Marjia Siddik
Computer Science @ DCU
Dublin, Ireland
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Marjia Siddik is a third-year computer science student at Dublin City University, passionate about leveraging artificial intelligence to foster inclusivity and ensure ethical, bias-mitigated applications. She has recently conducted funded research focused on improving the documentation of training data to mitigate bias and enhance the ethical implementation of AI systems. Marjia is dedicated to advancing AI practices that promote fairness and equity in technology.
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Addressing Algorithmic Bias: Fostering Fair and Inclusive Healthcare
The integration of AI algorithms into healthcare has revolutionized patient care, with the potential to improve diagnosis, treatment, and overall health outcomes. However, this transformative technology also introduces the risk of algorithmic bias, which can have detrimental consequences for patients and perpetuate existing health disparities.
What will be discussed:
- The dangers of algorithmic bias in healthcare, i.e. how bias can infiltrate AI algorithms and lead to unfair treatment of marginalized groups.
- How bias can arise from data collection to algorithm implementation, and how it can distort healthcare decisions and lead to disparities in care for diverse patients.
- Illustrate real-world implications of algorithmic bias in healthcare.
What the audience will learn:
- The significance of algorithmic bias in healthcare and its potential to harm patients.
- The dangers of biased AI algorithms, including misdiagnosis, delayed treatment, and poorer health outcomes for marginalized groups.
- The various ways in which bias can arise in healthcare algorithms, from data imbalances to flawed evaluation metrics.
Key Takeaways:
- Develop a comprehensive understanding of algorithmic bias in healthcare.
- Identify the potential sources of bias in AI algorithms.
- Become an advocate for responsible AI development and contribute to a healthier, more equitable future for all.
Addressing Algorithmic Bias: Fostering Fair and Inclusive Healthcare
The integration of AI into healthcare holds both promise and peril. While AI algorithms can improve diagnosis and treatment, they also risk perpetuating existing health disparities if biases aren't addressed.
This talk will explore:
The dangers of algorithmic bias, include misdiagnosis, delayed treatment, and poorer health outcomes for marginalized groups.
How bias can creep in at every stage of AI development, from flawed data to the metrics used for evaluation.
Real-world examples demonstrate the urgent need for change.
A potential solution: Althical, a platform that puts physicians at the center of AI development to create bias-reducing and explainable solutions.
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