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Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video Vision-Language Models Suppress Female Representations Under Ambiguous Input The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace TUX: Measuring Human--AI Tacit Understanding LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback VideoFDB: Evaluating Full-Duplex Vision-Speech Capabilities in Conversational Agents Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Analyzing Persona Effects in Generated Explanations from Multimodal LLM Agents in Urban Perception First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams Bayesian Distributional Models of Executive Functioning Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts Data-driven Head Motion Generation through Natural Gaze-Head Coordination Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why Perceptually Lossless Tactile Texture Synthesis with Compact Spectral Envelope Models MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation Augmented Analytics and Decision Quality: The Role of Trust among Non-Technical BI Users Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build PaintCopilot: Modeling Painting as Autonomous Artistic Continuation Personality Engineering with AI Agents: A New Methodology for Negotiation Research PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis nASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI
Reimagining AI: Exploring Speculative Design Workshops fo...
Sadhbh Kenny, Alissa N. Antle · 2024-05-07 · via cs.HC updates on arXiv.org

As Artificial Intelligence ecosystems become increasingly entangled within our everyday lives, designing systems that are ethical, inclusive and socially just is more vital than ever. It is well known that AI can algorithmic biases that reflect, extend and exacerbate our existing systemic injustices. Yet, despite most teenagers interacting with AI daily, only few have the opportunity to learn how it works and its socio-technical complexities. This is a particularly salient issue for marginalized communities. BIPOC teens are often misrepresented throughout AI development and implementation, but they are also less likely to receive STEM education.In response to these unprecedented socio-technical challenges and calls for more critical approaches to child-centered AI design and education, we explore how we can leverage co-speculative design practices to help scaffold BIPOC youth (ages 14-17) critiques of existing AI systems and support the re-imagining of more just AI futures. Drawing on Harway's Situated Knowledges and Speculative Fabulations, these workshops highlight the unique ways marginalized youth perceive AI as having social and ethical implications and how they envision alternative worlds with AI. Our case study describes three 2 hour sessions of a larger 8 week black-led AI STEM program. Analysis includes, data from pre-post surveys, workshop recordings, focus group discussions, learning artifacts, and field notes. We contribute 1) a discussion of how youth perceive AI as having social and ethical implications, 2) a nuanced understanding of how speculative approaches can be leveraged to support youth engagement with complex socio-technical issues and 3) enable youth to open up new AI possibilities in a world absent of techno-capitalist values.