<p>STEM Fellowship is a Canadian registered charity that uses mentorship and experiential learning to equip the next generation of change-makers with indispensable skills in data science and scholarly writing. We began our activity in February 2015 and soon expanded to high schools and universities across Canada. Currently, we have 20 university branches in over 8 provinces and we’re still growing!</p>

<p>We present a new philosophy for student-driven experiential learning in STEM and provide our fellows with tools that will help them in the current STEM research and education ecosystem. We are a virtual meeting place for advanced STEM learning. A place where students can collaborate to develop their ideas and discuss them with world-class experts. An opportunity for peer-review and scholarly publications of high school and university research.</p>


STEM Fellowship

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.

We task undergraduate and graduate students to harness data science to:

* Detect, reduce, and prevent bias in diagnostics and drug discovery;

* Develop data-driven tools that increase fairness and inclusivity;

* Innovate bias-aware model evaluation techniques to build trustworthy AI systems.

Equitable Health Technologies: Overcoming Biases and False Inferences in AI Diagnostics and Drug Discovery

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.

Equitable Health Technologies: Overcoming Biases and False Inferences in AI Diagnostics and Drug Discovery

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.

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