Fair play? Navigating bias hurdles in AI-driven big data analytics

Around the world, governments, technology companies and academia are seeking consensus around artificial intelligence (AI) in everything, and trying to understand how best we can gain the benefits of AI, while also making sure the technology has adequate governance and regulation, and is used fairly, without harm.
common critical concern in AI systems is AI bias – when an AI algorithm produces unfair, prejudiced or discriminatory outcomes or predictions because of erroneous assumptions in the underlying machine learning. But before we can overcome the issue, we must first understand where the biases come fromBias in data and AI
Bias in data and AI algorithms mostly arise when the underlying data used to train the AI model is skewed in some way. One form of AI bias is when data used to train AI models may be inherently biased due to how it was collected. If the data selected for training is inaccurate, incomplete or in some way incompatible so that it doesn’t represent the population it means to serve, then the AI model can develop a bias based on this. A good example is when facial recognition systems are trained on a data set mainly from one race or gender, then the system will be better at recognising faces from the bias group, and may perform poorly for individuals from other racial or gender groups.