The risk of bias in artificial intelligence (AI) has been the source of much concern and debate. Numerous high-profile examples demonstrate the reality that AI is not a default “neutral” technology and can come to reflect or exacerbate bias encoded in human data. These risks undermine the underlying trust in AI and affect your organization’s ability to deliver successful AI projects, unhindered by potential ethical and reputational consequences.

Every day, telecom operators collect multiple terabytes of data from infrastructure, applications, customers, and devices—data that is becoming one of the most important assets that operators have. But not many organizations are able to effectively extract value from that data.

Advanced enterprise AI capabilities have the potential to transform both care management and back office innovations that can significantly improve both member outcomes and the cost of care. These innovations can enhance the detection of Fraud, Waste, and Abuse (FWA), targeting care management resources to the right members, predicting high-cost episodes of care to better utilize in-network resources, and reducing member churn in the Medicare Advantage space.

AI is an opportunity to do better, to deprogram our society of bias, and directly encode the ethics and values we’d like to see reflected in AI-driven processes. The path there requires a nuanced understanding of algorithmic bias, how it is produced, and how it can be mitigated, and also a more comprehensive framework for accountability and governance of AI systems in general, encompassing all risks including bias.

Artificial Intelligence (AI) offers a way forward for retail marketers to gain deeper insights and fine-tune their promotions based on a broad range of data inputs and accurate predictions. With AI-driven solutions around key metrics such as net promoter score and customer sentiment, marketers gain an automated means to adjust their campaigns, drive customer loyalty, and ensure that they are investing in the right product-focused promotional strategies.