Andrés Monroy-Hernández's research while affiliated with Princeton University and other places

This forum is dedicated to exploring the notion of meaningfulness in design processes, taking the perspectives of community groups, nongovernmental organizations, and those who are marginalized in society as starting points. Authors will reflect conceptually and methodologically on practical engagements. --- Rosanna Bellini and Angelika Strohmayer, Editors

Trust is an important factor in people's interactions with AI systems. However, there is a lack of empirical studies examining how real end-users trust or distrust the AI system they interact with. Most research investigates one aspect of trust in lab settings with hypothetical end-users. In this paper, we provide a holistic and nuanced understanding of trust in AI through a qualitative case study of a real-world computer vision application. We report findings from interviews with 20 end-users of a popular, AI-based bird identification app where we inquired about their trust in the app from many angles. We find participants perceived the app as trustworthy and trusted it, but selectively accepted app outputs after engaging in verification behaviors, and decided against app adoption in certain high-stakes scenarios. We also find domain knowledge and context are important factors for trust-related assessment and decision-making. We discuss the implications of our findings and provide recommendations for future research on trust in AI.

Social media platforms support the sharing of written text, video, and audio. All of these formats may be inaccessible to people who are deaf or hard of hearing (DHH), particularly those who primarily communicate via sign language, people who we call Deaf signers. We study how Deaf signers engage with social platforms, focusing on how they share content and the barriers they face. We employ a mixed-methods approach involving seven in-depth interviews and a survey of a larger population (n = 60). We find that Deaf signers share the most in written English, despite their desire to share in sign language. We further identify key areas of difficulty in consuming content (e.g., lack of captions for spoken content in videos) and producing content (e.g., captioning signed videos, signing into a phone camera) on social media platforms. Our results both provide novel insights into social media use by Deaf signers and reinforce prior findings on DHH communication more generally, while revealing potential ways to make social media platforms more accessible to Deaf signers.

Social media platforms support the sharing of written text, video, and audio. All of these formats may be inaccessible to people who are deaf or hard of hearing (DHH), particularly those who primarily communicate via sign language, people who we call Deaf signers. We study how Deaf signers engage with social platforms, focusing on how they share content and the barriers they face. We employ a mixed-methods approach involving seven in-depth interviews and a survey of a larger population (n = 60). We find that Deaf signers share the most in written English, despite their desire to share in sign language. We further identify key areas of difficulty in consuming content (e.g., lack of captions for spoken content in videos) and producing content (e.g., captioning signed videos, signing into a phone camera) on social media platforms. Our results both provide novel insights into social media use by Deaf signers and reinforce prior findings on DHH communication more generally, while revealing potential ways to make social media platforms more accessible to Deaf signers.

Social media platforms are deeply ingrained in society, and they offer many different spaces for people to engage with others. Unfortunately, accessibility barriers prevent people with disabilities from fully participating in these spaces. Social media users commonly post inaccessible media, including videos without captions (which are important for people who are Deaf or Hard of Hearing) and images without alternative text (descriptions read aloud by screen readers for people who are blind). Users with motor impairments must find workarounds to deal with the complex user interfaces of these platforms, and users with cognitive disabilities may face barriers to composing and sharing information. We invited accessibility researchers, industry practitioners, and end-users with disabilities to come together at the Computer-Supported Cooperative Work conference (CSCW 2019) to discuss challenges and solutions for improving social media accessibility. Over the course of a day that included two panels and breakout sessions, the workshop attendees outlined four critical future research directions to progress on the path to accessible social media: tooling to support disabled people authoring content, developing more accessible formats/tools for new forms of interaction (e.g, Augmented and Mixed Reality), using communities to distribute accessibility labor, and ensuring machine learning systems are built on representative datasets for disability use-cases.

For over 20 years, the academic human-computer interaction (HCI) and computer supported collaborative work (CSCW) communities have conducted research on user-facing hardware, software, and social media platforms dominated by a few key firms. Even as the contributions made by these scholars have shaped the development of these technologies, so too does industry funding play a key role in convening and supporting our community. This panel brings together HCI researchers for a reflective conversation on industry funding support for HCI.