonsense knowledge to discover how one argumentative unit relates to another.State-of-the-art methods, however, rely on predefined knowledge graphs, and thus might not cover target pairs of argumentative units well.We introduce a new generative approach to finding inference chains that connect these pairs by making use of the Commonsense Transformer (COMET).We evaluate our approach on three datasets for both the two-label (attack/support) and three-label (attack/support/neutral) tasks.Our approach significantly outperforms the state-of-the-art, by 2-5% in F1 score, on two out of the three datasets with minor improvements on the remaining one.

People are interacting with online systems all the time. In order to use the services being provided, they give consent for their data to be collected. This approach requires too much human effort and is impractical for systems like Internet-of-Things (IoT) where human-device interactions can be large. Ideally, privacy assistants can help humans make privacy decisions while working in collaboration with them. In our work, we focus on the identification and representation of privacy requirements in IoT to help privacy assistants better understand their environment. In recent years, more focus has been on the technical aspects of privacy. However, the dynamic nature of privacy also requires a representation of social aspects (e.g., social trust). In this survey paper, we review the privacy requirements represented in existing IoT ontologies. We discuss how to extend these ontologies with new requirements to better capture privacy, and we introduce case studies to demonstrate the applicability of the novel requirements.

With a significant amount of software now being developed for use in patient-facing studies, there is a pressing need to consider how to design this software effectively in order to support the needs of both researchers and patients. We posit that a microservice architecture-which offers a large amount of flexibility for development and deployment, while at the same time ensuring certain quality attributes, such as scalability, are present-provides an effective mechanism for designing such software. To explore this proposition, in this work we show how the paradigm has been applied to the design of Consult, a decision support system that provides autonomous support to stroke patients and is characterised by its use of a data-backed AI reasoner. We discuss the impact that the use of this software architecture has had on the teams developing Consult and measure the performance of the system produced. We show that the use of microservices can deliver software that is able to facilitate both research and effective patient interactions. However, we also conclude that the impact of the approach only goes so far, with additional techniques needed to address its limitations.

Privacy on the Web is typically managed by giving consent to individual Websites for various aspects of data usage. This paradigm requires too much human effort and thus is impractical for Internet of Things (IoT) applications where humans interact with many new devices on a daily basis. Ideally, software privacy assistants can help by making privacy decisions in different situations on behalf of the users. To realize this, we propose an agent-based model for a privacy assistant. The model identifies the contexts that a situation implies and computes the trustworthiness of these contexts. Contrary to traditional trust models that capture trust in an entity by observing large number of interactions, our proposed model can assess the trustworthiness even if the user has not interacted with the particular device before. Moreover, our model can decide which situations are inherently ambiguous and thus can request the human to make the decision. We evaluate various aspects of the model using a real-life data set and report adjustments that are needed to serve different types of users well.

Conference paper presented at the Biennial International Conferences on Computational Models of Argument (COMMA), Perugia, Italy [Online], 8-11 September, 2020.

In this demo paper, we propose an encoding for Metalevel Argumentation Frameworks (MAFs) to be used in Aspartix, an Answer Set Programming (ASP) approach to find the justified arguments of an AF. MAFs provide a uniform encoding of object level Dung Frameworks and extensions thereof that include values, preferences and attacks on attacks (EAFs). The justification status of arguments in the object level AF can then be evaluated and explained through evaluation of the arguments in the MAF. The demo includes multiple examples from the literature to show the applicability of our proposed encoding for translating various object level AFs to the uniform language of MAFs.

We directly instantiate metalevel argumentation frameworks (MAFs) to enable argumentation-based reasoning about information relevant to various applications. The advantage of this is that information that typically cannot be incorporated via the instantiation of object-level argumentation frameworks can now be incorporated, in particular information referencing (1) preferences over arguments, (2) the rationale for attacks, and (3) the dialectical effect of critical questions that shifts the burden of proof when posed. We achieve this by using a variant of ASPIC+ and a higher-order typed language that can reference object-level formulae and arguments. We illustrate these representational advantages with a running example from clinical decision support.

This short paper describes the design of the CONSULT system, a decision-support tool intended to help patients suffering from chronic conditions self-manage their health. The system takes input from multiple sources, including commercial wellness sensors and patient's electronic health record, to inform an intelligent back-end that reasons about day-to-day health management decisions, customised for individual patients. The architecture of the system features a modular structure for allowing input from a range of different sources, a reasoning engine underpinned by computational argumentation that constructs weighted opinions using these inputs and knowledge about their sources, and an interaction agent driven by argumentation-based dialogue that responds to user queries.

