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Vision language models can now generate long-form answers to questions about images -- long-form visual question answers (LFVQA). We contribute VizWiz-LF, a dataset of long-form answers to visual questions posed by blind and low vision (BLV) users. VizWiz-LF contains 4.2k long-form answers to 600 visual questions, collected from human expert describers and six VQA models. We develop and annotate functional roles of sentences of LFVQA and demonstrate that long-form answers contain information beyond the question answer such as explanations and suggestions. We further conduct automatic and human evaluations with BLV and sighted people to evaluate long-form answers. BLV people perceive both human-written and generated long-form answers to be plausible, but generated answers often hallucinate incorrect visual details, especially for unanswerable visual questions (e.g., blurry or irrelevant images). To reduce hallucinations, we evaluate the ability of VQA models to abstain from answering unanswerable questions across multiple prompting strategies. 

People who are blind share their images and videos with companies that provide visual assistance technologies (VATs) to gain access to information about their surroundings. A challenge is that people who are blind cannot independently validate the content of the images and videos before they share them, and their visual data commonly contains private content. We examine privacy concerns for blind people who share personal visual data with VAT companies that provide descriptions authored by humans or artifcial intelligence (AI) . We frst interviewed 18 people who are blind about their perceptions of privacy when using both types of VATs. Then we asked the participants to rate 21 types of image content according to their level of privacy concern if the information was shared knowingly versus unknowingly with human- or AI-powered VATs. Finally, we analyzed what information VAT companies communicate to users about their collection and processing of users’ personal visual data through their privacy policies. Our fndings have implications for the development of VATs that safeguard blind users’ visual privacy, and our methods may be useful for other camera-based technology companies and their users.