Equitable Health Technologies: Overcoming Biases and False Inferences in AI Diagnostics and Drug Discovery2026 National Inter-University Big Data Challenge Indicium Conference ProceedingOrganized by STEM Fellowship in partnership with RBC Foundation, Canadian Personalized Healthcare Innovation Network, Research Canada, Canadian Science Publishing, Underline.io

The National Inter-University Big Data Challenge and Indicium are unique, interdisciplinary experiential learning and research portfolio development programs that provide both undergraduate and graduate students with an exceptional opportunity to conduct full-scope independent or mentored scholarly research and to publish. Through the program, students engage with Open Data and apply advanced data science techniques to meaningful, socially relevant health research challenges. They develop and refine skills in uncovering hidden patterns and trends in both structured and unstructured datasets. They gain hands-on experience and demonstrate the use of a wide range of analytical tools and diverse machine learning frameworks, leading to scholarly publication. The National Inter-University Big Data Challenge is open to independent student researchers and groups regardless of their university affiliation to carry out real-world-level research meeting expectations of graduate studies and industry, while also benefiting from skill-building workshops and networking opportunities with the broader scientific community. Indicium is an annual research mentorship program tailored for students with limited research experience. Indicium connects participants with mentors from diverse STEM backgrounds—including faculty members, graduate students, and medical professionals. Throughout the program, students receive mentorship on research design, data analysis, and scientific communication. As AI transforms healthcare, ensuring reliability is critical. However, systems learning from existing data often reflect gaps or inconsistencies, leading to false inferences, diagnostic errors, and unequal performance across patient groups. Open Data is pivotal for transparency and accountability. Biases emerging from unrepresentative datasets or flawed assumptions can perpetuate health disparities. Leveraging public data on genomics, clinical trials, and social determinants allows researchers to audit and mitigate these risks effectively. The theme for 2026, “Equitable Health Technologies: Overcoming Biases and False Inferences in AI Diagnostics and Drug Discovery,” challenged participants to explore how to detect, reduce, and prevent bias in diagnostics and drug discovery; develop data-driven tools that increase fairness and inclusivity; and innovate bias-aware model evaluation techniques to build trustworthy AI systems. We are continually inspired by the intellectual curiosity, creativity, and analytical skills demonstrated by this next generation of researchers. Their work not only contributes to the scientific discourse around overcoming biases and false inferences in AI diagnostics and drug discovery but also exemplifies the spirit of innovation that these programs seek to foster. The following conference proceedings celebrate emerging investigators’ achievements and provide the necessary scholarly exposure of their work. On behalf of STEM Fellowship, we extend our heartfelt congratulations to all participants. We also thank volunteer peer-reviewers, our dedicated team of STEM Fellowship volunteers whose tireless support made these programs possible. We would also like to gratefully acknowledge the invaluable partnership and contributions of RBC Foundation, Canadian Science Publishing, CPHIN, and Underline.