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ARTIFICIAL INTELLIGENCE AND WORK

ARTIFICIAL INTELLIGENCE AND WORK (PART 2)

STEVEN M. MILLER AND THOMAS H. DAVENPORT

TWO PERSPECTIVES

This is Part 2 of a two-part article. Part 1 was published in the November issue of this journal.

The MIT Task Force addressed not only the issue of whether and when human work will be replaced by technology, but also addressed important aspects of the future workforce. One conclusion in this area highlighted the necessity of cultivating and refreshing worker skills. They stated:

  • Fostering opportunity and economic mobility necessitates cultivating and refreshing worker skills.

Enabling workers to remain productive in a continuously evolving workplace requires empowering them with excellent skills programs at all stages of life: in primary and secondary schools, in vocational and college programs, and in ongoing adult training programs.

We also found that frontline workers, in order to collaborate effectively with smart machines in their work, needed new skills. However, in contrast to the MIT report, we did not find that those skills had been acquired through “excellent skills programs” sponsored by schools, colleges, and employers. Instead, most of the new skills were acquired on the job, or by employees who were personally motivated to acquire new skills on their own.

Leading higher education institutions have already started to adopt new AI-related skills programmes, but there are still many education institutions that have not done so yet. While some progressive employers have internally implemented AI-related skills programmes, many have not. As such, the majority of existing employees in most countries are largely on their own to develop these skills. The situation in Singapore is an exception due to the SkillsFuture national initiative to provide continuing education for the existing workforce, and also due to the AI Singapore educational outreach programmes.

The MIT report also does not emphasise the importance of hybridised business and IT skills that we found in many of our case studies. In the context of these 30 case examples, organisations had to deepen their internal capabilities in IT and expand into related areas for digital transformation and data science/AI. Frontline system users had to learn how to work with the systems. Supervisors and frontline managers had to work through the process changes and learn how to manage in the new setting. Technology staff had to hybridise their skills in the direction of business and domain understanding. Business users had to hybridise in the direction of technology capabilities and digital thinking and savviness. In addition, people needed to move into new types of roles which spanned and integrated business and technology (for example, product management, data governance, ethical AI practices).

While both self-motivated learning and IT/business hybridisation are not easy to accomplish, they are relatively straightforward to do successfully for those in the workforce with the highest levels of education (undergraduate degrees and post-graduate degrees), and in fact, the MIT Task Force report shows that in recent decades, at least in US labour markets, those in the workforce with highest levels of education have mostly done well.1

The MIT report emphasises that augmentation is both a more desirable and more common outcome than large-scale automation. Augmentation is where employers create workplaces that combine smart machines with humans in close partnerships – symbiotically taking advantage of both human intelligence and machine intelligence. Most of our 30 case studies were examples of augmentation and, from what we observed, AI augmentation is largely quite successful. A few of our case studies involved some degree of full automation. Even for these few examples, there was still a need for augmentation in the sense that humans still have to supervise as well as support the continuous improvement of these fully automated tasks or processes, as well as handle special cases and disruptions.

The MIT Task Force effort included an imaginative and increasingly plausible view of how augmentation can be taken to even higher levels and expand into new types of applications. These ideas come from the Task Force research brief on “Artificial Intelligence and the Future of Work”. The research brief authors Tom Malone, Daniela Rus and Robert Laubacher emphasise “thinking less about people or computers and more about people and computers.” They elaborated as follows:

By focusing on human-computer groups – superminds – we can move away from thinking of AI as a tool for replacing humans by automating tasks, to thinking of AI as a tool for augmenting humans by collaborating with them more effectively. As we’ve just seen, AI systems are better than humans at some tasks such as crunching numbers, finding patterns, and remembering information. Humans are better than AI systems at tasks that require general intelligence – including non-routine reasoning and defining abstractions – and interpersonal and physical skills that machines haven’t yet mastered. By working together, AI systems and humans can augment and complement each other’s skills.

The possibilities here go far beyond what most people usually think of when they hear a phrase like “putting humans in the loop”. Instead of AI technologies just being tools to augment individual humans, we believe that many of their most important uses will occur in the context of groups of humans. As the Internet has already demonstrated, another very important use of information technology – in addition to AI – will be providing hyperconnectivity: connecting people to other people and often, to computers, at much larger scales and in rich new ways that were never possible before.

That’s why we need to move from thinking about putting humans in the loop to putting computers in the group.

While we did not find an explicit objective to put computers in the group in the work settings we described, using technology to attain new levels of collective coordination and intelligence is not at all far-fetched. We already see this occurring to some extent in real-world situations in our Singapore LTA Smart City rail network management case study as well as in our Certis Jewel Changi Airport example (especially the smart operations centre role). Both of these examples are in Singapore – an entire city-state economy and society making the future happen now. Over time, we expect to see more examples where smart-machine augmentation happens at the level of teams, departments, and entire business groups and organisations, and not just at the level of individual employees.

A WARNING ABOUT POLARISATION OF LABOUR MARKETS

Our research was case study-based and did not address long-term economic and labour market issues. But the MIT Work of the Future Task Force analysed US economy and labour market trends over prior decades up to the present, highlighting the stark realities of employment polarisation and diverging job quality. They spotlighted the decline in the proportion of “middle-skill jobs” in the US labour market and the fact that wages for those in low-skilled occupations have stagnated for several decades. The Task Force explained the situation as follows2:

This ongoing process of machine substitution for routine human labor tends to increase the productivity of educated workers whose jobs rely on information, calculation, problem-solving, and communication – workers in medicine, marketing, design, and research, for example. It simultaneously displaces the middle-skill workers who in many cases provided these information-gathering, organizational, and calculation tasks. These include sales workers, office workers, administrative support workers, and assembly-line production positions.

