We are truly experiencing the age of AI, where a host of new technologies are sweeping across almost every industry. Intelligent sensors are being used for applications as broad as monitoring patient symptoms in healthcare settings to predicting machinery lifespan in manufacturing facilities. AI has also begun to change everyday life, with the introduction of self-driving vehicles, voice activated assistants and more.<\/p>\n\n\n\n

The pandemic was a catalyst for demand for analytics and AI technology \u2014 both for healthcare and business. A 2021 study by PwC found that 52 per cent of companies surveyed accelerated AI adoption plans because of COVID and 86 per cent said AI was becoming a mainstream technology at their company. By using AI, businesses could better react to shifts in working arrangements, purchasing behaviour, skills shortages and more. Similarly in healthcare, an AI imaging database helped improve the diagnosis of patients presenting with COVID symptoms.<\/p>\n\n\n\n

The AI debate<\/h2>\n\n\n\n

When a technology is initially introduced to the market there is often little information about it available, particularly for public viewership. As a result, people may go looking for the information themselves which, if not credible, can lead to misconceptions about the technology, which can spread online.<\/p>\n\n\n\n

This is particularly true for AI, of which people have differing opinions. Professor Stephen Hawking, for example, stated that AI could \u201cspell the end of the human race\u201d, while Google CEO, Sundar Pichai said that AI will cause a \u201cmore profound change than fire or electricity.\u201d Both are renowned and respected figures, so which should the public trust?<\/p>\n\n\n\n

How AI will impact the workforce is another common debate. According to Pew Research, 52 per cent of experts surveyed expected AI and robots to create more jobs, while the others said it would displace blue- and white-collar workers. Many people also question the ethical implications of AI, particularly when it is used for decision-making applications, such as accident prevention in driverless cars, court proceedings and smart assistants.<\/p>\n\n\n\n

Trustworthy sources<\/h2>\n\n\n\n

One of the challenges we face is educating the general public about the true implications of AI, which can only be done using credible sources. Many of the questions people have about AI are already being answered by researchers in the field, but academic research is not straightforward for the general public to access. At best, we can advise that, when digesting any information or opinions about AI, readers check the provenance of the information and look for trustworthy sources, such as university researchers, technology journals, industry experts or educational books.<\/p>\n\n\n\n

With so much debate in the industry, this only goes so far. The public will need to cross reference any claims with a variety of sources to ensure they only form opinions based on credible information, discounting information from unreliable sources that make claims without researched evidence. It may even require them to refer to academic literature to see where claims have initiated from.<\/p>\n\n\n\n

A challenge here is that academic publications are written in language that only specialist audiences can understand. This can lead to the public missing valuable information that could help them make more informed decisions and back up or disprove other sources of content. The challenges of reading academic articles is an important reason that people turn to social media as a more digestible \u2500 but less credible \u2500 source of news.<\/p>\n\n\n\n