By Jessica Montgomery, Senior Policy Adviser

The Royal Society’s artificial intelligence (AI) programme explores the frontiers of AI technologies, and their implications for individuals, communities, and society.

As part of our programme of international science and policy dialogue about AI, last year we worked with the American Academy of Arts and Sciences to bring together leading researchers from across disciplines to consider the implications of AI for equality, transparency, and democracy.

This blog gives some of the key points from discussions, which are summarised in more detail in the workshop discussions note (PDF).

AI for society

Today’s AI technologies can help create highly accurate systems, which are able to automate sophisticated tasks. As these technologies progress, researchers and policymakers are grappling with questions about how well these technologies serve society’s needs.

Experience of previous waves of transformative technological change shows that, even when society has a good understanding of the issues new technologies present, it is challenging to create a common vision for the future, to set in place measures that align the present with that desired future, and to engage collective action in ways that help bring it into being.

Where might this common vision for the future come from? International conventions on human rights and sustainable development already point to areas of internationally-agreed need for action, and a growing list of organisations have produced guidelines or principles aiming to shape the development of AI for societal benefit. However, the mechanisms for putting these principles into practice remain unclear. At the same time, there are many other ways in which the things that societies value seem to be contested. In this context, what might AI mean for core values like fairness, transparency, or democracy?

Fairness and equality

Real-world data is messy: it contains missing entries, it can be skewed or subject to sampling errors, and it is often re-purposed for different types of analysis. These sampling errors or other issues in data collection can skew the outputs of a machine learning system, influencing how well it works for different communities of users.

The models created by a machine learning system can also generate unfair outputs, even if trained on accurate data. In recruitment, for example, systems that make predictions about the outcomes of job offers or training can be influenced by biases arising from social structures that are embedded in data at the point of collection. A lack of diversity in the tech community can compound these technical issues, if it reduces the extent to which developers are aware of potential biases when designing machine learning systems.

In seeking to resolve these issues, both technology-enabled and human-led solutions can play a role. For example:

• Initiatives to address issues of bias in datasets, for example Datasheets for Datasets (PDF), set out recommended usage for datasets that are made available for open use.
• A combination of technical and domain insights can improve the performance of machine learning systems on ‘real world’ problems. This requires teams of people from a range of backgrounds and areas of expertise.

Interpretability and transparency

The terms ‘interpretability’ or ‘transparency’ mean different things to different people (PDF), and words such as interpretable, explainable, intelligible, transparent or understandable are often used interchangeably, or inconsistently, in debates about AI. But is this variability in meaning problematic?

There are many reasons why users or developers might want to understand why an AI system reached a decision: interpretability can help developers improve system design; it can help users assess risk or understand how a system might fail; and it might be necessary for regulatory or legal standards.

And there are different approaches to creating interpretable systems. Some AI is interpretable by design, while in other cases researchers can create tools to interrogate complex AI systems.

The significance attached to interpretability, and the type of interpretability that is desirable, will likely depend on the context and user. So the absence of a clear definition of interpretability might ultimately be helpful, if it encourages researchers to reach out to a range of stakeholders to understand what different communities need from AI systems.

Democracy and civil society

Early in the development of digital technologies, a great hope had been that they would enable people to connect and build communities in new ways, strengthening society and promoting new forms of citizen engagement. To some extent, this goal has been achieved: people have an opportunity to communicate with much broader – or much narrower – groups in ways that were not previously possible.

Many countries today are grappling with the unintended consequences of these networks. The information echo chambers that have existed in the physical world have found new manifestations in algorithmically-enabled filter bubbles, and the anonymity afforded by digital interactions has raised new questions also arise about the trustworthiness of online information.

Public and policy debates about AI and democracy have tended to concentrate on how changing patterns of news consumption might shape people’s political opinions. In response, could AI be used to improve the circulation of information, providing people with trustworthy information necessary to inform political debate? Perhaps, but insights from behavioural and social sciences show that the process of making political choices is influenced by emotional and social forces, as well as information.

Democracy is more than the exchange of information in campaigns and elections. It draws from a collection of institutions and civic interactions. Democracy persists because institutions preserve it: in the press and the electoral process, but also in courts, in schools, in hospitals, and more. If democracy resides in institutions, then how can AI support them? There is a need for spaces where people can develop civic networks or new civic institutions that allow people from different backgrounds to engage as citizens on common endeavours, as well as interacting online.

