Foundation Studies and Diploma Programs
Subject Code: BBUS1BIB
Subject Name: Big Ideas in Business
Reading Time: 0 Minutes
Writing Time: 4 Hours
QUESTION AND ANSWER BOOKLET
READ INSTRUCTIONS OVERLEAF CAREFULLY
DO NOT COMMENCE WRITING UNTIL INSTRUCTED TO DO SO
Student ID #.
Family Name: ................................................................................................................
Other Name/s: ................................................................................................................
Lecturer’s name: ................................................................................................................
INSTRUCTIONS TO CANDIDATES
READ BEFORE COMMENCING
Note: A cross, (X), in a box indicates the instruction applies. A blank box indicates the instruction does not apply.
- There is one question for you to answer in this exam, via a short essay.
- In answering the question, please make reference to the two texts attached.
- Your essay will be graded based on the quality of the writing, with reference to the rubric attached.
- The marks for your essay will comprise a maximum of 50% of your final mark for this subject.
- Submit your essay to a Turnitin link on Moodle within the allowable timeframe.
This exam consists of the following sections:
THIS EXAMINATION PAPER MUST NOT BE REMOVED
FROM THE EXAMINATION ROOM
Assessment Task (50 marks)
Will artificial intelligence eliminate our jobs?
Support your view based on your reading of the two texts overpage.
Rubric: Your essay will be assessed according to the following rubric.
Argument and context
- There is one clear, well-focused proposition or argument.
- Main ideas are clear and are well-supported by detailed and accurate information.
- There is one clear, well-focused proposition or argument.
- Main ideas are clear but are not well-supported by detailed information.
- The topic and/or main ideas are not clear.
- The author demonstrates a detailed understanding of the texts and the ability to use them thoughtfully to support their answer to the question posed.
- The author demonstrates some understanding of the texts but limited ability to use them thoughtfully to support their answer to the question posed.
- The author does not demonstrate a clear understanding of the texts nor the ability to use them thoughtfully to support their answer to the question posed.
- The introduction is inviting, states the main proposition or argument and provides an overview of the paper. The conclusion is strong.
- Information is relevant and presented in a logical order.
- Uses basic essay structure with an introduction, middle and conclusion.
- Ideas do not always flow logically.
- Transition is sometimes ineffective.
- There is no clear introduction, middle and conclusion.
- Ideas do not flow logically.
- Weak transition between paragraphs.
- All sentences are well-constructed and have varied structure and length.
- There are no errors in grammar and/or spelling.
- Vivid use of words and phrases.
- The choice and placement of words seems accurate, natural, and not forced.
- Most sentences are well-constructed, but they have a similar structure and/or length.
- There are several errors in grammar and/or spelling which do not interfere with understanding.
- Words chosen communicate meaning clearly, but the writing lacks variety.
- Sentences sound awkward, are distractingly repetitive, or are difficult to understand.
- There are numerous errors in grammar and/or spelling which interfere with understanding.
- Limited range of vocabulary.
- Jargon or clichés may be present and detract from the meaning.
In-text citations and reference list (La Trobe Harvard Citation Style)
- All evidence/sources of information are correctly cited and referenced according to the specified style.
- Most evidence/sources of information are correctly cited and referenced according to the specified style.
- In-text citations are incorrect or incomplete.
- Reference list is missing.
Artificial Intelligence, Robots, and Work: Is This Time Different? As technological innovation has eliminated many types of jobs over the past few centuries, economies have evolved to create new jobs that have kept workers well employed. Is there reason to worry that the future will be different?
Stuart W. Elliott
Issues in Science and Technology, Volume 35, Issue 1, 2018
Over the past few years, there has been increasing public discussion about the potential impact of artificial intelligence (AI) and robots on work. However, despite the attention given to the issue, there has not been much progress on understanding whether or not AI and robots deserve such special treatment. Specifically, are AI and robots just like past technologies, causing shifts in the workplace but leaving the fundamental structure of work in place? Or are they unique in some way that suggests this time is different?
On the technology side, the discussion often focuses on surprising examples of tasks that AI and robots can now carry out, but without putting those examples in perspective. How do they compare to the full range of tasks at work?
On the economics side, the discussion often focuses on analyses of past changes caused by technology, but without demonstrating that they apply to the question at hand. How do we know that AI and robots will affect work in the same way that technologies have in the past?
Neither of these approaches moves the argument beyond a simple repetition of opposing conclusions that these new technologies either will or will not cause big changes to work.
To make some progress on analyzing the issue, it helps to pay close attention to the way these discussions end, which is often with an enthusiastic description of the new jobs that will come from AI and robots. Such descriptions tend to involve jobs requiring critical thinking, creativity, entrepreneurial initiative, and social interaction. Finally, there is usually a statement about the need to improve education to prepare people for these new jobs.
What I want to suggest is that the way to tell if AI and robots are different from previous technologies is by thinking more carefully about the jobs that will exist in the future and the education that will be needed to prepare for them.
