Adding data context for more accurate, flexible artificial intelligence

We are seeing impressive gains in artificial intelligence (AI) systems across diverse industries today: in faster drug discovery with around double the screening success rate; in finance to prevent fraud by analyzing billions of transactions for criminal behavior across multiple networks; and in retail to provide improved, personalized experiences with smarter chatbots, real-time recommendations, and search. Yet, at times, we find ourselves disappointed with suboptimal AI-produced results. Take, as recent examples, the worst-ever performance by AI-hedge funds and of course amplified bias found in recruiting tools. AI is making progress and being applied in just about every aspect of our lives – but we have a ways to go to fulfill the promises of AI with robust and trustworthy systems. Although we are just beginning to understand the full range of AI impact, there are practical steps we can take now to reap near-term benefits, ameliorate dangers, and create a foundation for what will undoubtedly be a frenetic future.