Online social networks provide an environment for their users to share content with others, where the user who shares a content item is put in charge, generally ignoring others that might be affected by it. However, a content that is shared by one user can very well violate the privacy of other users. To remedy this, ideally, all users who are related to a content should get a say in how the content should be shared. Recent approaches advocate the use of agreement technologies to enable stakeholders of a post to discuss the privacy configurations of a post. This allows related individuals to express concerns so that various privacy violations are avoided up front. Existing techniques try to establish an agreement on a single post. However, most of the time, agreement should be established over multiple posts such that the user can tolerate slight breaches of privacy in return of a right to share posts themselves in future interactions. As a result, users can help each other preserve their privacy, viewing this as their social responsibility. This article develops a reciprocity-based negotiation for reaching privacy agreements among users and introduces a negotiation architecture that combines semantic privacy rules with utility functions. We evaluate our approach over multiagent simulations with software agents that mimic users based on a user study.

More and more, devices around us are being connected to each other in the realm of Internet of Things (IoT). Their communication and especially collaboration promises useful services to be provided to end users. However, the same communication channels pose important privacy concerns to be raised. It is not clear which information will be shared with whom, for which intents, under which conditions. Existing approaches to privacy advocate policies to be in place to regulate privacy. However, the scale and heterogeneity of the IoT entities make it infeasible to maintain policies among each and every entity in the system. Conversely, it is best if each entity can reason on the privacy using norms and context autonomously. Accordingly, this paper proposes an approach where each entity finds out which contexts it is in based on information it gathers from other entities in the system. The proposed approach uses argumentation to enable IoT entities to reason about their context and decide to reveal information based on it. We demonstrate the applicability of the approach over an IoT scenario.

Preserving users’ privacy is important for Web systems. In systems where transactions are managed by a single user, such as e-commerce systems, preserving privacy of the transactions is merely the capability of access control. However, in online social networks, where each transaction is managed by and has effect on others, preserving privacy is difficult. In many cases, the users’ privacy constraints are distributed, expressed in a high-level manner, and would depend on information that only becomes available over interactions with others. Hence, when a content is being shared by a user, others who might be affected by the content should discuss and agree on how the content will be shared online so that none of their privacy constraints are violated. To enable this, we model users of the social networks as agents that represent their users’ privacy constraints as semantic rules. Agents argue with each other on propositions that enable their privacy rules by generating facts and assumptions from their ontology. Moreover, agents can seek help from others by requesting new information to enrich their ontology. Using assumption-based argumentation, agents decide whether a content should be shared or not. We evaluate the applicability of our approach on real-life privacy scenarios in comparison with user surveys.

In online social networks (OSNs), users are allowed to create and share content about themselves and others. When multiple entities start distributing content, information can reach unintended individuals and inference can reveal more information about the user. Existing applications do not focus on detecting privacy violations before they occur in the system. This thesis proposes an agent-based representation of a social network, where the agents manage users' privacy requirements and create privacy agreements with agents. The privacy context, such as the relations among users, various content types in the system, and so on are represented with a formal language. By reasoning with this formal language, an agent checks the current state of the system to resolve privacy violations before they occur. We argue that commonsense reasoning could be useful to solve some of privacy examples reported in the literature. We will develop new methods to automatically identify private information using commonsense reasoning, which has never been applied to privacy context. Moreover, agents may have conflicting privacy requirements. We will study how to use agreement technologies in privacy settings for agents to resolve conflicts automatically.

Social network users expect the social networks that they use to preserve their privacy. Traditionally, privacy breaches have been understood as the malfunctioning of a given system. However, in online social networks, privacy breaches are not necessarily a malfunctioning of a system but a byproduct of its workings. The users are allowed to create and share content about themselves and others. When multiple entities start distributing content without a control, information can reach unintended individuals and inference can reveal more information about the user. Accordingly, this paper first categorizes the privacy violations that take place in online social networks. Our categorization yields that the privacy violations in online social networks stem from intricate interactions and detecting these violations requires semantic understanding of events. Our proposed approach is based on agent-based representation of a social network, where the agents manage users' privacy requirements by creating commitments with the system. The privacy context, including the relations among users or content types, are captured using description logic. The proposed detection algorithm performs reasoning using the description logic and commitments on a varying depths of social networks. We implement the proposed model and evaluate our approach using real-life social networks.

Radiologists inspect CT scans and record their observations in reports to communicate with physicians. These reports may suffer from ambiguous language and inconsistencies resulting from subjective reporting styles, which present challenges in interpretation. Standardization efforts, such as the lexicon RadLex for radiology terms, aim to address this issue by developing standard vocabularies. While such vocabularies handle consistent annotation, they fall short in sufficiently processing reports for intelligent applications. To support such applications, the semantics of the concepts as well as their relationships must be modeled, for which, ontologies are effective. They enable the software to make inferences beyond what is present in the reports. This paper presents the open-source ontology onlira (Ontology of the Liver for Radiology), which is developed to support such intelligent applications, such as identifying and ranking similar liver patient cases. onlira is introduced in terms of its concepts, properties, and relations. Examples of real liver patient cases are provided for illustration purposes. The ontology is evaluated in terms of its ability to express real liver patient cases and address semantic queries.