AI bias refers to the systematic error or unfairness that can be introduced into an AI system due to certain features of the training data or the way the system is designed.This can lead to discriminatory outcomes that can disproportionately affect certain individuals or groups.Here are some real-life examples of AI bias that have led to calls for ethical AI: One of the most talked about cases of AI bias was in the AI hiring tool developed by Amazon.In 2018, Amazon had to scrap an AI recruiting tool that was biased against women because it had been trained on a dataset of resumes that were overwhelmingly from men.This led to calls for greater transparency and accountability in the design and implementation of AI-based hiring systems. There have been several instances where facial recognition systems have been shown to exhibit racial bias, where the systems are less accurate at identifying individuals with darker skin tones.In 2018, researchers found that Amazon's facial recognition software, Rekognition, was less accurate at identifying the gender of darker-skinned individuals, and had a higher error rate for identifying women than men.This has led to calls for stricter regulations on the use of facial recognition technology, especially in law enforcement. AI systems in healthcare are being developed to assist doctors in making diagnoses and treatment decisions.However, there have been concerns that these systems can be biased against certain groups., A specific example of AI bias in healthcare involves a study published in 2019 in the New England Journal of Medicine.The study found that an algorithm used by a hospital to identify patients who might benefit from extra care and follow-up was biased against black patients. The AI algorithm used a variety of factors, such as the number of chronic conditions and recent hospitalizations, to identify patients who were at high risk of being readmitted within 30 days of being discharged.However, the study found that the algorithm was much more likely to flag white patients as high risk, even when they were no more likely to be readmitted than black patients. The researchers found that the algorithm was biased because it was trained on data from the hospital's previous patient population, which had a lower proportion of black patients than the current population.As a result, the algorithm was less accurate at identifying high-risk black patients, which could lead to disparities in care and outcomes. These examples highlight the importance of ensuring that AI systems are trained on diverse and representative datasets and that they are rigorously tested for bias in AI and fairness.It also underscores the need for greater transparency and accountability in the development and implementation of AI in healthcare, to ensure that patients receive equitable and high-quality care.