Are Business Leaders Taking an Ethical Approach to A.I.?
Powerful, functional tools – like machine and deep learning – are becoming commonplace within businesses, governments, and more. But every positive story seems to be met with equal outpourings of concern about job loss, wealth stratification, and Skynet-esque machine domination.
There are positive signs that businesses recognize their role in assuaging these fears, according to a recent study conducted by SAS, Accenture, Intel, and Forbes Insights. “AI Momentum, Maturity, and Models for Success,” based on a global executive survey of 305 business leaders, reports that leadership is both aware of and actively taking steps to ensure that AI is used ethically and responsibly at their respective companies. 72 percent of businesses around the world have adopted AI; of these companies, 70 percent offer training for staff and 63 percent have functional ethics committees to oversee AI-related issues. 92 percent of “AI leaders” in these organizations receive some type of ethics training – chief information, technology, and analytics officers included.
Some organizations have created AI-oversight positions, including study-contributor Accenture Applied Intelligence. Rumman Chowdhury, the responsible AI lead there, says that organizations realize the seriousness of potential AI issues and “have begun addressing concerns and aberrations…such as biased and unfair treatment of people.” Doing so means enjoying AI’s benefits while embracing a “do no harm” ethos, which Chowdhury believes includes providing “prescriptive, specific and technical guidelines to develop AI systems that are secure, transparent, explainable, and accountable” – all in an effort “to avoid unintended consequences and compliance challenges that can be harmful to individuals, businesses, and society.”
There is additional positive news: 74 percent of AI leaders “reported careful oversight with at least a weekly review or evaluation of outcomes”, while 43 percent have implemented processes for “augmenting or overriding” results found to be questionable through the review process. But there remains room for improvement, and advances in AI (as well as increased adoption) mean businesses are responsible for keeping up. Yinyin Liu, who heads Intel AI’s data science division, believes understanding AI “enables effective human oversight”. “Algorithm transparency and accountability, as well as having AI systems signal that they are not human, will go a long way toward developing the trust needed for widespread adoption,” says Liu.
Trust – between business and customers, and between employees and AI – may be the single most important driver in encouraging companies to practice ethical AI use. 60 percent of businesses that have or plan to deploy AI “are concerned about the impact of AI-driven decisions on customer engagement…that their actions will not show enough empathy or customers will trust them less.”
Functional AI is still a relatively new phenomenon, with all the challenges inherent to that newness. Forbes Insights research director Ross Gagnon says “…the question executives should be asking themselves is not whether to deploy AI, but how quickly?” Doing so ethically will mean maintaining the trust of their customers and employees – and ensuring a bright and successful future.
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Contributor
Sergey Savastiouk, Ph.D. has a degree in Applied Mathematics from Moscow University and has extensive experience as an entrepreneur, investor, manager, and mathematician. His professional expertise is in applied mathematics, mathematical modeling, system and pattern analysis, and software and hardware system integration. He has served as the CEO of several hi-tech start-up companies and nonprofit organizations, which has given him proven capabilities in business strategy for high-tech start-up companies, market assessment, company formation, team building, product development, marketing, and sales. He has published numerous articles in journals and magazines on related fields. As a retail investor, he spent 15 years developing his proprietary trading and quantitative algorithms (now Tickeron’s A.I.), which brought him significant returns in trading the stock market. His current work and goal in founding Tickeron is to bring professional, sophisticated stock market analysis capabilities to retail investors via an easy-to-use interface.
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