To read more about the key questions raised by this discussion, check out the meeting note (PDF), and you can also read more about the Society’s AI programme.

Advances in AI technologies are contributing to new products and services across industries – from robotic surgery to debt collection – and offer many potential benefits for economies, societies, and individuals.

With this potential, come questions about the impact of AI technologies on work and working life, and renewed public and policy debates about automation and the future of work.

Building on the insights from the Royal Society’s Machine Learning study, a new evidence synthesis by the Royal Society and the British Academy draws on research across disciplines to consider how AI might affect work. It brings together key insights from current research and policy debates – from economists, historians, sociologists, data scientists, law and management specialists, and others – about the impact of AI on work, with the aim of helping policymakers to prepare for these.

Current understandings about the impact of AI on work

While much of the public and policy debate about AI and work has tended to oscillate between fears of the ‘end of work’ and reassurances that little will change in terms of overall employment, evidence suggests that neither of these extremes is likely. However, there is consensus that AI will have a disruptive effect on work, with some jobs being lost, others being created, and others changing.

Over the last five years, there have been many projections of the numbers of jobs likely to be lost, gained, or changed by AI technologies, with varying outcomes and using various timescales for analysis.

Most recently, a consensus has begun to emerge from such studies that 10-30% of jobs in the UK are highly automatable. Many new jobs will also be created. However, there remain large uncertainties about the likely new technologies and their precise relationship to tasks. Consequently, it is difficult to make predictions about which jobs will see a fall in demand and the scale of new job creation.

Implications for individuals, communities, and societies

Despite this uncertainty, evidence from previous waves of technological change – including the Industrial Revolution and the advent of computing – can provide evidence and insights to inform policy debates today.

Studies of the history of technological change demonstrate that, in the longer term, technologies contribute to increases in population-level productivity, employment, and economic wealth. However, such studies also show that these population-level benefits take time to emerge, and there can be periods in the interim where parts of the population experience significant disbenefits. In the context of the British Industrial Revolution, for example, studies show that wages stagnated for a period despite output per worker increasing. In the same period, technological changes enabled or interacted with large population movements from land to cities, ways of working at home and in factories changed, and there were changes to the distribution of income and wealth across demographics.

Evidence from historical and contemporary studies indicates that technology-enabled changes to work tend to affect lower-paid and lower-qualified workers more than others. For example, in recent years, technology has contributed to a form of job polarisation that has favoured higher-educated workers, while reducing the number of middle-income jobs, and increasing competition for non-routine manual labour.

This type of evidence suggests there are likely to be significant transitional effects as AI technologies begin to play a bigger role in the workplace, which cause disruption for some people or places. One of the greatest challenges raised by AI is therefore a potential widening of inequality.

The role of technology in changing patterns of work and employment

The extent to which technological advances are – overall – a substitute for human workers depends on a balance of forces. Productivity growth, the number of jobs created as a result of growing demand, movement of workers to different roles, and emergence of new jobs linked to the new technological landscape all influence the overall economic impact of automation by AI technologies. Concentration of market power can also play a role in shaping labour’s income share, competition, and productivity.

So, while technology is often the catalyst for revisiting concerns about automation and work, and may play a leading role in framing public and policy debates, it is not a unique or overwhelming force. Non-technological factors – including political, economic, and cultural elements – will contribute to shaping the impact of AI on work and working life.

Policy responses and ‘no regrets’ steps

In the face of significant uncertainties about the future of work, what role can policymakers play in contributing to the careful stewardship of AI technologies?

At workshops held by the Royal Society and British Academy, participants offered various suggestions for policy responses to explore, focused around:

• Ensuring that the workers of the future are equipped with the education and skills they will need to be ‘digital citizens’ (for example, through teaching key concepts in AI at primary school-level, as recommended in the Society’s Machine Learning report);
• Addressing concerns over the changing nature of working life, for example with respect to income security and the gig economy, and in tackling potential biases from algorithmic systems at work;
• Meeting the likely demand for re-training for displaced workers through new approaches to training and development; and
• Introducing measures to share the benefits of AI across communities, including by supporting local economic growth.

