Inside the black box: Understanding AI decision-making ZDNet

Neural networks, machine-learning systems, predictive analytics, speech recognition, natural-language understanding and other components of what's broadly defined as'artificial intelligence' (AI) are currently undergoing a boom: research is progressing apace, media attention is at an all-time high, and organisations are increasingly implementing AI solutions in pursuit of automation-driven efficiencies. The first thing to establish is what we're not talking about, which is human-level AI -- often termed'strong AI' or'artificial general intelligence' (AGI). A survey conducted among four groups of experts in 2012/13 by AI researchers Vincent C. Müller and Nick Bostrom reported a 50 percent chance that AGI would be developed between 2040 and 2050, rising to 90 percent by 2075; so-called'superintelligence' -- which Bostrom defines as "any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest" -- was expected some 30 years after the achievement of AGI (Fundamental Issues of Artificial Intelligence, Chapter 33). This stuff will happen, and it certainly needs careful consideration, but it's not happening right now. What is happening right now, at an increasing pace, is the application of AI algorithms to all manner of processes that can significantly affect peoples' lives -- at work, at home and as they travel around. Although hype around these technologies is approaching the'peak of expectation' (sensu Gartner), there's a potential fly in the AI ointment: the workings of many of these algorithms are not open to scrutiny -- either because they are the proprietary assets of an organisation or because they are opaque by their very nature.