# Now You See Me: Designing Responsible AI Dashboards for Early-Stage Health Innovation

arXiv: https://arxiv.org/abs/2602.23378 · HTML: https://mlsanity.com/paper/2602.23378/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 71 references that are in the map.
- First posted on arXiv 27 Jan 2026.

## Builds on (most cited first, 19 of 19)

- [Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers](https://mlsanity.com/paper/2504.18412/) (2025)
- [Principles of Explanation in Human-AI Systems](https://mlsanity.com/paper/2102.04972/) (2021)
- [Robustness and Cybersecurity in the EU Artificial Intelligence Act](https://mlsanity.com/paper/2502.16184/) (2025)
- [Comparing Apples to Oranges: A Taxonomy for Navigating the Global Landscape of AI Regulation](https://mlsanity.com/paper/2505.13673/) (2025)
- [Red Teaming AI Policy: A Taxonomy of Avoision and the EU AI Act](https://mlsanity.com/paper/2506.01931/) (2025)
- [Sociotechnical Considerations for Accessible Visualization Design](https://mlsanity.com/paper/1909.05118/) (2019)
- [Smallset Timelines: A Visual Representation of Data Preprocessing Decisions](https://mlsanity.com/paper/2206.04875/) (2022)
- [From Information to Choice: A Critical Inquiry Into Visualization Tools for Decision Making](https://mlsanity.com/paper/2307.08326/) (2023)
- [A Toolkit for Compliance, a Toolkit for Justice: Drawing on Cross-sectoral Expertise to Develop a Pro-justice EU AI Act Toolkit](https://mlsanity.com/paper/2505.17165/) (2025)
- [A User-centered Design Study in Scientific Visualization Targeting Domain Experts](https://mlsanity.com/paper/1903.12349/) (2019)
- [WEIRD Audits? Research Trends, Linguistic and Geographical Disparities in the Algorithm Audits of Online Platforms -- A Systematic Literature Review](https://mlsanity.com/paper/2401.11194/) (2024)
- [Legacy Procurement Practices Shape How U.S. Cities Govern AI: Understanding Government Employees' Practices, Challenges, and Needs](https://mlsanity.com/paper/2411.04994/) (2024)
- [Data Ecofeminism](https://mlsanity.com/paper/2502.11086/) (2025)
- [Justified Evidence Collection for Argument-based AI Fairness Assurance](https://mlsanity.com/paper/2505.08064/) (2025)
- [Aggregating Concepts of Fairness and Accuracy in Prediction Algorithms](https://mlsanity.com/paper/2505.08829/) (2025)
- [A Framework for Creative-Visualization Opportunities Workshops](https://mlsanity.com/paper/1808.02502/) (2018)
- [Design by Immersion: A Transdisciplinary Approach to Problem-Driven Visualizations](https://mlsanity.com/paper/1908.00559/) (2019)
- [Achieving Socio-Economic Parity through the Lens of EU AI Act](https://mlsanity.com/paper/2503.23056/) (2025)
- [Recourse, Repair, Reparation, & Prevention: A Stakeholder Analysis of AI Supply Chains](https://mlsanity.com/paper/2507.02648/) (2025)

## Abstract (arXiv)

Innovative HealthTech teams develop Artificial Intelligence (AI) systems in contexts where ethical expectations and organizational priorities must be balanced under severe resource constraints. While Responsible AI practices are expected to guide the design and evaluation of such systems, they frequently remain abstract or poorly aligned with the operational realities of early-stage innovation. At the ecosystem level, this misalignment disproportionately affects disadvantaged projects and founders, therefore limiting the diversity of problem-areas under consideration, solutions, stakeholder perspectives, and population datasets represented in AI-enabled healthcare systems. Visualization provides a practical mechanism for supporting decision-making across the AI lifecycle. When developed via a rigorous and collaborative design process, structured on domain knowledge and designed around real-world constraints, visual interfaces can operate as effective sociotechnical governance artifacts enabling responsible decision-making. Grounded in innovation-oriented Human-Centered Computing methodologies, we synthesize insights from a series of design studies conducted via a longitudinal visualization research program, a case study centered on governance dashboard design in a translational setting, and a survey of a cohort of early-stage HealthTech startups. Based on these findings, we articulate design process implications for governance-oriented visualization systems: co-creation with stakeholders, alignment with organizational maturity and context, and support for heterogeneous roles and tasks among others. This work contributes actionable guidance for designing Responsible AI governance dashboards that support decision-making and accountability in early-stage health innovation, and suggests that ecosystem-level coordination can enable more scalable and diverse AI innovation in healthcare.