AI bias is a complex problem that can arise in healthcare applications. Some of the challenges include:.
19 Sep 2023, 06:42
đ AI bias is a complex problem that can arise in healthcare applications. Some of the challenges include:
âď¸ Artificial models trained using algorithms that can be biased if said #algorithm is designed not acounting for the potential sources of bias or if it's trained on unreliable #data.
âď¸ AI models trained using data that can also be biased (intentionally or unintentionally), creating predictions or decisions in the same manner and be less accurate.
âĄď¸ Even if the data and algorithms are not biased, #human bias can still interject in the use and #development of AI models. The people who collect the data, design the algorithms, and interpret the results of AI models may have their own biases.
đ There are various potential solutions to address these challenges:
âď¸ One solution is different bias-mitigation techniques like data cleaning, algorithm design and human oversight.
âď¸ Another is people's awareness and #education about AI bias and fairness, helping to ensure that everyone knows about the #challenges and how to address them.
âď¸ Lastly, AI models trained on data that is as diverse as possible regarding race, gender, ethnicity, age, and other factors, thus helping to reduce the risk.
âĄď¸ These challenges are complex but certainly not insuperable. The objective is to have safe, accurate, non-biased AI models.
âĄď¸ By adressing these and searching for solutions, we can help to ensure that AI is used to improve healthcare for everyone.
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