Artificial Intelligence (AI) is no longer a future-gazing technology but something that humans interact with every day. It continues to bring new possibilities into different facets of life and work, with the pace of change promising even more innovation in the years to come.
About the author
Jack Watts, AI Specialist, NetApp.
Whether it is improved efficiency, the creation of new industries or cost reduction, the benefits to our economy seem endless. According to PwC, AI has the potential to contribute $ 15.7 trillion to the global economy by 2030 and boost GDP up by 26% in local economies in the same time frame.
In fact, even the UK Government acknowledges the role AI can play in society. Last year, the UK Government launched its National AI Strategy – a 10 year plan with the aim of positioning the country as a global leader in the governance of AI technologies. This also includes a new National AI Research and Innovation program to help make sure the UK discovers and develops the latest innovations in this area.
Risks of AI
However, like with any technology, there are risks. According to McKinsey, AI can bring a rise to a host of unwanted and sometimes serious consequences. Some of the most apparent risks of AI include privacy violations, discrimination, accidents, and manipulation of political systems. But there are even more concerning consequences worth considering, including the compromise of national security which can result in huge reputation damage, revenue loss, regulatory backlash, criminal investigation and diminished public trust.
Businesses looking to leverage AI must understand and respect these risks, but also be ready to take responsibility for AI. This means understanding its power and working with leaders across the business to ensure it co-exists and is not misused.
The tension between AI and humans is often framed as a question of either / or. However, the two will co-exist. It’s not a case of AI replacing or taking over humans, but the decisions humans make about how and where we use AI. If it’s underused, businesses will miss out on millions of ways to improve the way we live and work in big and small ways. If it’s overused, organizations risk depriving humans of certain job opportunities or prioritizing efficiency over personality. We must frame AI as empowering humans, creating opportunities, and opening up new job markets.
The imperative is therefore on humans to ensure AI is used responsibly. Responsible AI is a governance framework that documents how an organization can deploy AI in a manner that is ethical and legal. It marries the need for AI to be used for good and the opportunity it provides businesses, governments and broader society. Part of this conundrum is the role of human oversight built on ethics and data governance or freedoms.
While humans are ultimately responsible for deciding how their business will use AI at a strategic level and managing the digital infrastructure that enables AI, automation levels will continue to rise. Code is writing code and machines are making decisions about machines. Responsible AI is not necessarily about humans overseeing every single decision an algorithm makes, but ensuring that they are equipped to provide unbiased, ethical and legal or restrictive outcomes.
There are multiple ways to leverage internal and / or securing external resources, software and consultancy for organizations looking to Responsible AI as a means of delivering value for their customers, partners and employees. Dealing with strategic vendors for data management and compute resource – whether that’s in the cloud or on premise – and coupling that with strategic consultancy will result in higher quality decision making and faster time to market that is critical to an organization’s machine learning operations (MLOps) .
Data fabrics are key to this concept of MLOps – a set of practices that aims to deploy and maintain machine learning models from development into production. To take full advantage of the opportunities Responsible AI can bring organizations must ensure that software engineers and developers can easily access clean, unbiased data. Furthermore, MLOps teams must be plugged into the strategic aims of how the business wants to use AI as well as its ethical framework for doing so. Getting this combination right means the people who train the algorithms and deploy machine learning / AI have the tools to do so, but also a clear vision of the outcomes their organization is looking to drive.
The future of AI is Responsible AI
It is vital that businesses using AI remain accountable, secure and compliant, while regulation must be impartial and transparent, if application of the technology is to positively transform our lives in the future. As such, Responsible AI is the way businesses must navigate to balance risk, earn trust and overcome unconscious bias.
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