Opinion paperArtificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy
Introduction
Artificial Intelligence (AI) is a concept that has been part of public discourse for decades, often depicted within science fiction films or debates on how intelligent machines will take over the world relegating the human race to a mundane servile existence in supporting the new AI order. Whilst this picture is a somewhat caricature-like depiction of AI, the reality is that artificial intelligence has arrived in the present and many of us regularly interact with the technology in our daily lives. AI technology is no longer the realm of futurologists but an integral component of the business model of many organisations and a key strategic element in the plans for many sectors of business, medicine and governments on a global scale. This transformational impact from AI has led to significant academic interest with recent studies researching the impacts and consequences of the technology rather than the performance implications of AI, which seems to have been the key research domain for a number of years.
The literature has offered various definitions of AI, each encapsulating the key concepts of non-human intelligence programmed to perform specific tasks. Russell and Norvig (2016) defined the term AI to describe systems that mimic cognitive functions generally associated with human attributes such as learning, speech and problem solving. A more detailed and perhaps elaborate characterisation was presented in Kaplan and Haenlein (2019), where the study describes AI in the context of its ability to independently interpret and learn from external data to achieve specific outcomes via flexible adaptation. The use of big data has enabled algorithms to deliver excellent performance for specific tasks (robotic vehicles, game playing, autonomous scheduling etc.) and a more pragmatic application of AI rather than the more cognitive focussed – human level AI where the complexities of human thinking and feelings have yet to be translated effectively (Hays & Efros, 2007; Russell & Norvig, 2016). The common thread amongst these definitions is the increasing capability of machines to perform specific roles and tasks currently performed by humans within the workplace and society in general.
The ability for AI to overcome some of the computationally intensive, intellectual and perhaps even creative limitations of humans, opens up new application domains within education and marketing, healthcare, finance and manufacturing with resulting impacts on productivity and performance. AI enabled systems within organisations are expanding rapidly, transforming business and manufacturing, extending their reach into what would normally be seen as exclusively human domains (Daugherty & Wilson, 2018; Miller, 2018). The era of AI systems has progressed to levels where autonomous vehicles, chatbots, autonomous planning and scheduling, gaming, translation, medical diagnosis and even spam fighting can be performed via machine intelligence. The views of AI experts as presented in Müller and Bostrom (2016), predicted that AI systems are likely to reach overall human ability by 2075 and that some experts feel that further progress of AI towards super intelligence may be bad for humanity. Society generally is yet to fully grasp many of the ethical and economic considerations associated with AI and big data and its wider impact on human life, culture, sustainability and technological transformation (Duan, Edwards, & Dwivedi, 2019; Pappas, Mikalef, Giannakos, Krogstie, & Lekakos, 2018).
The probabilistic analysis of the economic impact of AI and automation has been assessed by the World Economic Forum (WEF), where they predict that 20% of existing UK jobs could be impacted by AI technologies. This figure is greater in emerging economies such as China and India, where the level rises to 26% due to the greater scope for technological change within the manufacturing sector. AI technologies are predicted to drive innovation and economic growth creating 133 million new jobs globally by 2022, contributing 20% of GDP within China by 2030 (WEF 2018). AI technology spending in Europe for 2019 has increased 49% over the 2018 figure to reach $5.2 billion (IDC, 2019). Juniper Research (2019) highlighted that global spending on AI technologies within the consumer retail sector alone is predicted to reach $12bn by 2023, a significant rise from the current figure of $3.5bn. The research also highlighted the increasing use of AI in the form of chatbots for customer service applications, where these deployments could realise annual savings of $439m globally by 2023, up from $7m in 2019. Technology giants such as Amazon and Walmart have been experimenting with AI for some time, applying the technology to demand forecasting and supply chain fulfilment. Walmart's store of the future – Intelligent Retail Lab (IRL) is testing AI with analytics to trigger the need to respond when customers pick the last item and then track the store's ability to quickly restock the product. The Walmart IRL AI systems are supported by cameras and sensors installed throughout the store that transmit 1.6 TB of data per second to data centres and linked supply chain fulfilment (Forbes, 2019a). The use of AI technology within this sector can only increase as other firms respond to the competition from these market leaders.