While it is not yet clear how potential changes to the world of work might look, active consideration is needed now about how society can ensure that the increased use of AI is not accompanied by increased inequality. At this stage, it will be important to take ‘no regrets’ steps, which allow policy responses to adapt as new implications emerge, and which offer benefits in a range of future scenarios. One example of such a measure would be in building a skills-base that is prepared to make use of new AI technologies.

Through the varying estimates of jobs lost or created, tasks automated, or productivity increases, there remains a clear message: AI technologies will have a significant impact on work, and their effects will be felt across the economy. Who benefits from AI-enabled changes to the world of work will be influenced by the policies, structures, and institutions in place. 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 ensure that the benefits of this technology are broadly shared.

In early December, 8000 machine learning researchers gathered in Long Beach for 2017’s Neural Information Processing Systems conference. In the margins of the conference, the Royal Society and Foreign and Commonwealth Office Science and Innovation Network brought together some of the leading figures in this community to explore how the advances in machine learning and AI that were being showcased at the conference could be harnessed in a way that supports broad societal benefits. This highlighted some emerging themes, at both the meeting and the wider conference, on the use of AI for social good.

The question is not ‘is AI good or bad?’ but ‘how will we use it?’

Behind (or beyond) the headlines proclaiming that AI will save the world or destroy our jobs, there lie significant questions about how, where, and why society will make use of AI technologies. These questions are not about whether the technology itself is inherently productive or destructive, but about how society will choose to use it, and how the benefits of its use can be shared across society.

In healthcare, machine learning offers the prospect of improved diagnostic tools, new approaches to healthcare delivery, and new treatments based on personalised medicine.  In transport, machine learning can support the development of autonomous driving systems, as well as enabling intelligent traffic management, and improving safety on the roads.  And socially-assistive robotics technologies are being developed to provide assistance that can improve quality of life for their users. Teams in the AI Xprize competition are developing applications across these areas, and more, including education, drug-discovery, and scientific research.

Alongside these new applications and opportunities come questions about how individuals, communities, and societies will interact with AI technologies. How can we support research into areas of interest to society? Can we create inclusive systems that are able to navigate questions about societal biases? And how can the research community develop machine learning in an inclusive way?

Creating the conditions that support applications of AI for social good

Applying AI to public policy challenges often requires access to complex, multi-modal data about people and public services. While many national or local government administrations, or non-governmental actors, hold significant amounts of data that could be of value in applications of AI for social good, this data can be difficult to put to use. Institutional, cultural, administrative, or financial barriers can make accessing the data difficult in the first instance. If accessible in principle, this type of data is also often difficult to use in practice: it might be held in outdated systems, be organised to different standards, suffer from compatibility issues with other datasets, or be subject to differing levels of protection. Enabling access to data through new frameworks and supporting data management based on open standards could help ease these issues, and these areas were key recommendations in the Society’s report on machine learning, while our report on data governance sets out high-level principles to support public confidence in data management and use.

In addition to requiring access to data, successful research in areas of social good often require interdisciplinary teams that combine machine learning expertise with domain expertise. Creating these teams can be challenging, particularly in an environment where funding structures or a pressure to publish certain types of research may contribute to an incentives structure that favours problems with ‘clean’ solutions.

Supporting the application of AI for social good therefore requires a policy environment that enables access to appropriate data, supports skills development in both the machine learning community and in areas of potential application, and that recognises the role of interdisciplinary research in addressing areas of societal importance.

The Royal Society’s machine learning report comments on the steps needed to create an environment of careful stewardship of machine learning, which supports the application of machine learning, while helping share its benefits across society. The key areas for action identified in the report – in creating an amenable data environment, building skills at all levels, supporting businesses, enabling public engagement, and advancing research – aim to create conditions that support the application of AI for social good.

Research in areas of societal interest

In addition to these application-focused issues, there are broader challenges for machine learning research to address some of the ethical questions raised around the use of machine learning.

Many of these areas were explored by workshops and talks at the conference. For example, a tutorial on fairness explored the tools available for researchers to examine the ways in which questions about inequality might affect their work.  A symposium on interpretability explored the different ways in which research can give insights into the sometimes complex operation of machine learning systems.  Meanwhile, a talk on ‘the trouble with bias’ considered new strategies to address bias.

The Royal Society has set out how a new wave of research in key areas – including privacy, fairness, interpretability, and human-machine interaction – could support the development of machine learning in a way that addresses areas of societal interest. As research and policy discussions around machine learning and AI progress, the Society will be continuing to play an active role in catalysing discussions about these challenges.

