From the course: Data Governance for the Healthcare Industry

Ethical frameworks and bias mitigation in AI

From the course: Data Governance for the Healthcare Industry

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Ethical frameworks and bias mitigation in AI

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- [Instructor] Medical data can help us make healthcare better in amazing new ways, but using this data comes with important responsibilities. We need to balance two main concerns. First, how do we use patient information to create helpful new tools while still respecting patients' rights to control their information? Second, how do we make sure the AI is not amplifying existing biases? Healthcare information is very personal and shows people their most vulnerable moments. Good governance goes beyond just following the law. It should include ethical principles like respect for people, doing good and fairness. Respecting people means getting proper permission to use their data. Your organization needs to decide what kind of consent is needed for different uses of data and how patients can control their information. Traditional consent forms often don't work well for newer, more complex ways of using health data. Doing good means making sure using data benefits both individual patients and society. Your framework should include ways to evaluate proposed data uses based on their potential benefits. Fairness requires making sure these benefits are sharing an equal manner and don't make existing health inequalities worse. Now, let's put these ethical principles into practice by tackling one of the biggest challenges, AI bias in healthcare. This bias often sneaks in through the data itself. If your training data reflects past unfair treatments of certain groups, your AI system will learn and repeat the same unfair patterns. Think of it like this. If you teach someone about heart disease using data that mostly come from male patients, they may miss important signs when treating women. Your governance framework needs to catch these problems early by establishing who reviews training data and what standard it must meet for diversity and representation. But bias doesn't just come from data. It can also emerge from how algorithms are designed and the assumptions built into them. This is why your framework should include review processes that examine these key design decisions before systems go live. Think of testing your AI system the same way you would test a new medical treatment. You want to make sure it works equally well for all patients, not just some. Once your AI is ready, you need to test it across different groups to see if it performs fairly for everyone. Your organization should set clear standards beforehand about how much difference in performance is acceptable. Going back to our heart disease example, if your AI correctly diagnoses heart problems in 90% of men, but only 70% of women, that 20% performance gaps confirms exactly what we fear. The male heavy training data create a system that works better for men. Someone needs to be assigned the responsibility of conducting these fairness checks. You can't just hope problems will be noticed. When you do discover unfair results, you have several options to fix the issue. You can rebalance your training data by adding more examples from underrepresented groups, redesign parts of the algorithm to make better decisions, or change how the system is using clinical practice. But testing is just the beginning. Remember, even a well-designed system can behave differently once it's been used in hospitals and clinics. Your framework should address how these tools fit into real clinical workflows and ensure the feedback from healthcare workers leads to continuous improvements. Building ethical AI in healthcare isn't about checking boxes. It's about weaving ethical thinking into every decision, from the technology you choose to how you develop and deploy these systems. By addressing potential bias at every stage, healthcare organizations can ensure these powerful tools help reducing inequalities rather than make them worse.

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