What is changing is that uses are becoming more obvious: from search engines (where the use of algorithms is largely invisible to the uninitiated) to chatbots (where they are very, very public), for example.
Being able to analyze behaviour in real time and act directly to prevent fraud, or to improve customer service, makes a big difference to value.
The use of real-time analytics, too, makes a difference. Being able to analyze behaviour in real time and act directly to prevent fraud, or to improve customer service, makes a big difference to value. This takes AI from the realms of the retrospective into the prospective: from analyzing past actions to predicting (and managing) future performance. The speed of the processing—far beyond human capability—is one of the real powers of AI, and enables organisations to move faster from data to value. Automation has been key to achieving this.
Governance, ethics and partnership
This sounds like machine learning is outrunning human intelligence, but that is not really the case. There are things that machines and algorithms can do much better, such as rapid processing of huge amounts of data, and developing decisions about patterns and meaning. But there are also things that humans can do better, such as ethics, emotions and understanding.
Governance and ethics are vital to the ongoing development and use of machine learning techniques. Visibility is, and will remain, crucial. And by visibility, I mean transparency—being able to explain how decisions are made, and being confident that they are fair. Rapid deployment of models means that they are more accurate, because all data has a sell-by date. But models also need to be retired, redeveloped and retrained. This kind of governance needs people.
Governance and ethics are vital to the ongoing development and use of machine learning techniques.
This partnership between people and algorithms will only become more important as organisations scale up their use of machine learning models, and models become more a part of ‘how we do business’.
This article was republished with permission from SAS.