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.

Keeping Humans In The Loop

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.

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