For more information about the Society’s work on machine learning and AI, please visit our website at: royalsociety.org/machine-learning

Technology narratives

The nature, promise and risks of new technologies enter into our shared thinking through narrative – explicit or implicit stories about the technologies and their place in our lives. These narratives can determine what is salient about the technologies, influencing how they are represented in media, culture and everyday discussion. The narratives can influence the dynamics of concern and aspiration across society; the ways and the contexts in which different groups and individuals become aware of and respond to mainstream, new and emerging technologies. The narratives available at a particular point in time, and who tells them, can affect the course of technology development and uptake in subtle ways.

Whilst stories about artificial intelligence have been around for centuries, the way we think about AI is evolving. The Royal Society and Leverhulme Centre for the Future of Intelligence are exploring the ways that narratives about AI might be influencing the development of the technology.

The longevity and influence of narratives

Exploring different technology areas can show how explicit framings of a technology – how it is presented to the wider world – can be influential and long-lasting in this respect. For example in nuclear energy, Lewis Strauss, the Chairman of the US Atomic Energy Commission in 1954, stated that nuclear power would create energy “too cheap to meter”. What turned out to be over-promising for this technology shaped the arguments of sceptics, and this image continues to be used by those who critique the technology. Early scientific optimism can create inadvertent and unexpected milestones that may – rightly or wrongly – influence how technology is perceived when those milestones are not met. Such framings can be hard to shake off and can dominate more complex and subtle considerations.

Diverse visions: aspirations and concerns

Instead of promoting single framings for technologies and their applications, sowing the seeds early on for multiple voices to be heard can promote diverse narratives and ensure that the technology develops in line with genuine societal needs. A greater diversity of both actors in the development of the technology and diversity in the stories we tell about AI may elucidate new uses and governance needs. This requires extensive and continued public dialogue; the Royal Society’s public dialogue on machine learning recently explored how these views can be context specific.

Encouraging credible, trustworthy and independent communicators who do not stand to benefit personally from the technology can create more realistic narratives around new technologies and science, especially when combined with greater scientific transparency and self-correction. Comprehensive scenario planning can build trustworthy narratives, helping to analyse possible worst case accident scenarios and substantially reduce future risk.

Widening the narratives on AI

Diversifying today’s stories about AI to ensure that they are reflective of the current technological development, will give us better ideas of how AI can be used to transform our lives. Dominant narratives focus on anthropomorphised AI, but the reality of AI includes systems that are distributed, embedded in complex systems and can be found in varied applications such as helping doctors detect breast cancer or increasing the responsiveness of emergency services to flooding incidents. Adding to existing narratives with stories from underrepresented voices can also help us, as citizens, policy-makers and scientists, to imagine new opportunities and expand our assessment of how AI should be regarded, regulated and harnessed for the best possible economic and societal outcomes.

This is why the Leverhulme Centre for the Future of Intelligence and the Royal Society are exploring how visions and narratives are shaping perceptions, the development of intelligent technology and trust in its use. Find more information.

Credit: nutonomy

Over the past few weeks, we’ve blogged about how machine learning is transforming research, nuclear decommissioning, and astronomy. Building on our “Ask the Experts” panel discussion on driverless cars last July, how might machine learning shape the way we move through and interact with the immediate environment around us?

As the last in our series of blog posts on machine learning in research, we spoke to Dr Nathan Griffiths to find out more about machine learning in transport. Nathan is a Reader in the Department of Computer Science at the University of Warwick, whose research into the application of machine learning for autonomous vehicles (or “driverless cars”) has been supported by a Royal Society University Research Fellowship.

What inspired you to study science? And why Computer Science particularly?

I have always been interested in how things work and understanding the patterns we see in the world around us. Science helps us to understand patterns, provides insights into how things work, and ultimately enables us to understand how complex structures can arise from simple rules. Computer Science appeals because it can be both a creative and an analytical science – we can use computers to create patterns, or use computers as tools to understand our world, including the patterns that exist within it, and predict patterns which may arise.

How is machine learning used in your research?

My research uses machine learning to give insights into how objects or people interact and how patterns emerge and evolve. This involves the use of computational models, known as agent-based modelling, to simulate the actions and interactions of entities (such as drivers or autonomous vehicles) to see how their decisions affect the wider systems in which they operate.

