{"id":32251,"date":"2021-05-17T16:55:28","date_gmt":"2021-05-17T15:55:28","guid":{"rendered":"https:\/\/digileaders.com\/?p=32251"},"modified":"2021-05-19T17:44:23","modified_gmt":"2021-05-19T16:44:23","slug":"scaling-and-accelerating-ai-the-assurance-challenge","status":"publish","type":"post","link":"https:\/\/digileaders.com\/scaling-and-accelerating-ai-the-assurance-challenge\/","title":{"rendered":"Scaling and accelerating AI – The assurance challenge"},"content":{"rendered":"

Artificial Intelligence (AI) is one of the fastest-growing, and popular data-driven technologies being used across the globe.\u00a0<\/span><\/p>\n

According to analyst house Gartner, just 10% of companies had used or were about to use AI technology in 2015. Fast forward to 2020 and that number had risen to almost 40%, with over 90% of multinationals questioned planning to invest in AI technologies for efficiency and productivity gains.<\/span><\/p>\n

Today, all around the globe, organisations of all sizes from governments and large organisations to small online businesses are adopting AI with increasing pace and scale. This surge of interest and uptake is being driven by the exponential growth of available data, which, when set alongside the huge increases in computational power provided by cloud computing, and the commoditisation of AI software tools and frameworks makes AI and Machine Learning (ML) technologies ever-more attractive.<\/span><\/p>\n

Now, the benefits of AI can be seen almost daily. Organisations are taking advantage of AI technologies to improve efficiency, productivity and decision making, Consumers are showing increasing willingness to share their data and adopt AI to improve their experience of services, from streaming music to choosing financial products and more. Alongside this, the societal benefits of such technologies are becoming ever more evident seen by advancement in the speed and accuracy of disease and illness diagnosis.<\/span><\/p>\n

Such views on how AI is used must be balanced with some caution over the potential for AI and ML technologies to be wrongly configured, mishandled, or abused, and the unpredictability of the technology if its evolution proceeds without necessary safety and bias checks and balances.\u00a0<\/span><\/p>\n

So, whilst benefits and advancements are clear, the introduction of AI and ML-based technologies should not be rushed, particularly for organisations operating in complex or highly regulated environments. Indeed, for regulators that serve and protect those industries a dual challenge is presented: How do regulators best regulate AI across industry segments, and how can they themselves take advantage of AI to regulate those industries and assure safe and ethical deployment of autonomous systems.<\/span><\/p>\n

One of the key considerations for the assurance of AI systems relates to data. This is where adoption of AI introduces unique challenges:\u00a0Training data (data that is used for machine learning development) can be flawed, introducing error, duplication or even bias into a system.\u00a0<\/span><\/p>\n

Data degradation can result in \u2018Model Drift\u2019, which fundamentally changes system performance and results, with the AI degrading over time because the algorithms and data do not adequately reflect changes in the real world.\u00a0The result is that an organisation may make bad decisions and the potential for the AI to be unfair or discriminatory increases.<\/span><\/p>\n

AI assurance and making sure AI systems are safe requires a hands-on approach – managing processes and people to get the best results.\u00a0So, what are some best practices to consider?\u00a0What can be done to put together a good framework for AI governance?\u00a0If you already have a data policy in place, then you have the right start.\u00a0This relationship between data and AI is so close: What data do you have?\u00a0Where is it coming from?\u00a0How is the data being altered?\u00a0By whom? Clear-cut requirements and principles must be adopted.\u00a0<\/span><\/p>\n

Our work at Informed puts us at the heart of the design and delivery of digital services for complex and highly regulated environments. Whether in healthcare, justice and emergency services or energy and the environment, the application of AI and ML technologies that bring life and meaning to complex data must be carefully considered in light of policy direction.\u00a0<\/span><\/p>\n

The development of smarter regulation can accompany the growth and acceptance of emerging technology, protecting consumers, citizens, or patients, whilst helping drive innovation that sits naturally and effectively with policy. In the last ten years however, policymakers have given industry players something of a free rein to deploy emerging technologies. A key to smart policy development for complex markets that wish to take advantage of AI and ML relies on regulators having greater access to and understanding of the potential and challenges brought by these technologies. This in turn will help the establishment of effective future frameworks that result in innovation growth, whilst the public remains protected.<\/span><\/p>\n

So where can organisations or regulators who are struggling with AI assurance challenges go to best understand and manage complexities involved in regulating AI and ML technologies for complex markets? Today, one of the world\u2019s leading safety assurance programmes is being run by an increasingly influential and important group led by Professor John McDermid, Director of the Assuring Autonomy International Programme at the University of York.\u00a0<\/span><\/p>\n

In 2016 a Lloyd\u2019s Register Foundation Foresight Review of Robotics and Autonomous Systems, (RAS) which includes AI and ML, identified that the biggest obstacle to gaining the benefits of RAS was their assurance and regulation.\u00a0The <\/span>Assuring Autonomy International Programme<\/span><\/a> was created and is now actively addressing these global challenges.<\/span><\/p>\n

It is leading state-of-the-art research to provide industry, regulators and others with guidance in assuring and regulating RAS. It breaks safety assurance into three elements: Communication; confidence; and trust.<\/span><\/p>\n