In principle, as long as there are some types of work left for people to do, we can build an entire workforce and economic structure around them. Two centuries ago, roughly 80% of the workforce was involved in agriculture. Since then, a succession of new technologies mechanized many agricultural tasks, and employment on farms steadily decreased. Today, only a few percent of the workforce are in agriculture. There is no problem imagining a similar transition over the next few decades, in which AI and robots automate a large portion of current jobs, and the displaced workers--or their would-be replacements in the next generation--shift to other types of work.
With respect to the functioning of a work-based economy, it is irrelevant which jobs remain for people. It could be, as many analysts argue, that most jobs in a few decades will involve nonroutine tasks, with the routine tasks largely automated. However, the economy could still function if the technology instead allowed the reverse, eliminating the nonroutine tasks and leaving the routine tasks. That seems odd to us now, though it is what occurred in the nineteenth century, when early mechanization eliminated craftwork, replacing it with more standardized tasks.
Whether technology implementation results in the upskilling or downskilling of work has large consequences for the difficulty of the transition for the workers themselves. From a macroeconomic perspective, it is equally easy to imagine an economy requiring either more nonroutine tasks or more routine tasks. However, with respect to education and training, it is likely to be much harder to upskill a workforce to carry out more nonroutine tasks than it is to downskill a workforce to carry out more routine tasks. Indeed, in some cases, the degree of upskilling required may simply not be feasible.
To understand whether AI and robots are likely to alter the fundamental structure of work, we need to know whether these new technologies will require changes in work skills that are feasible or not. If the changes are feasible, it is most likely that the overall effect of AI and robots will look like the changes we have seen with other technological innovations. However, if the necessary changes in work skills are not feasible, then it is most likely that this time will be different.
The skill of literacy
To illustrate the point, consider literacy, a basic skill that is widely used at work. Three-quarters of US workers use their literacy skills every day at work, reading materials such as emails, directions, or reference manuals. In addition, literacy is also a key foundational skill in many occupations for more advanced or specialized tasks involving reasoning or problem solving. This is particularly true of managerial, professional, and technical occupations that tend to involve extensive use of information.
Because of the importance of literacy in the economy, we collect data on the literacy proficiency of the workforce. A few decades ago, the United States collected these data using a national test of adult literacy. Today, the Organisation for Economic Cooperation and Development (OECD) conducts an international version of this test in its Programme for the International Assessment of Adult Competencies (PIAAC), which tests a representative sample of working-age adults in each participating country. The adults selected for the sample usually take the test at home, as part of a survey administered by a trained interviewer. The survey, which usually takes between one and two hours, includes a variety of background questions in addition to the test.
Unlike tests given to students, PIAAC specifically uses test questions that aim to represent tasks adults might encounter outside school, either in their personal lives or at work. The goal is to test adults using practical tasks that are similar to some of the real tasks that adults need to carry out using literacy.
The scoring process for PIAAC grades each question in terms of five levels of difficulty, with the higher levels involving questions that are more difficult.[…].Adults score at the level where they can answer the questions correctly about two-thirds of the time. For example, adults at Level 3 will be able to answer the Level 3 questions correctly two-thirds of the time. They will be better at questions at Level 2, answering them correctly over 90% of the time. They will be worse with questions at Level 4, answering them correctly less than 30% of the time.
Using this scoring system, Figure 1 shows the PIAAC literacy results for adults in the United States in 2012, comparing them with the results from the earlier International Adult Literacy Survey (IALS) conducted in 1994.
Half of US working-age adults score at Level 2 or below on the PIAAC literacy test, meaning they cannot reliably answer either of the two example questions described above. The other half score at Level 3 or higher and can reliably answer questions such as the international call example. Only 12% of US adults score at Level 4 or Level 5 and can reliably answer questions such as the example about searching for books about genetically modified foods. These results are quite similar to the average results for other developed countries.
The comparison of the results for IALS and PIAAC shows that the literacy proficiency of US adults has declined over the past two decades. There are now fewer adults at Levels 4 and 5 and more adults at Level 2. Compared with the 1990s, a smaller percentage of US adults can now reliably answer questions such as the two examples described above. Other developed countries show a similar pattern, though the shift in the United States is larger.
The PIAAC literacy results provide a way of thinking concretely about possible changes in work skills. If most jobs in a few decades will need lower levels of literacy--comparable to Levels 2 and 3 on the PIAAC scale--then there will be no problem for the workforce to adjust because most adults already have those skills. However, if most jobs will require Level 4 literacy, it is clear that most members of the workforce would face a challenge. The first hurdle would be stopping the decline in literacy that has been taking place over recent decades.
To think more about the skill change that could be required for the workforce in the next few decades, it is helpful to consider what capabilities computers themselves will be offering with respect to literacy. To do that, I worked with a group of 11 computer scientists to evaluate the PIAAC literacy test questions to determine whether or not computers could answer them using state-of-the-art techniques in AI.
