Organizations are increasingly depending upon artificial intelligence (AI) and Machine Learning (ML) to assist humans in decision making. It’s how top organizations improve Customer interactions and accelerate time-to-market for goods and services. But these organizations need to be able to trust their AI/ML models before they can be operationalized and used in crucial business processes. Trustworthy AI has become a requirement for the successful adoption of AI in the industry.
These days, if an AI model makes a biased, unfair decision involving the health, wealth or well-being of humans, an Organization can hit the news for the wrong reasons. Alongside the significant brand reputation risk, there’s also a growing set of data and AI regulations across the world and across industries — like the upcoming European Union AI Act — that companies must adhere to.
Before you can trust an AI model and its insights, you need to be able to trust the data that’s being used. The right data fabric solution will naturally support these pillars and help you build trustworthy AI models. Consider these three crucial steps in the lifecycle of building out your next AI or Machine Learning model or improving a current one.
First things first: you need access and insight into all relevant data.
Research shows that up to 68% of data is not analyzed in most organizations. But successful AI implementations require connection to high quality, accurate data that’s ready for self-service consumption by the right stakeholders. Without the ability to aggregate data from disparate internal and external sources (on-premises, public or private clouds), you’ll have an inferior AI model, simply because you don’t have all the information you need.
Second, you need to make sure that the data itself can be trusted. There are two factors in a trusted data set:
According to Gartner, 53% of AI and ML projects are stuck in pre-production phases. You can operationalize your AI by looking at all stages of the AI lifecycle. Automated, integrated data science tools help build, deploy, and monitor AI models. This approach helps ensure transparency and accountability at each stage of the model lifecycle. But to do so, you also need to ensure guardrails for fairness, robustness, fact collection and more, throughout each stage of the model life cycle.
Often data scientists aren’t thrilled with the prospect of generating all the documentation necessary to meet ethical and regulatory standards.
