“She who laughs most, learns best!”

Summary

Anonymity is an important principle online. However, malicious actors have long used misleading identities to conduct fraud, spread disinformation, and carry out other deceptive schemes. With the advent of increasingly capable AI, bad actors can amplify the potential scale and effectiveness of their operations, intensifying the challenge of balancing anonymity and trustworthiness online. In this paper, we analyze the value of a new tool to address this challenge: "personhood credentials" (PHCs), digital credentials that empower users to demonstrate that they are real people -- not AIs -- to online services, without disclosing any personal information. Such credentials can be issued by a range of trusted institutions -- governments or otherwise. A PHC system, according to our definition, could be local or global, and does not need to be biometrics-based. Two trends in AI contribute to the urgency of the challenge: AI's increasing indistinguishability from people online (i.e., lifelike content and avatars, agentic activity), and AI's increasing scalability (i.e., cost-effectiveness, accessibility). Drawing on a long history of research into anonymous credentials and "proof-of-personhood" systems, personhood credentials give people a way to signal their trustworthiness on online platforms, and offer service providers new tools for reducing misuse by bad actors. In contrast, existing countermeasures to automated deception -- such as CAPTCHAs -- are inadequate against sophisticated AI, while stringent identity verification solutions are insufficiently private for many use-cases. After surveying the benefits of personhood credentials, we also examine deployment risks and design challenges. We conclude with actionable next steps for policymakers, technologists, and standards bodies to consider in consultation with the public.

Summary

Building upon the framework established in my previous work ''Mitigating Barriers to Public Social Interaction with Meronymous Communication,`` this project endeavors to extend the application of meronymous communication beyond academia to a broader social media landscape. Meronymity, as previously defined, entails disclosing verified aspects of one's identity to lend credibility while maintaining a level of anonymity to alleviate social apprehensions in high stake interactions.

The focus of this endeavor lies in integrating meronymity into a general-purpose social media platform that I am building, where users engage in discussions spanning diverse topics. My attention particularly gravitates towards facilitating sensitive conversations that possess the potential to impact minority communities or vulnerable individuals adversely. To ensure the integrity of such discussions, robust moderation mechanisms are imperative. In this context, I advocate for individual-based moderation, wherein users wield agency over the content they encounter. By empowering users to curate their viewing experience based on personal thresholds of sensitivity, I aim to foster an environment conducive to open dialogue while safeguarding against potential harm. Central to my approach is the implementation of a trust-based system for individual content propagation. This personalized moderation strategy acknowledges the nuanced nature of sensitivity across different individuals, thereby enhancing the effectiveness of content management in sensitive discussions. Through this project, I aspire to cultivate a socially inclusive digital space where meronymous communication serves as a catalyst for meaningful discourse while prioritizing the preferences of all participants.

Abstract

In communities with social hierarchies, fear of judgment can discourage communication. While anonymity may alleviate some social pressure, fully anonymous spaces enable toxic behavior and lack the social context that motivates people to participate and helps them tailor their communication. We explore a design space of meronymous communication, where people can reveal carefully chosen aspects of their identity and also leverage trusted endorsers to gain credibility. We implemented these ideas in a system for scholars to meronymously seek and receive paper recommendations on Twitter and Mastodon. A formative study with 20 scholars confirmed that scholars see benefits to participating but are deterred due to social anxiety. From a month-long public deployment, we found that with meronymity, junior scholars could comfortably ask “newbie” questions and get responses from senior scholars who they normally found intimidating. Responses were also tailored to the aspects about themselves that junior scholars chose to reveal.

Abstract

In order to help scholars understand and follow a research topic, significant research has been devoted to creating systems that help scholars discover relevant papers and authors. Recent approaches have shown the usefulness of highlighting relevant authors while scholars engage in paper discovery. However, these systems do not capture and utilize users’ evolving knowledge of authors. We reflect on the design space and introduce ComLittee, a literature discovery system that supports author-centric exploration. In contrast to paper-centric interaction in prior systems, ComLittee’s author-centric interaction supports curating research threads from individual authors, finding new authors and papers using combined signals from a paper recommender and the curated authors’ authorship graphs, and understanding them in the context of those signals. In a within-subjects experiment that compares to a paper-centric discovery system with author-highlighting, we demonstrate how ComLittee improves author and paper discovery.

