【正文】 machine learning is more widespread than at present, many of the opportunities and challenges it discusses arise in other contexts too. So rather than be distracted with an academic discussion about terminology, we‘ve chosen to use the umbrella term artificial intelligence. There are many different kinds of algorithm used in machine learning. The key distinction between them is whether their learning is ?unsupervised‘ or ?supervised‘. Unsupervised learning presents a learning algorithm with an unlabelled set of data – that is, with no ?right‘ or ?wrong‘ answers – and asks it find structure in the data, perhaps by clustering elements together – for example, examining a batch of photographs of faces and learning how to say how many different people there are. Google‘s News service2 uses this technique to group similar news stories together, as do researchers in genomics looking for differences in the degree to which a gene might be expressed in a given population, or marketers segmenting a target audience. Supervised learning involves using a labelled data set to train a model, which can then be used to classify or sort a new, unseen set of data (for example, learning how to spot a particular person in a batch of photographs). This is useful for identifying elements in data (perhaps key phrases or physical attributes), predicting likely outes, or spotting anomalies and outliers. Essentially this approach presents the puter with a set of ?right answers‘ and asks it to find more of the same. 1 Mitchell, T. (1997), Machine Learning 2 Artificial intelligence: opportunities and implications for the future of decision making 7 amp。 EXW WKH NH\ IHDWXUH LV WKDW WKH\ HDFK XVH D OD\HUHG RU VWDJHG GHVLJQ， LQ ZKLFK RXWSXWV IURP WKH SUHYLRXV OD\HU DUH XVHG DV LQSXWV IRU WKH QH[W. amp。s role, however, is not straightforward. If they never question the advice of the machine, the decision has de facto bee automatic and they offer no oversight. If they question the advice they receive, however, they may be thought reckless, more so if events show their decision to be poor. 12 13 _framework _v Artificial intelligence: opportunities and implications for the future of decision making 11 As with any adviser, the influence of these systems on decisionmakers will be questioned, and departments will need to be transparent about the role played by artificial intelligence in their decisions Legal constraints There are currently specific legal frameworks, in addition to general legislation such as the UK Data Protection Act (1998) and the EU General Data Protection Regulation (20xx), that govern the use of citizens‘ data by government analysts, protecting rights to privacy, ensuring equal treatment for all, and safeguarding personal identity. These are an essential ingredient in maintaining public trust in government‘s ability to manage data safely. Teams making use of artificial learning approaches need to understand how these existing frameworks apply in this context. For example, if deep learning is used to infer personal details that were not intentionally shared, it may not be clear whether consent has been obtained. These current protections are effective and wellestablished. However, understanding the opportunities and risks associated with more advanced artificial intelligence will only be possible through trials and experimentation. For government analysts to be able to explore cutting edge techniques it may be desirable to establish sandbox areas where the potential of this technology can be investigated in a safe and controlled environment. In addition to these three areas, the productive use of artificial intelligence in government depends on resolving wider data science issues: skills, privacy, data quality and so on. The work of the Data Science Partnership, led by the Government Digital Service (GDS), is raising the awareness of the potential of data science across government. It also provides a focal point for sharing experiences and lessons learnt to promote innovation and the spread of best practice between departments and agencies. Artificial intelligence: opportunities and implications for the future of decision making 12 Effects on labour markets The emergence of machine learning, as well as robotics, big data and autonomous systems, is likely to have significant implications for the economy and labour markets. These technologies together can be seen as part of a new wave of ?general purpose‘ digital technologies14, parable to the steam engine, and the moving assembly line, with the potential to drive significant socioeconomic change. There is evidence to suggest that these technologies could drive productivity growth and so boost economic growth, but there is much uncertainty about the scale and the speed of these changes. They will depend on both the pace of technological development and the speed of its deployment by firms across the economy. In particular, these technologies may have a particular impact on roles in the service sector, which makes up the majority of UK employment. Whilst manufacturing has been revolutionised by technological change, personal services have been less affected and, as such, have not seen the same rates of productivity growth. However, evidence from the OECD15 indicates that the leading global service sector firms are now seeing productivity growth that outstrips their less technologicallyadvanced petitors. The precise impact on labour markets of big data and robotic and autonomous systems is the subject of much debate. There is little consensus about the possible scale of job losses due to automation, which is most often the focus of these discussions. For example, one study from Deloitte found that 35% of