Council Post: AI Is For Human Empowerment: So Why Are We Cutting Humans Out?

Almost every company understands the value that artificial intelligence (AI) or machine learning (ML) can bring to their business, but for many, the potential risks of adding AI do not outweigh the benefits. Report after report consistently ranks AI as critically important to C-suite executives. To remain competitive means streamlining processes, increasing efficiency and improving outcomes, all of which can be achieved through AI and ML decisioning.

Despite the value that AI and ML bring, a lack of trust or fear that the technology will open businesses to more risk has slowed the implementation of AI/ML decisioning. This isn’t wholly unfounded—the risk of biased decisions in highly regulated industries and applications, like insurance eligibility, mortgage lending or talent acquisition, has been the subject of several new laws focused on the “right to explainability.” Earlier this year, congress proposed the Algorithmic Accountability Act. Overseas, the European Union is pushing for stricter AI regulations abroad, as well. These laws, and the “right to explainability” movement in general, are a reaction to mistrust of AI/ML decisions.

In fact, ethical worries around AI and ML impede the use of AI/ML decisioning. Research from Forrester commissioned by InRule discovered that AI/ML leaders are fearful that bias could negatively impact their bottom line.

To solve this problem, businesses must rethink their goals for AI/ML decisioning. For too long, many outside of the AI/ML field have seen technology as the replacement for human intelligence instead of the amplification of it. In removing humans from the decision-making loop, we are increasing the chance of bias and inaccurate and potentially costly decisions.

Human-in-the-loop AI is designed to thoughtfully include humans in the automated decision-making process. This is not a new concept. For years, human-in-the-loop AI has been used to manage and train models for efficiency through supervised learning. But that traditional view of human-in-the-loop does not go far enough. Those who use AI must expand human-in-the-loop to be part of the overall decisioning lifecycle, in addition to model training and review. And because machines can’t be accountable for the outcomes of automated decision making, keeping humans in the loop helps mitigate risk by adding a layer of accountability and scrutiny to decisions and outcomes.

While artificial intelligence is great for low-risk decisions—like what songs to put on a playlist based on your previous downloads—it doesn’t have the nuanced, versatile learning and experience that human intelligence does. Human intelligence isn’t based on a predetermined set of data, which provides us with the ability to review a more complex, high-risk decision—like verifying official documents, processing a loan or approving someone for an insurance policy.

Leaders understand the value human intelligence brings to the AI decision-making process. The Forrester survey found that almost 70% of AI/ML leaders believe that including humans in AI/ML decisioning curtails risks—and that better decisions and model transparency comes from engaging a wider group of stakeholders within an organization. However, to keep humans in the loop, AI systems need native explainability functionality.

How To Better Bring Humans Into AI Decisioning

Humans can be involved throughout the AI/ML decisioning process, including in the following areas.

When figuring out where and how to incorporate humans into the loop, it’s important to remember that the purpose of any AI system is human empowerment, whether it be a customer service bot on a website or a more sophisticated decisioning system working to approve homebuyers for a mortgage. The goal isn’t to make those systems as human as possible; it is to optimize business processes while meeting the customer’s needs for a personalized and expedient experience. This requires AI/ML systems to be accessible and understandable to anyone involved in the lending process.

Human-in-the-loop workflows are central to empowering a line of business to handle exceptions, optimize the flow of decisioning processes and ensure a positive customer experience. By creating accessible solutions that require fewer resources while reducing the risk of human error and increasing the accuracy of predictions, these workflows make it easy to leverage the power of computing without the complexity of programming.

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