We propose a harm-centric conceptualization of privacy and operationalize it in the context of using artificial intelligence (AI) in education and employment. In an online study (N=400), US college and university students reported their perceptions of 14 harms (e.g., manipulation) when AI infers personal data (e.g., demographics and personality traits) and use it in decision-making.

The education technology (EdTech) landscape is expanding rapidly in higher education institutes (HEIs). This growth brings enormous complexity. Protecting the extensive data collected by these tools is crucial for HEIs as data breaches and misuses can have dire security and privacy consequences for the data subjects, particularly students, who are often compelled to use these tools. This urges an in-depth understanding of HEI and EdTech vendor dynamics, which is largely understudied.

Researchers invested enormous efforts to understand and mitigate the concerns of users as technologies collect their private data. However, users often undermine other people’s privacy when, e.g., posting other people’s photos online, granting mobile applications to access contacts, or using technologies that continuously sense the surrounding. Research to understand technology adoption and behaviors related to collecting and sharing data about non-users has been severely lacking. An essential step to progress in this direction is to identify and quantify factors that affect technology’s use. Toward this goal, we propose and validate a psychometric scale to measure how much an individual values other people’s privacy. We theoretically grounded the appropriateness and relevance of the construct and empirically demonstrated the scale’s internal consistency and validity. This scale will advance the field by enabling researchers to predict behaviors, design adaptive privacy-enhancing technologies, and develop interventions to raise awareness and mitigate privacy risks.

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Misinformation is now a major problem due to its poten-tial high risks to our core democratic and societal values and orders. Out-of-context misinformation is one of the easiest and effective ways used by adversaries to spread vi-ral false stories. In this threat, a real image is re-purposed to support other narratives by misrepresenting its context and/or elements. The internet is being used as the go-to way to verify information using different sources and modali-ties. Our goal is an inspectable method that automates this time-consuming and reasoning-intensive process by fact-checking the image-caption pairing using Web evidence. To integrate evidence and cues from both modalities, we intro-duce the concept of ‘multi-modal cycle-consistency check’ starting from the image/caption, we gather tex-tual/visual evidence, which will be compared against the other paired caption/image, respectively. Moreover, we propose a novel architecture, Consistency-Checking Network (CCN), that mimics the layered human reasoning across the same and different modalities: the caption vs. textual evidence, the image vs. visual evidence, and the image vs. caption. Our work offers the first step and bench-mark for open-domain, content-based, multi-modal fact-checking, and significantly outperforms previous baselines that did not leverage external evidence11For code, checkpoints, and dataset, check: https://s-abdelnabi.github.io/OoC-multi-modal-fc/.

Abstract Education technologies (EdTech) are becoming pervasive due to their cost-effectiveness, accessibility, and scalability. They also experienced accelerated market growth during the recent pandemic. EdTech collects massive amounts of students’ behavioral and (sensitive) demographic data, often justified by the potential to help students by personalizing education. Researchers voiced concerns regarding privacy and data abuses (e.g., targeted advertising) in the absence of clearly defined data collection and sharing policies. However, technical contributions to alleviating students’ privacy risks have been scarce. In this paper, we argue against collecting demographic data by showing that gender—a widely used demographic feature—does notcausallyaffect students’ course performance: arguably the most popular target of predictive models. Then, we show that gender can be inferred from behavioral data; thus, simply leaving them out does not protect students’ privacy. Combining a feature selection mechanism with an adversarial censoring technique, we propose a novel approach to create a ‘private’ version of a dataset comprising of fewer features that predict the target without revealing the gender, and are interpretive. We conduct comprehensive experiments on a public dataset to demonstrate the robustness and generalizability of our mechanism.