Abstract

Collaborative project management involves interacting with various tasks in a shared planning space where members add, assign, complete, and edit project-related tasks to have a shared view of the project's status. This process directly impacts how individual team members select, prioritize, and organize tasks on which to focus on a daily basis. However, such coordination and task prioritization can become increasingly challenging for individuals working on multiple projects with big teams. Accordingly, tasks could become at risk and eventually not be completed on time, leading to personal or team losses in many situations. To support task-doers in completing their tasks, we conducted a mixed-methods study focusing on Microsoft Planner---a collaborative project management tool---to understand how users manage their tasks in a team setting, what challenges they encounter, and their preferred solutions. Based on the findings from a qualitative survey with 151 participants and our Planner log data analysis, we further developed a task at risk prediction model using various task characteristics and user actions. Our experimental results suggest that a task at risk can be classified with high effectiveness (accuracy of 89\%). Our work provides novel insights on how users manage their tasks in team task management tools, what challenges they face, how they perceive a task at risk, and how tasks at risk can be modeled. Such an application can significantly improve the user experience in such tools by providing a personal assistant that helps users prioritize their tasks and pay attention to critical situations.

Summary

Large online discussions happen extensively on the web to exchange information, insights, humor, diverse opinions and others’ experiences. Archiving conversations available for future retrieval or contribution allows faster and more efficient consumption of information as well as better collaborations. With the growing number of tools and channels becoming available for researchers to engage with the public, and the rising significance of online platforms like science blogs, social media and sub-communities and engaging with audience, developing platforms tailored to guide, encourage and archive academic conversations around research papers is very valuable.

Abstract

Alzheimer's disease is estimated to affect around 50 million people worldwide and is rising rapidly, with a global economic burden of nearly a trillion dollars. This calls for scalable, cost-effective, and robust methods for detection of Alzheimer's dementia (AD). We present a novel architecture that leverages acoustic, cognitive, and linguistic features to form a multimodal ensemble system. It uses specialized artificial neural networks with temporal characteristics to detect AD and its severity, which is reflected through Mini-Mental State Exam (MMSE) scores. We first evaluate it on the ADReSS challenge dataset, which is a subject-independent and balanced dataset matched for age and gender to mitigate biases, and is available through DementiaBank. Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 83.3% each for AD classification, and state-of-the-art test root mean squared error (RMSE) of 4.60 for MMSE score regression. To the best of our knowledge, the system further achieves state-of-the-art AD classification accuracy of 88.0% when evaluated on the full benchmark DementiaBank Pitt database. Our work highlights the applicability and transferability of spontaneous speech to produce a robust inductive transfer learning model, and demonstrates generalizability through a task-agnostic feature-space.

Abstract

We introduce a novel interactive narrative exhibit supporting general public learning about Hip Hop culture and history developed as a collaboration of the MIT Center for Advanced Virtuality, the Universal Hip Hop Museum, and Microsoft and supported by the TunesMap Educational Foundation and internationally known Afrofuturist artists Black Kirby. The exhibit's narrative system is personalized by categorizing users based on evaluating their input data light of a social psychology-based model based in musical identity theory. The system uses user input to determine which interactive narrative and customized music playlist to present to the user. The system has been deployed as the central interactive display within the [R]Evolution of Hip Hop for an exhibit of the Universal Hip Hop Museum. Future work will involve analysis of user feedback data from the thousands of local and international exhibit visitors to determine the impact of personalization on visitor engagement, satisfaction, and learning.

Abstract

Online social platforms allow users to filter out content they do not like. According to selective exposure theory, people tend to view content they agree with more to get more self-assurance. This causes people to live in ideological filter bubbles. We report on a user study that encourages users to break the political filter bubble of their Twitter feed by reading more diverse viewpoints through social comparison. The user study is conducted using political-bias-analyzing and Twitter-mirroring tools to compare the political slant of what a user reads and what other Twitter users read about a topic, and in general. The results show that social comparison can have a great impact on users’ reading behavior by motivating them to read viewpoints from the opposing political party.