AI bias occurs when a system picks up and maintains biases from the training set of data. This may affect everything from chances to outcomes in court.

In a recent study, DataRobot found that 42% of organizations surveyed have encountered bias in their AI models.

Understanding the Sources of Bias: – Training Data Bias – Algorithmic Bias – Human Bias

AI models learn from the data they are trained on. If this data reflects societal biases, the AI system may perpetuate them in its outputs

Because of the decisions made by programmers, the algorithms employed to create AI systems could be biased by nature

Decision-making, classification, and data collection activities performed by humans have the potential to inject unconscious biases into the AI system

How to reduce The Bias

– Data Diversity – Algorithmic Fairness – Explainable AI

It takes constant work to fight bias in AI. We can strive to create fairer, more equitable AI systems that benefit all parties by implementing plans and promoting open communication