More specifically, machine learning can be used to analyse how cooperation emerges through interactions between objects. In the context of autonomous vehicles, there are many situations in which it is desirable to establish a cooperative environment, without the need for centralised control. For example, who should give way at a road junction where the rules are ambiguous or typically misinterpreted, such as flashing headlights to let another driver proceed? In this case, machine learning can be trained to predict what decisions a driver or autonomous vehicle will make. Machine learning algorithms will examine previous behaviours and learn from these behaviours, to then predict what will happen in the future. Many of the initial predictions will be incorrect, however the algorithm will modify its next predictions based on what it has learnt and continue to reduce its mistakes until it can make accurate predictions. An accurate algorithm could then be used to inform the decisions vehicles make and predict vehicle journeys and routes.

Modern vehicles are equipped with numerous sensors to collect vast amounts of data during vehicle journeys. For example cameras provide live images to the vehicle which are fed into an object-recognition machine learning algorithm which learns to identify and distinguish between entities such as pedestrians, cyclists or other vehicles on the road. The images can tell the vehicle how close the objects are and change the vehicle’s behaviour accordingly, such as by reducing speed to stay a safe distance from the car in front. The use of these data also enables transport to be made more efficient, such as through automatic traffic light signal detection and live notifications of parking opportunities which could help to reduce congestion.

What are the challenges in using machine learning in your field of research?

The techniques for extracting information from data and building predictive models are advanced, but perhaps the greatest challenge is to support the interface between machine learning and human users, which is likely to lead to greater public confidence in something like autonomous vehicles.

I also expect to see advances in techniques for dealing with “concept drift”, where changes in the world   need to be accompanied by changes in a machine learnt model. Concept drift is the phenomena whereby predictions may become less accurate over time if the models do not adapt to changes in the environment. This could be an issue if learning models are not able to adapt to changes sufficiently quickly to react.

Currently, autonomous vehicles are good at detecting the world around them and reacting as necessary to changes on the road. However the next challenge is to enable machine learning systems to not only react to changes and adapt to previously unseen environments (such as a swerving car or the vehicle being driven in adverse conditions) but be able to understand the probabilities of what it thinks might happen. For example, a car which is driving alongside a busy footpath will not just react to a pedestrian stepping out into the road, but will understand that there is a chance that this might happen and actively take measures to reduce risk in case it does, perhaps by positioning itself further away from the curb.

So what are the future prospects for machine learning?

The future is very bright for machine learning. Machine learning is fundamental to the development of driverless vehicles and is rapidly transforming the automotive world; vehicle manufacturers are very keen to get computer scientists on board for the betterment of their projects.

Machine learning is already central to how our vehicles work, underpinning systems such as lane-keeping assistance, reactive cruise control and automated vehicle personalisation and recognition. There is a wide and increasing range of applications that machine learning will allow us to tackle and it is enabling us to develop fully autonomous vehicles, which will be ubiquitous in the future. I also envisage that autonomous vehicles will be connected, such that the information and insights that one vehicle has learnt can be transferred to another; which may prove particularly useful for managing concept drift.

The Royal Society is undertaking a project about machine learning and its impact for the UK economy and society. To find out more please visit our project page.

This post was originally published on the Royal Society. Click here to view the original post.

Heavy Water Components Test Reactor decommissioning.

How can we create robots that can carry out important tasks in dangerous environments? Machine learning is supporting advances in the field of robotics. To find out more, we talked to Dr Rustam Stolkin, Royal Society Industry Fellow for Nuclear Robotics, Professor of Robotics at the University of Birmingham, and Director at A.R.M Robotics Ltd, about his work combining machine learning and robotics to create practical solutions to nuclear problems.

What drew you to engineering and robotics?

There are many definitions of engineering, but the one I like is “the creation of artefacts for the benefit of mankind”. Engineering is a way of using science to be creative and to create novel technologies which can bring major societal and economic benefit.

Dr Rustam Stolkin. Source: University of Birmingham/YouTube

Robotics, in particular, attracted me because robots are inherently inter-disciplinary: they use mechanics and electronics, as well as computational elements, such as machine learning and artificial intelligence. This has allowed me to become an “expert generalist”; I specialise in being extremely cross-disciplinary. At the University of Birmingham, I have research collaborations spanning eight different schools across three colleges on the campus.

