FEATURE : AI GOVERNANCE monitoring to ensure that the data used to train and test AI models is of high quality and free from bias .
Additionally , it is important to continually evaluate the data quality throughout the AI system ’ s lifecycle and to incorporate feedback from users to improve the accuracy and reliability of the results produced by the system .
While AI and machine learning models have the potential to revolutionise industries and transform the way we approach decision-making , it is important to remain vigilant and ensure that these models , and the data that feeds them , are constantly monitored , and updated to deliver reliable results .
In AI , the accuracy of the results produced by models depends heavily on the quality of the data being used to train them . Data that is inaccurate , incomplete , or biased can lead to incorrect or biased results , which can have serious consequences in areas such as healthcare , finance , and law enforcement .
Even with the best training data , it is important to note that AI and machine learning models can yield inconsistent results if the real-world data they are interpreting varies significantly from the data used during their initial training .
This discrepancy can occur when the training data does not accurately reflect the complexities and
variations present in the real-world data , which can lead to biased and unreliable results . Additionally , external factors such as changes in the environment , user behaviour , and technology can also contribute to this disparity .
Data observability helps ensure that the data being used by AI models is of consistent quality and remains suitable for the intended purpose . It involves tracking , monitoring , and analysing data movements to identify significant shifts in the state of the data used to feed AI and ML models .
In some instances , these issues may be the result of a failure in a data pipeline , which can be corrected by the operations team . In other cases , data observability may indicate long terms shifts in data that require the AI model to be retrained , using new , more representative training data .
In addition to maintaining the accuracy of AI results , data observability provides transparency into the data used by AI models , which is increasingly important as organisations face growing scrutiny over the use of AI and its potential impacts on society .
Overall , data observability plays a crucial role in ensuring that AI produces accurate and reliable results over time , and helps organisations build trust in the use of AI in their operations . p
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