Ironically, digitalization has had the smallest impact on the tasks of workers in low-paid manual and service jobs, such as food service workers, cleaners, janitors, landscapers, security guards, home health aides, vehicle drivers, and numerous entertainment and recreation workers. Performing these jobs demands physical dexterity, visual recognition, face-to-face communications, and situational adaptability, which remain largely out of reach of current hardware and software but are readily accomplished by adults with modest levels of education. As middle-skill occupations have declined, manual and service occupations have become an increasingly central job category for those with high school or lower education. This polarization likely will not come to a halt any time soon.

The Task Force’s observation that US labour market employment polarisation has been the status quo situation for over four decades now – and that it is more extreme in the US than in other advanced economies that have experienced positive productivity growth over past decades – led to their three additional conclusions:

1) Rising labor productivity has not translated into broad increases in incomes because labor market institutions and policies have fallen into disrepair.

2) Improving the quality of jobs requires innovation in labor market institutions.

3) Investing in innovation will drive new job creation, speed growth, and meet rising competitive challenges.

These three MIT Task Force conclusions address economy-wide issues that were beyond the scope of our more focused set of company-specific case studies. However, we feel these additional national policy-oriented conclusions are important to highlight here for the following reasons. These conclusions, when combined with their other conclusions discussed above, set the stage for what is perhaps the strongest statement in their final report:3

Yet, if our research did not confirm the dystopian vision of robots ushering workers off of factory floors or artificial intelligence rendering superfluous human expertise and judgment, it did uncover something equally pernicious: amidst a technological ecosystem delivering rising productivity, and an economy generating plenty of jobs (at least until the COVID-19 crisis), we found a labor market in which the fruits are so unequally distributed, so skewed toward the top, that the majority of workers have tasted only a tiny morsel of a vast harvest.4

These conclusions are the foundations of important warning statements made by the MIT Task Force team that need to be heeded by senior managers, C-suite executives and Board of Director members in the private sector as well as by civil servants and elected government officials. Even though their statements are directly aimed at the situation in the US, the threats associated with excluding major segments of the workforce from sharing the fruits of productivity improvement and wealth creation apply to managers and government officials in all countries. The Task Force final report stated:5

Where innovation fails to drive opportunity, however, it generates a palpable fear of the future: the suspicion that technological progress will make the country wealthier while threatening livelihoods of many. This fear exacts a high price: political and regional divisions, distrust of institutions, and mistrust of innovation itself.

The last four decades of economic history give credence to that fear. The central challenge ahead – indeed the work of the future – is to advance labor market opportunity to meet, complement, and shape technological innovations. This drive will require innovating in our labor market institutions by modernizing the laws, policies, norms, organizations and enterprises that set the “rules of the game”.

CONCLUSION

For our forthcoming book, we focused on case studies of people collaborating with smart machines that were examples of successful deployment and usage of AI systems in work settings. We interviewed people who were gainfully employed, highly engaged with all of the technology and process changes that had taken place in their work setting and, for the most part, enthusiastic about working with or managing the new AI-enabled systems in their workplace. The strength of our case studies is that they provide real-world examples in actual operational everyday work settings of what it is possible to achieve in terms of people collaborating with smart machines in ways that improve business capabilities.

At the same time, as AI and other forms of advanced automation continue to diffuse across an entire economy, there are other aspects of the story. The MIT Work of the Future Task Force provides a broad view of these changes. It focuses on the multiple sides of this unfolding journey from an economy-wide and labour market perspective.


Steven M. Miller is Professor Emeritus of Information Systems, Singapore Management University. Thomas H. Davenport is the President’s Distinguished Professor of Information Technology and Management, Babson College, and Visiting Professor, Oxford University Saïd Business School. This article was originally published in the AI Singapore website.


1 Auto D., Mindell D. & Reynolds E. (Nov 2020). Section 2 – Labor Markets and Growth. The work of the future: building better jobs in an age of intelligent machines. And Autor, Mindell & Reynolds. (Nov 2019). Section 2 – The Paradox of the Present; Section 3 – Technology and Work: A Fraught History, and Section 4 – Is This Time Different?.

2 Autor, Mindel & Reynolds. (Nov 2020). Section 2.3, Employment Polarization and Diverging Job Quality.

3 Autor, Mindell & Reynolds. (Nov 2020). Introduction.

4 Autor, Mindell & Reynolds (Nov 2020) go on to explain in their introduction, “Four decades ago, for most US workers, the trajectory of productivity growth diverged from the trajectory of wage growth. This decoupling had baleful economic and social consequences: low paid, insecure jobs held by non-college workers; low participation rates in the labor force; weak upward mobility across generations; and festering earnings and employment disparities among races that have not substantially improved in decades. While new technologies have contributed to these poor results, these outcomes were not an inevitable consequence of technological change, nor of globalization, nor of market forces. Similar pressures from digitalization and globalization affected most industrialized countries, and yet their labor markets fared better.”

5 Autor, Mindell & Reynolds (Nov 2020). Introduction.