Bridging accountability and innovation: key findings from the Health AI Systems Thinking for Community (HASTC) workshop
Bibliographic record
Abstract
Artificial intelligence (AI) is transforming healthcare, but its rapid deployment raises concerns about equity, transparency, and accountability. Without proper oversight, these systems can reinforce biases, disproportionately affecting marginalized communities. Current regulations and policies fail to fully address these risks, making proactive safeguards essential to prevent systemic health inequities. To address these challenges, we organized the Health AI Systems Thinking for Community (HASTC) workshop at the University of Toronto (October 2024). This cross-disciplinary workshop convened 66 participants from post-secondary, healthcare, and nonprofit sectors to collaboratively discuss the management of AI harm in healthcare. Participants, guided by mentors, analyzed real-world cases of algorithmic bias, privacy risks, and unintended consequences of AI-assisted decision-making. Semi-structured discussions within groups focused on accountability, transparency, and fairness. Through structured discussions, participants identified worst-case scenarios and proposed safeguards at different levels, with implications toward government regulations, institutional policies, and healthcare practices. Key findings emphasized the need for adaptive, context-specific regulations and discussions to ensure responsible AI use in healthcare. There is a need for ongoing dialogue and reflection. By integrating community-driven advocacy and interactive learning, HASTC highlights the importance of creating AI systems that are fair, accountable, and transparent, to benefit all patients, not just a privileged few.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".