The potential for AI has not been lost on the global superpowers with the US and China heavily focussed on the race for technology supremacy in this area. Currently this seems to be a battle that China seems to be winning with estimates of $12 billion spending on AI in 2017 and predicted spend of up to $20 billion by 2020. Although the Trump administration has earmarked $2 billion for the department of Defence to spend on its AI Next project, this pales into insignificance when compared to China. Chinese academics continue to publish significant levels of articles on AI and Chinese industry has increased the number of AI patents by 200% in recent years, significantly surpassing the US. Although Europe is still the lead academic publisher on AI related technologies, China now accounts for 25% of the global ouput Shoham et al. (2018). China is determined to be the world leader in AI by 2030 (Forbes, 2019b). Chinas ability to aggressively implement rather than rely solely on innovation coupled with its hypercompetitive and entrepreneurial economy and business friendly governance, has driven the AI sector forward (FT, 2019).
Whilst the benefits of greater levels of AI adoption within many sectors of the global economy are felt in the context of greater efficiency, improved productivity and reliability, this picture of positive innovation is not universally welcomed globally. Estimates for work displacement due to automation, highlight that up to a third of current work activities could be impacted by 2030 (Manyika et al., 2017). Studies have analysed the impact of this significant change, developing a narrative of a changing jobs market that is predicted to focus humans further up the value chain on more creative and cognitive orientated roles in support of AI technologies (DIN & DKE, 2018; Jonsson & Svensson, 2016). However, is this particular vision of an AI future a universal one across the globe within both developed and emerging markets? The fact that AI has the capacity to replace many rules-based and repetitive tasks, means that significant numbers of jobs that traditionally would be undertaken within emerging market economies will be lost. There are benefits of AI being centred within the developed economies where new higher skilled jobs are likely to be created, but there is a potential scenario where AI could displace millions of jobs within emerging economies. This is likely to have significant impact within Asia and Africa as traditional low skilled jobs are replaced by intelligent machine thereby damaging growth and worker livelihoods within these economies (BBC, 2019). The social/economic construction of AI, its impact on humans and society from its evolution, is still being assessed. However, it is clear that there are likely to be both winners and losers and that decision makers need to be strategic in their outlook for the future.
This study brings together the collective insight from the workshop entitled “Artificial Intelligence (AI): Emerging Challenges, Opportunities, and Agenda for Research and Practice” held at the School of Management, Swansea University, UK on 13th June 2019. Contributions were received from collaborators within industry, academia and public sector to highlight the significant opportunities, challenges and potential research agenda posed by the emergence of AI within several domains: business and management, government and public sector. science and technology. This research is presented as offering significant and timely insight to AI technology, its potential application and its impact on the future of industry and society.
The remaining sections of this article are organised as follows: Section 2 presents many of the key debates and overall themes within the literature; Section 3 details the multiple perspectives on AI technologies from the expert contributors; Section 4 presents a discussion on the key AI related topics relating to the challenges, opportunities and research agendas presented by the expert contributors. The study is concluded in Section 5.
Section snippets
Debate within existing literature
This section synthesises the existing AI focussed literature and elaborates on the key themes listed in Table 1 from the literature review. Studies included in this section were identified using the Scopus database, using the following combination of keywords
(TITLE (“Artificial intelligence”) AND TITLE (“Advantages” OR “Benefit” OR “Opportunities” OR “Limitation” OR “Challenge” OR “Barriers” OR “Shortcoming” OR “agenda” OR “Research Direction”. This approach is similar to approach employed by
Multiple perspectives from invited contributors
This section has been structured by employing an approach adopted from Dwivedi et al. (2015b) to present consolidated yet multiple perspectives on various aspects of AI from invited expert contributors. We invited each expert to set out their contribution in up to 3–4 pages, which are compiled in this section in largely unedited form, expressed directly as they were written by the authors. Such an approach creates an inherent unevenness in the logical flow but captures the distinctive
Discussion and recommendations for future research
The expert views outlined in the previous section are grouped in alignment with a number of perspectives on AI: Technological; Business and management; Arts, humanities and law; Science and technology; Government/public sector. This section pulls together many of the key themes and significant factors arising from the individual contributions to develop an informed discourse on many of the key topics and potential for future research.
Conclusions
In alignment with an approach adopted from Dwivedi et al. (2015b), this study presents a consolidated yet multiple perspective on various aspects of AI from invited expert contributors from public sector, industry and academia. The collective insights stem from the workshop titled “Artificial Intelligence (AI): Emerging Challenges, Opportunities, and Agenda for Research and Practice” held on 13th June 2019 at the School of Management, Swansea University UK. Each of the individual perspectives
Acknowledgement
This submission was developed from a workshop on Artificial Intelligence (AI), which was held at the School of Management, Swansea University on 13th June 2019. We are very grateful to everyone who attended the workshop and contributed their perspectives during the workshop and as an input to this article. We are also truly appreciative to those who although not able to attend the workshop, provided their valuable perspectives for developing this work. We are also very grateful to our Senior
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These authors have made equal contributions and are placed in alphabetical order.