Providing awareness, explanation and control of personalized filtering in a social networking site

Social networking sites (SNSs) have applied personalized filtering to deal with overwhelmingly irrelevant social data. However, due to the focus of accuracy, the personalized filtering often leads to “the filter bubble” problem where the users can only receive information that matches their pre-stated preferences but fail to be exposed to new topics. Moreover, these SNSs are black boxes, providing no transparency for the user about how the filtering mechanism decides what is to be shown in the activity stream. As a result, the user’s usage experience and trust in the system can decline. This paper presents an interactive method to visualize the personalized filtering in SNSs. The proposed visualization helps to create awareness, explanation, and control of personalized filtering to alleviate the “filter bubble” problem and increase the users’ trust in the system. Three user evaluations are presented. The results show that users have a good understanding about the filter bubble visualization, and the visualization can increase users’ awareness of the filter bubble, understandability of the filtering mechanism and to a feeling of control over the data stream they are seeing. The intuitiveness of the design is overall good, but a context sensitive help is also preferred. Moreover, the visualization can provide users with better usage experience and increase users’ trust in the system

Physiological Indicators for User Trust in Machine Learning with Influence Enhanced Fact-Checking

© IFIP International Federation for Information Processing 2019. Trustworthy Machine Learning (ML) is one of significant challenges of “black-box” ML for its wide impact on practical applications. This paper investigates the effects of presentation of influence of training data points on machine learning predictions to boost user trust. A framework of fact-checking for boosting user trust is proposed in a predictive decision making scenario to allow users to interactively check the training data points with different influences on the prediction by using parallel coordinates based visualization. This work also investigates the feasibility of physiological signals such as Galvanic Skin Response (GSR) and Blood Volume Pulse (BVP) as indicators for user trust in predictive decision making. A user study found that the presentation of influences of training data points significantly increases the user trust in predictions, but only for training data points with higher influence values under the high model performance condition, where users can justify their actions with more similar facts to the testing data point. The physiological signal analysis showed that GSR and BVP features correlate to user trust under different influence and model performance conditions. These findings suggest that physiological indicators can be integrated into the user interface of AI applications to automatically communicate user trust variations in predictive decision making

Providing awareness, explanation and control of personalized stream filtering in a P2P social network

In Online Social Networks (OSNs), users are often overwhelmed with a huge amount of social data, most of which are irrelevant to their interest. Filtering of the social data stream is the common way to deal with this problem, and it has already been applied by OSNs, such as Facebook and Google+. Unfortunately, personalized filtering leads to “the filter bubble” problem where the user is trapped inside a world within the limited boundaries of her interests and cannot be exposed to any surprising, desirable information. Moreover, these OSNs are black boxes, providing no transparency for the user about how the filtering mechanism decides what is to be shown in the activity stream. As a result, the user trust in the system can decline. This thesis presents an interactive method to visualize the personalized stream filtering in OSNs. The proposed visualization helps to create awareness, explanation, and control of personalized stream filtering to alleviate “the filter bubble” problem and increase the users’ trust in the system. The visualization is implemented in MADMICA – a new privacy-aware decentralized OSN, based on the Friendica P2P protocol, which filters the social updates stream of users based on their interests. The results of three user evaluations are presented in this thesis: small-scale pilot study, qualitative study and large-scale quantitative study with 326 participants. The results of the small-scale study show that the filter bubble visualization makes the users aware of the filtering mechanism, engages them in actions to correct and change it, and as a result, increases the users’ trust in the system. The qualitative study reveals a generally higher proportion of desirable user perceptions for the awareness, explanation and control of the filter bubble provided by the visualization. Moreover, the results of the quantitative study demonstrate that the visualization leads to increased users’ awareness of the filter bubble, understandability of the filtering mechanism and to a feeling of control over the data stream they are seeing

Artefacts and Errors: Acknowledging Issues of Representation in the Digital: Imaging of Ancient Texts

