Jieqiong Ding

Jieqiong Ding, Yumo Zhang, Xiuqi Tommy Zhu et al.

Meaningful social interaction is vital to well-being, yet Blind and Low Vision (BLV) individuals face persistent barriers when collaborating with sighted peers due to inaccessible visual cues. While most wearable assistive technologies emphasize individual tasks, smart glasses introduce opportunities for real-time, contextual support in social settings. To explore how smart glasses affect interpersonal dynamics and support inclusion in mixed-vision groups, we developed a smart glasses-based system, CollabLens, as a technology probe and employed it in four workshop sessions. We found that smart glasses can meaningfully support inclusive collaboration through expanding BLV participants' assistive networks with more flexible, independent access to visual information. While sighted participants viewed smart glasses as a promising medium that fosters interpersonal connection, they revealed uncertainty in adapting their helping behaviors. We concluded by discussing and synthesizing challenges and opportunities for designing smart glasses that provide seamless interaction experiences and enhance reciprocal mixed-vision social inclusion.

Priyanshi Garg, Ishita Rao, Jieqiong Ding et al.

Clinical NLP increasingly relies on electronic health record (EHR) data to detect suicidal behaviors, treating clinical documentation as more reliable ground truth than social media. We argue that this framing obscures how EHR-based suicidality datasets encode a particular operationalization of suicidality, shaped by who authors the data, how episodes are bounded, and how ambiguity is resolved. We ground this argument in a case study of the ScAN dataset, built over MIMIC-III clinical notes. We show how governance constraints, ICD-based cohort selection, single-annotator labeling, and hospital-stay-level aggregation produce labels that reflect clinician-documented judgments, treat suicidality as a bounded episode, and assume that intent can be reliably inferred from documentation. A linguistic analysis demonstrates that identical labels subsume heterogeneous clinical framings differing in temporality, negation, and uncertainty. We argue that clinical NLP should examine the assumptions embedded in suicidality datasets before interpreting their labels as ground truth.