The road to AI – speedy and full of blind spots
It is fair to say that artificial intelligence (AI) is everywhere. Newspapers and magazines are littered with articles about the latest advancements and new projects being launched because of AI and machine learning (ML) technology. In the last few years it seems like all of the necessary ingredients – powerful, affordable computer technologies, advanced algorithms, and the huge amounts of data required – have come together. We’re even at the point of mutual acceptance for this technology from consumers, businesses, and regulators alike. It has been speculated that over the next few decades, AI could be the biggest commercial driver for companies and even entire nations.
However, with any new technology, the adoption must be thoughtful both in how it is designed and how it is used. Organisations also need to make sure that they have the people to manage it, which can often be an afterthought in the rush to achieve the promised benefits. Before jumping on the bandwagon, it is worth taking a step back, looking more closely at where AI blind spots might develop, and what can be done to counteract them. Only then will organisations be able to truly take advantage of the benefits that new technology, especially AI, will be able to bring to them in the long run and how it can change the future of their business.
As the pace of AI and ML development intensifies alongside heightened awareness of cybercrime, organisations must ensure they take into account any potential liabilities.
Despite this, it has been proven that security, privacy, and ethics are low-priority issues for developers when modelling their machine learning solutions.
According to O’Reilly’s recent AI Adoption in the Enterprise survey, security is the most concerning blind spot within organisations. In fact, nearly 73 per cent of senior business leaders admit that they don’t check for security vulnerabilities during model building. Additionally, more than half of organisations also don’t consider fairness, bias, or ethical issues during machine learning development. Privacy is similarly neglected, with only 35 per cent keeping this top of mind during model building and deployment.
Despite the lack of attention to security and privacy concerns with machine learning development, the majority of resources are focused on ensuring AI projects are accurate and successful. For example, 55 per cent of developers mitigate against unexpected outcomes or predictions, but a large number who don’t still remain.