Tell us about your research?

My work focuses on developing advanced robotics technologies for nuclear decommissioning, demolishing legacy nuclear sites, and safely packaging, storing and monitoring any radiological or contaminated materials. The UK contains 4.9 million tonnes of legacy nuclear waste, mainly by-products of weapons production during the Cold War, alongside a smaller contribution from nuclear power use for civilian electricity generation. Cleaning up the UK’s legacy waste is the biggest environmental remediation project across Europe; it could take over 100 years and cost up to £220billion.

Factories are highly structured environments, where robots can be pre-programmed to make repetitive motions. This has supported the revolution in robotics that we’ve seen since the 1980s. In contrast, there has been surprisingly little use of robots in the nuclear industry, aside from a very small number of robotic vehicles deployed after the Fukushima disaster, and a very few cases of deploying bespoke robotic manipulators, controlled in rudimentary ways by joystick teleoperation.

This is in part because legacy nuclear sites, some of them over 60 years old, are highly complex and unknown environments, such as contaminated buildings that have been sealed off for decades. They have an enormous diversity of scenes, materials, and objects that nuclear decommissioning robots must interact with in complex ways, such as by grasping and cutting objects.

We have only recently discovered how to use state-of-the-art machine learning methods to help robots learn adaptive and transferable skills which they can apply in new environments. This allows them to perceive and handle new kinds of objects which they have not seen before. For example, using machine learning, a robot can be trained how to grasp and pick up one object, and then use this knowledge to adapt the grasp for a new object with a different shape.

Recent “deep learning” approaches might offer part of the solution in navigating these complex environments. However such learning approaches can rely on large amounts of “training data” – labelled examples of images of objects for example – which can be used to train the robot so that it can recognise similar objects in future. It is still very unclear how to generate sufficient training data in order to enable robots to learn how to behave in useful ways in complex real environments.

Why do we need robots to carry out nuclear decommissioning?

Contaminated materials are currently decommissioned by human workers in air-fed plastic suits, which is not only unsustainable but can be dangerous. Using robots means humans do not need to enter highly hazardous work environments, and means that zones that humans cannot enter (even in protective suits) due to high gamma radiation can still be decommissioned. Using robots also reduces “secondary nuclear waste”; for every one container filled with actual primary nuclear waste, more than ten containers become filled with contaminated plastic suits, respirators, rubber gloves, and other “secondary waste” from human entries.

How have you used machine learning to develop robots capable of nuclear decommissioning?

My lab has used robots to perform fully autonomous grasping and manipulation of nuclear waste-like objects and grasping of moving objects.

We also use state-of-the-art deep-learning neural networks to do real-time 3D reconstruction of a room, and simultaneously recognise and label materials such as concrete, metal, wood and fabric. This helps us develop ways for robots to characterise nuclear environments and nuclear waste objects in real-time. We are extending these methods to also combine visual camera information with other kinds of data from radiation, thermal and other sensors.

Where do you see machine learning going within your field of research?

Robotics engineers often argue that machine learning has only been used on relatively simple problems so far. Achieving machine learning of real robotic actions, with real objects in real scenes, is a significantly more complicated challenge that is only just starting to be attempted.

Innovative new approaches will emerge in the next few years for overcoming these challenges in enabling useful robotic behaviours in real environments. The next few years will see an interesting fusion of conventional approaches, such as hand-crafted robot control algorithms, with new approaches, like machine learning of robot behaviours.

Human-robot interaction, and human-AI collaborative working will also become increasingly important. What we are working towards now is “shared control” in which artificial intelligence and humans collaborate together to control a remote robot. For example, recent research from the French institute CNRS shows human operators controlling the motion of a robot arm and gripper in relation to an object, while the AI automatically orients the gripper with the object. Simultaneously, the AI automatically controls a second robot arm to move a camera so that it always offers the best viewpoint. Thus, human and AI collaborate to control a pair of arms for a remote handling task. In the future, this kind of human-machine collaboration could transform how we interact with nuclear and hazardous environments.

The Royal Society is currently carrying out a major policy project on machine learning, and its impact for the UK economy and society. To find out more please visit our project page.

This post was originally published by the Royal Society. Click here to view the original post.

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.