Today, many companies are entrusting their top business-critical operations and decisions to artificial intelligence. Rather than traditional, rule-based programming, users now have the ability to provide machine data, define outcomes, and let it create its own algorithms and provide recommendations to the business.
For instance, an auto insurance company can feed a machine a library of photos of previous totaled cars with data on their make, model and payout. The system can then be “trained” to review future incidents, determine if a car is totaled, and give a recommended payout amount. This streamlines the review process, which is both a positive for the company and customer.
However, with the ability for AI to arrive at its own conclusions, governance over the machines is critical for the sake of business executives and customers alike. Was the machine accurate in its review of the accident photos? Was the customer paid the right amount? By taking the proper measures, organizations can gain clarity and ensure they are using these tools responsibly and to everyone’s benefit.
Here are three areas to keep in mind.
Traceability sheds light on machine reasoning and logic
In a recent Genpact study of C-suite and other senior executives, 63 percent of respondents said that they find it important to be able to trace an AI-enabled machine’s reasoning path. After all, traceability helps with articulating decisions to customers, such as in a loan approval.
Traceability is also critical for compliance and meeting regulatory requirements, especially with the implementation of the General Data Protection Regulation (GDPR) in Europe, which has affected practically every global company today.
One critical GDPR requirement is that any organization using automation in decision-making must disclose the logic involved in the processing to the data subject. Without traceability, companies can struggle to communicate the machine’s logic and face penalties from regulatory bodies.
The right controls and human intervention remain paramount
By design, AI enables enterprises to review large datasets and delivers intelligence to facilitate decisions at far greater scale and speed than humanly possible. However, organizations cannot leave these systems to run in autopilot. There needs to be command and control by humans.
For example, a social media platform can use natural language processing to review users’ posts for warning signs of gun violence or suicidal thoughts. The system can comb through billions of posts and connect the dots–which would be impossible for even the largest team of staff–and alert customer agents. Not every post that will be a legitimate concern so it is up to humans to verify what the machine picked up.
This case highlights why people are still critical in the AI-driven future, as only we possess domain knowledge–business, industry, and customer intelligence acquired through experience–to validate the machine’s reasoning.
Command and control is also necessary to ensure algorithms are not being fooled or malfunctioning. For example, machines trained to identify certain types of images, such as for determining if a car is totaled for insurance purposes, can be fooled by feeding completely different images that have inherently the same pixel patterns. Why? Because the machine is analyzing the photos based on patterns, and not looking at them in the same context that human beings do.
Beware of unintentional human biases within data
Since AI-enabled machines constantly absorb data and information, it is highly likely for biases or unwanted outcomes to emerge, such as a chatbot that picks up inappropriate or violent language from interactions over time. However, if there is bias in the data going in, then there will be bias in what the system puts out.
Beforehand, individual users with domain knowledge have to review the data that goes into these machines to prevent possible biases and then maintain governance to make sure that none emerges over time. With more visibility, understanding of their data and governance over AI, companies can proactively assess the machine’s business rules or acquired patterns before they are adopted and rolled out across the enterprise and to customers.
At its root, responsible use of AI is all about trust. Companies, customers, and regulatory agencies want to trust that these intelligent systems are processing information and feeding back recommendations in the right fashion. They want to be clear that the business outcomes created by these machines are in everyone’s best interest.
By applying the various techniques discussed above, organizations can strengthen this trust with better understanding of the AI’s reasoning path, communication of decisions to customers, regulatory compliance, and command and control to ensure that they have clarity and can always make the best decisions.
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