Signals of Provenance: Practices & Challenges of Navigating Indicators in AI-Generated Media for Sighted and Blind Individuals

AI-Generated (AIG) content has become increasingly widespread by recent advances in generative models and the easy-to-use tools that have significantly lowered the technical barriers for producing highly realistic audio, images, and videos through simple natural language prompts. In response, platforms are adopting provable provenance with platforms recommending AIG to be self-disclosed and signaled to users. However, these indicators may be often missed, especially when they rely solely on visual cues and make them ineffective to users with different sensory abilities. To address the gap, we conducted semi-structured interviews (N=28) with 15 sighted and 13 BLV participants to examine their interaction with AIG content through self-disclosed AI indicators. Our findings reveal diverse mental models and practices, highlighting different strengths and weaknesses of content-based (e.g., title, description) and menu-aided (e.g., AI labels) indicators. While sighted participants leveraged visual and audio cues, BLV participants primarily relied on audio and existing assistive tools, limiting their ability to identify AIG. Across both groups, they frequently overlooked menu-aided indicators deployed by platforms and rather interacted with content-based indicators such as title and comments. We uncovered usability challenges stemming from inconsistent indicator placement, unclear metadata, and cognitive overload. These issues were especially critical for BLV individuals due to the insufficient accessibility of interface elements. We provide practical recommendations and design implications for future AIG indicators across several dimensions.

Personhood Credentials: Human-Centered Design Recommendation Balancing Security, Usability, and Trust

Building on related concepts, like, decentralized identifiers (DIDs), proof of personhood, anonymous credentials, personhood credentials (PHCs) emerged as an alternative approach, enabling individuals to verify to digital service providers that they are a person without disclosing additional information. However, new technologies might introduce some friction due to users misunderstandings and mismatched expectations. Despite their growing importance, limited research has been done on users perceptions and preferences regarding PHCs. To address this gap, we conducted competitive analysis, and semi-structured online user interviews with 23 participants from US and EU to provide concrete design recommendations for PHCs that incorporate user needs, adoption rules, and preferences. Our study -- (a)surfaces how people reason about unknown privacy and security guarantees of PHCs compared to current verification methods -- (b) presents the impact of several factors on how people would like to onboard and manage PHCs, including, trusted issuers (e.g. gov), ground truth data to issue PHC (e.g biometrics, physical id), and issuance system (e.g. centralized vs decentralized). In a think-aloud conceptual design session, participants recommended -- conceptualized design, such as periodic biometrics verification, time-bound credentials, visually interactive human-check, and supervision of government for issuance system. We propose actionable designs reflecting users preferences.

BIV-Priv-Seg: Locating Private Content in Images Taken by People With Visual Impairments

Individuals who are blind or have low vision (BLV) are at a heightened risk of sharing private information if they share photographs they have taken. To facilitate developing technologies that can help preserve privacy, we introduce BIV-Priv-Seg, the first localization dataset originating from people with visual impairments that shows private content. It contains 1,028 images with segmentation annotations for 16 private object categories. We first characterize BIV-Priv-Seg and then evaluate modern models' performance for locating private content in the dataset. We find modern models struggle most with locating private objects that are not salient, small, and lack text as well as recognizing when private content is absent from an image. We facilitate future extensions by sharing our new dataset with the evaluation server at https://vizwiz.org/tasks-and-datasets/object-localization.