# JEEVHITAA -- An HCAI Ecosystem to Support Collective Care

arXiv: https://arxiv.org/abs/2512.06364 · HTML: https://mlsanity.com/paper/2512.06364/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 69 references that are in the map.
- First posted on arXiv 6 Dec 2025.

## Builds on (most cited first, 9 of 9)

- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach](https://mlsanity.com/paper/2002.01092/) (2020)
- [Sensing Wellbeing in the Workplace, Why and For Whom? Envisioning Impacts with Organizational Stakeholders](https://mlsanity.com/paper/2303.06794/) (2023)
- [NeckSense: A Multi-Sensor Necklace for Detecting Eating Activities in Free-Living Conditions](https://mlsanity.com/paper/1911.07179/) (2019)
- [User Characteristics in Explainable AI: The Rabbit Hole of Personalization?](https://mlsanity.com/paper/2403.00137/) (2024)
- [Not Now, Ask Later: Users Weaken Their Behavior Change Regimen Over Time, But Expect To Re-Strengthen It Imminently](https://mlsanity.com/paper/2101.11743/) (2021)
- [Diagrammatization and Abduction to Improve AI Interpretability With Domain-Aligned Explanations for Medical Diagnosis](https://mlsanity.com/paper/2302.01241/) (2023)
- [Towards Automated Fatigue Assessment using Wearable Sensing and Mixed-Effects Models](https://mlsanity.com/paper/2108.04022/) (2021)
- [Unpacking "Personal" Health Informatics for Proactive Collective Care](https://mlsanity.com/paper/2509.01231/) (2025)

## Abstract (arXiv)

Current mobile health platforms are predominantly individual-centric and lack the support for coordinated, auditable multi-actor workflows. However, in many settings worldwide, health decisions are enacted through multi-actor coordination rather than individual users. We present JEEVHITAA, a cross-platform mobile system enabling role-aware sharing and verifiable information flows within permissioned care circles. JEEVHITAA ingests platform and device data, builds layered profiles from sensors and tiered onboarding, and enforces fine-grained, time-bounded access control across care graphs. Data stays secure both within the application and the cloud. Integrated retrieval-augmented Large Language Models produce structured, role-targeted summaries and action plans, offer evidence-grounded verification with provenance and confidence scores, and support advanced insights on reports. We describe the architecture, connector abstractions, and security primitives, and report robustness evaluations using synthetic, ontology-driven data and findings from a feasibility study with real-life care circles across 9-14 weeks. We outline plans for larger multi-site evaluations focusing on operational alignment, longitudinal trust & literacy impact, and relational friction & efforts to sink into the daily infrastructure.