Machine learning requires careful stewardship says Royal Society

The many potential social and economic benefits from advances in AI-based technologies depend entirely on the environment in which these technologies evolve, says the Royal Society. According to a new report from the UK’s science academy, urgent consideration needs to be given to the “careful stewardship” needed over the next ten years to ensure that the dividends from machine learning – the form of artificial intelligence that allows machines to learn from data – benefit all in UK society.

Machine Learning: the power and promise of computers that learn by example, published today (25 April 2017), comes at a critical time in the rapid development and use of this technology, and the growing debate about how it will reshape the UK economy and people’s lives. Crucially the report calls for research funding bodies to support a new wave of machine learning research that goes beyond technical challenges, and into areas aimed at addressing public confidence in machine learning – vital to the UK maintaining its internationally competitive edge at the forefront of this area.

The report also offers the first evidence about the UK public’s views on machine learning, including the application areas about which they are particularly positive, and the need for the real-world data feeding the growth of this technology to be dealt with fairly and securely.

Professor Peter Donnelly FRS, chair of the report’s working group and Director of the Wellcome Trust Centre for Human Genetics and Professor of Statistical Science at the University of Oxford, says: 

“Machine learning is already used in many apps and services that we encounter every day. It is used to tag people in our photos, by our phones to interpret voice commands, by internet retailers to make recommendations, and by banks to spot unusual activity on a credit or debit card. However, these current applications only scratch the surface of understanding just how powerful a technology this could be.

“Machine learning will have an increasing impact on our lives and lifestyles over the next five to ten years. There is much work to be done so that we take advantage of machine learning’s potential and ensure that the benefits are shared, especially as this could be a key area of opportunity for the UK in the coming years.”

An action plan so no one is left behind and the benefits are shared

The report calls for action in a number of key areas over the next five to ten years to create an environment of “careful stewardship” that can help ensure that the benefits of this technology are felt broadly. Understanding who will be most affected, how the benefits are likely to be distributed, and where the opportunities for growth lie, will be key to designing the most effective interventions to enable people and businesses to adapt to, and take advantage of, the machine learning driven changes to their lives and livelihoods.

Supporting the development of skills at every level

• 23% of the UK population lack basic digital skills
• Build digital skills and understanding at every level from schools to universities, and into the workplace, and ensure that opportunities are not limited by gender, ethnicity or socio-economic background
• Government should consider introducing funded Masters courses in Machine Learning to develop a pool of informed users of machine learning across business, industry, and research sectors
• There is a critical need for increased training at PhD level and beyond to invest in the next generation of research leaders in machine learning.

Creating opportunities to use machine learning

• Integrate machine learning into the UK’s industrial strategy, to help businesses make the most of its potential
• Support a new wave of research in machine learning, including in areas that can address social or ethical concerns.

Creating a data environment that supports machine learning

• 90% of the world’s data has been created within the last five years
• Continue to build on the UK’s track record of open data and safe sharing of data
• Where there is value in accessing data that cannot be open – for example medical data or commercially sensitive industry data – ensuring there are frameworks and agreements in place which facilitate appropriate data sharing in these circumstances.

An enabling governance environment

• Support an informed public debate about what we want machine learning to do, and how the benefits are distributed
• Develop a new framework for data governance – one that can keep pace with the challenge of the governance of data and its uses in the 21st century. While there may be specific questions about the use of machine learning in particular contexts, these should be handled in a sector-specific way.

Results from the UK’s first in-depth assessment of public views on machine learning – carried out by the Royal Society and Ipsos MORI – demonstrate that while most people have not heard the term ‘machine learning’ (only 9% have), the vast majority have heard about or used at least one of its applications. The public do not have a single view on machine learning – attitudes, positive or negative, vary depending on the circumstances in which machine learning is being used. For example, computers that could help with medical diagnoses, or could help relieve pressure on professionals in core services such as health and social care, education, and policing were viewed more favourably than computers that could make investments in the stock market.

As with most new technologies, some of those interviewed expressed concerns. These included: depersonalisation, or machine learning systems replacing valued human experiences; the potential impact of machine learning on employment; the potential for machine learning systems to cause harm, for example accidents in autonomous vehicles; and machine learning systems restricting choice, such as when directing consumers to specific products and services.

Ongoing public confidence will be central to realising the benefits that machine learning promises, and continued engagement between machine learning researchers and practitioners and the public will be important as the field develops. The report also calls on researchers to consider the wider impact of their work and to receive training in recognising the ethical implications.