| | MARCH 20208CIOReviewIN MY OPINIONOver 20 years ago, a supercomputer beat world chess champion Garry Kasparov at chess. That moment helped plant the seeds for the era of big data in which we live today, and it marked a critical cultural turning point that suggested a machine could outsmart a human after all. While Kasparov initially expressed cynicism regarding the computer's methods and its intelligence, he has more recently changed his tune, crediting the power of artificial intelligence (AI) and advocating for a symbiotic relationship between humans and machines. "At the end of the day," he argued, "it is for us to even explain when something is successful. It is still for us to define success and machines to perform their duty" ­ underscoring the significance of our human role in defining and creating the knowledge base, the logic, and the authority that we empower our AI systems to wield. What does this mean for those of us who create AI systems, in the era of big data, and in an era where consumers are (rightfully) expecting and demanding that we leverage that data responsibly and accurately? To ensure both consumer trust and high-quality products and services, AI systems need to maintain a high degree of data integrity, enable end-users effectively, and employ responsible use safeguards.RESPONSIBLE AI:THE HUMAN- MACHINE SYMBIOSISBy Sal Cucchiara, CIO & Head of Wealth Management Technology, Morgan Stanley [NYSE: MS]Data IntegrityData integrity sits at the core of AI systems and is the foundation of client trust. Consumers hold higher expectations of accuracy for machines than for humans, however all machine intelligence is derived from human inputs. Our AI systems have the capacity to deliver this accuracy if we have an exacting grip over how our data is identified, collected, maintained, and integrated into our systems. The first step to achieving this is through effective data curation, which involves identifying the most authoritative, trustworthy source for each piece of data, and then structuring the data in such a way that makes it easily accessible and eliminates ambiguity. We then need to implement robust knowledge management practices to continuously ensure that information remains up-to-date. We must account, for example, for any and all events that impact our data, from world events, to regulatory changes, to individual client events. Finally, AI systems must implement built-in feedback loops, to give technologists visibility into how the system interacts with end-users, and to allow end-users to communicate the accuracy of the answers that AI systems provide. Beyond the answers themselves, end-users also tend to demand the reasoning behind these answers. For example, if a client's request to execute a trade is denied, he or she will generally want to know why. To engender optimal trust, AI systems must thus also enable model explainability, to provide evidence for its responses and actions. If a system detects a transaction to be fraudulent, it should be able to provide evidence for its detection. If a ChatBot provides an answer to a question about stock prices, it should also be able to link to its source. When an AI system provides
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