We investigate how people’s ‘humor style’ relates to their online photo-sharing behaviors and reactions to ‘privacy primes’. In an online experiment, we queried 437 participants about their humor style, likelihood to share photo-memes, and history of sharing others’ photos. In two treatment conditions, participants were either primed to imagine themselves as the photo-subjects or to consider the photo-subjects’ privacy before sharing memes. We found that participants who frequently use aggressive and self-deprecating humor were more likely to violate others’ privacy by sharing photos. We also replicated the interventions’ paradoxical effects – increasing sharing likelihood – as reported in earlier work and identified the subgroups that demonstrated this behavior through interaction analyses. When primed to consider the subjects’ privacy, only humor deniers (participants who use humor infrequently) demonstrated increased sharing. In contrast, when imagining themselves as the photo-subjects, humor deniers, unlike other participants, did not increase the sharing of photos.

We investigate the effects of perspective taking, privacy cues, and portrayal of photo subjects (i.e., photo valence) on decisions to share photos of people via social media. In an online experiment we queried 379 participants about 98 photos (that were previously rated for photo valence) in three conditions: (1) Baseline: participants judged their likelihood of sharing each photo; (2) Perspective-taking: participants judged their likelihood of sharing each photo when cued to imagine they are the person in the photo; and (3) Privacy: participants judged their likelihood to share after being cued to consider the privacy of the person in the photo. While participants across conditions indicated a lower likelihood of sharing photos that portrayed people negatively, they - surprisingly - reported a higher likelihood of sharing photos when primed to consider the privacy of the person in the photo. Frequent photo sharers on real-world social media platforms and people without strong personal privacy preferences were especially likely to want to share photos in the experiment, regardless of how the photo portrayed the subject. A follow-up study with 100 participants explaining their responses revealed that the Privacy condition led to a lack of concern with others' privacy. These findings suggest that developing interventions for reducing photo sharing and protecting the privacy of others is a multivariate problem in which seemingly obvious solutions can sometimes go awry.

Photographs taken in public places often contain bystanders - people who are not the main subject of a photo. These photos, when shared online, can reach a large number of viewers and potentially undermine the bystanders' privacy. Furthermore, recent developments in computer vision and machine learning can be used by online platforms to identify and track individuals. To combat this problem, researchers have proposed technical solutions that require bystanders to be proactive and use specific devices or applications to broadcast their privacy policy and identifying information to locate them in an image.We explore the prospect of a different approach - identifying bystanders solely based on the visual information present in an image. Through an online user study, we catalog the rationale humans use to classify subjects and bystanders in an image, and systematically validate a set of intuitive concepts (such as intentionally posing for a photo) that can be used to automatically identify bystanders. Using image data, we infer those concepts and then use them to train several classifier models. We extensively evaluate the models and compare them with human raters. On our initial dataset, with a 10-fold cross validation, our best model achieves a mean detection accuracy of 93% for images when human raters have 100% agreement on the class label and 80% when the agreement is only 67%. We validate this model on a completely different dataset and achieve similar results, demonstrating that our model generalizes well.

Pervasive photo sharing in online social media platforms can cause unintended privacy violations when elements of an image reveal sensitive information. Prior studies have identified image obfuscation methods (e.g., blurring) to enhance privacy, but many of these methods adversely affect viewers' satisfaction with the photo, which may cause people to avoid using them. In this paper, we study the novel hypothesis that it may be possible to restore viewers' satisfaction by 'boosting' or enhancing the aesthetics of an obscured image, thereby compensating for the negative effects of a privacy transform. Using a between-subjects online experiment, we studied the effects of three artistic transformations on images that had objects obscured using three popular obfuscation methods validated by prior research. Our findings suggest that using artistic transformations can mitigate some negative effects of obfuscation methods, but more exploration is needed to retain viewer satisfaction.

With the rise of digital photography and social networking, people are sharing personal photos online at an unprecedented rate. In addition to their main subject matter, photographs often capture various incidental information that could harm people's privacy. While blurring and other image filters may help obscure private content, they also often affect the utility and aesthetics of the photos, which is important since images shared in social media are mainly for human consumption. Existing studies of privacy-enhancing image filters either primarily focus on obscuring faces, or do not systematically study how filters affect image utility. To understand the trade-offs when obscuring various sensitive aspects of images, we study eleven filters applied to obfuscate twenty different objects and attributes, and evaluate how effectively they protect privacy and preserve image quality for human viewers.