You probably haven't noticed, but there's a good chance that some of what you've read on the internet was written by robots. And it's likely to be a lot more soon. Artificial-intelligence software programs that generate text are becoming sophisticated enough that their output often can't be distinguished from what people write. And a growing number of companies are seeking to make use of this technology to automate the creation of information we might rely on, according to those who build the tools, academics who study the software, and investors backing companies that are expanding the types of content that can be auto-generated. "It is probably impossible that the majority of people who use the web on a day-to-day basis haven't at some point run into AI-generated content," says Adam Chronister, who runs a small search-engine optimization firm in Spokane, Wash.

National Australia Bank will use artificial intelligence technology to make credit decisions on small business loans, illustrating how AI applications are shifting from the periphery to the heart of banking operations. NAB hopes the system that is being built by Rich Data, a Sydney-based AI company, will increase the supply of credit by widening the pool of small businesses that can qualify for a loan. It will also help NAB push towards real-time loan assessments, tapping data from cloud accounting platforms, transaction systems and other macroeconomic sources to profile small to medium enterprises and predict their likelihood of repayment. Howard Silby, chief innovation officer at NAB: "This will be put live with real customers early in the 2021 calendar year." NAB is the first major Australian customer for Rich Data, which supplies its Delta platform to banks in Asia and North America. NAB uses AI to triage of customer complaints and to detect money-laundering and fraud.

As AI becomes increasingly interwoven into our lives--fueling our experiences at home, work, and even on the road--it is imperative that we question how and why our machines do what they do. Although most AI operates in a "black box" in which its decision-making process is hidden--think, why did my GPS re-route me?--transparency in AI is essential to building trust in our systems. But that transparency is not all we want: We also need to ensure that AI decision-making is unbiased, in order to fully trust its abilities. The issue of bias in the tech industry is no secret--especially when it comes to the underrepresentation of and pay disparity for women. But bias can also seep into the very data that machine learning uses to train on, influencing the predictions it makes.