It is assumed, in palaeography, papyrology and epigraphy, that a certain amount of uncertainty is inherent in the reading of damaged and abraded texts. Yet we have not really grappled with the fact that, nowadays, as many scholars tend to deal with digital images of texts, rather than handling the texts themselves, the procedures for creating digital images of texts can insert further uncertainty into the representation of the text created. Technical distortions can lead to the unintentional introduction of ‘artefacts’ into images, which can have an effect on the resulting representation. If we cannot trust our digital surrogates of texts, can we trust the readings from them? How do scholars acknowledge the quality of digitised images of texts? Furthermore, this leads us to the type of discussions of representation that have been present in Classical texts since Plato: digitisation can be considered as an alternative form of representation, bringing to the modern debate of the use of digital technology in Classics the familiar theories of mimesis (imitation) and ekphrasis (description): the conversion of visual evidence into explicit descriptions of that information, stored in computer files in distinct linguistic terms, with all the difficulties of conversion understood in the ekphratic process. The community has not yet considered what becoming dependent on digital texts means for the field, both in practical and theoretical terms. Issues of quality, copying, representation, and substance should be part of our dialogue when we consult digital surrogates of documentary material, yet we are just constructing understandings of what it means to rely on virtual representations of artefacts. It is necessary to relate our understandings of uncertainty in palaeography and epigraphy to our understanding of the mechanics of visualization employed by digital imaging techniques, if we are to fully understand the impact that these will have

Conceptualization of a Trust Dashboard for Distributed Usage Control Systems

Achieving data protection and privacy in modern data processing systems is a prominent topic of academic research today. The goal of retaining comprehensive informational sovereignty requires new and innovative solutions, both technological and methodological in nature. Distributed usage control is a popular technology that can give data providers the ability to actively govern the usage of their personal information even in remote systems. However, the architecture of distributed usage control systems is rather complex and often highly dynamic. This makes the assessment of the system’s soundness and trustworthiness difficult, especially for untrained laypersons. In this work we present the concept of a trust dashboard for distributed usage control systems that are backed by trusted computing technologies. The trust dashboard is intended to give users a visual intuition about the current state of the usage control system and its trustworthiness. We achieve this by using a formal model to describe relevant trust dependencies and the actually conducted remote attestations between usage control components, as well as a-priori trust levels for system operators. Based on this we propose a visualization concept that illustrates the current system state and estimates the overall trustworthiness of the system. Ultimately the trust dashboard aids system operators in the assessment of dynamic and distributed usage control architectures

Design Criteria for Transparent Mobile Event Recommendations

Recommender systems assist the user to overcome the information overflow of today’s information society. When a recommendation failed, user’s trust in a system decreases due to the fact that most recommender systems act as black boxes. They don’t offer any insight into the systems logic and cannot be questioned as it is normal for recommendations between humans. Users don’t know how and which personal information is processed. Transparency, which is about explaining to the user why a recommendation is made, allows understanding the nature of a recommendation. Within a mobile environment, it is possible to address the user more individualized but transparency needs a completely different way of visualization and interaction. The paper in hand aims at an analysis of a survey which asked about the kind of style element as well as how much information should be visualized on a mobile device in order to offer transparency

Visualizing recommendations to support exploration, transparency and controllability

Research on recommender systems has traditionally focused on the development of algorithms to improve accuracy of recommendations. So far, little research has been done to enable user interaction with such systems as a basis to support exploration and control by end users. In this paper, we present our research on the use of information visualization techniques to interact with recommender systems. We investigated how information visualization can improve user understanding of the typically black-box rationale behind recommendations in order to increase their perceived relevance and meaning and to support exploration and user involvement in the recommendation process. Our study has been performed using TalkExplorer, an interactive visualization tool developed for attendees of academic conferences. The results of user studies performed at two conferences allowed us to obtain interesting insights to enhance user interfaces that integrate recommendation technology. More specifically, effectiveness and probability of item selection both increase when users are able to explore and interrelate multiple entities - i.e. items bookmarked by users, recommendations and tags. Copyright © 2013 ACM