Implications for work
The investigation of AI techniques for literacy shows that current computers are clearly limited. However, the PIAAC results for people show that many people have difficulty with the same questions that are hard for computers. What does this mean for the potential impact of AI on work?
Literacy is a skill that many adults use at work. In addition to its direct applications, literacy is also a foundation skill that is critical for many specialized reasoning and problem-solving skills in specific domains. This suggests that computers may be able to carry out many of the information-related tasks currently performed by workers with literacy proficiency at Levels 2 or 3. It also suggests that computers may not be able to perform many of the information-related tasks performed by workers with literacy proficiency at Levels 4 or 5. Of course, without substantial skill development, many workers with lower literacy levels may not be able to perform those tasks either.
As a society, we make large investments in providing everyone with education to develop his or her literacy skills, along with the associated reasoning and problem-solving capabilities. However, despite years of educational preparation, many adults achieve only limited literacy proficiency. Although the basic PIAAC results offer a rather pessimistic assessment of adult skills, further considerations provide reason to hope that we can do better in preparing adults with higher-level proficiency in literacy and related information-processing skills.
First, PIAAC suggests, not surprisingly, that education makes a difference in literacy skill. Of US adults with a postsecondary degree (two-year degree and higher), 24% perform at Levels 4 or 5. Of course, this simple relationship alone does not prove causality, but other types of analyses support the common sense inference that education does indeed play a causal role in improving cognitive skills. This points the way to improving proficiency by increasing education.
Second, PIAAC suggests that the quality of education makes a difference in literacy, with some countries showing much better results than others do. For example, 22% of adults in Finland and 23% of adults in Japan are at Levels 4 or 5, compared with 12% in the United States. This points the way to improving proficiency by changing the education system to make it more effective.
Combining the effects of the quantity and quality of education suggests what may be possible at the limits of what we know how to do with large-scale education systems. For adults with postsecondary education (two-year degree and higher), 36% in Finland and 37% in Japan are at Levels 4 or 5.
These comparisons offer hope that the United States can do better, but the hope is modest with respect to the full labor force. Furthermore, we need to acknowledge that aggregate literacy levels have been moving in the wrong direction over the past few decades, despite efforts to improve education during this period. Recent history suggests that improvement in the literacy of the overall adult population is likely to be both difficult and slow.
These results raise cautions about any projections that have a large percentage of workers performing tasks involving critical thinking or creativity. Although the PIAAC literacy assessment is certainly not an assessment of those more advanced skills, it is hard to imagine adults being able to carry out meaningful levels of critical thinking or creativity without also having enough literacy to answer questions such as the book search example described above.
We know that three-quarters of US workers currently carry out tasks using literacy skills at work but that the literacy proficiency of most of these workers is at a level that computers are close to achieving. It is reasonable to expect that employers will automate many of these literacy-related tasks over the next few decades by applying the computer techniques that already exist to do so. However, our experience with educational improvement suggests that it is not reasonable to expect that the jobs of most of these workers will be able to shift toward tasks involving much higher levels of literacy and related skills, comparable to Levels 4 and 5 in PIAAC. Such a change would likely not be feasible to carry out over a few decades.
That suggests that many workers will need to switch instead toward other kinds of skills. In the abstract, this is a straightforward statement. However, whichever skills are suggested, we are faced immediately with two practical questions: how proficient are computers with respect to these other skills, and how many people are more proficient than computers are? The key issue is whether other skill areas look like literacy. For other skills, can most people do what computers cannot do, or do most people have trouble with those tasks as well?
Take social skills, for example. Occupations involving high levels of social interaction are often proposed as promising occupations for the future because people are assumed to be good at social interaction whereas machines not so much.
However, if we look closely at social skills, we are likely to find that we are overestimating the abilities of people and underestimating the abilities of computers. Most people are capable of simpler aspects of social interaction, such as facial recognition or responding to direct requests for information. However, computers also now have these capabilities. Of course, computers cannot perform more complex social interactions, such as conducting a sensitive negotiation or gaining the trust of an angry customer. However, experience suggests that those complex social interactions are also too difficult for many people.
The question for social skills--and for other major skill areas--is how computer capabilities compare with the distribution of human proficiency. Thinking in terms of literacy, computer performance at PIAAC Level 1 is not very threatening because few adults have literacy that low, but computer performance at Level 3 is quite threatening because few adults are better than that.
If computer proficiency in other major work skills looks more like PIAAC literacy at Level 1, then the adjustment to AI and robots should be similar to past technologies, at least over the next few decades. In that case, even if automation displaces many workers from some tasks, there will still be many tasks using other work skills that most people can perform.
However, if computer proficiency in other major work skills looks more like PIAAC literacy at Level 3, then AI and robots are likely to have a different effect than did past technologies. In that case, as automation displaces workers from some tasks, they are likely to find that the tasks in the jobs that remain are much more difficult to perform, and they may have trouble acquiring those skills.
So what about social skills: is computer proficiency with respect to social skills more like Level 1 or Level 3 in PIAAC literacy terms? Unfortunately, we have not yet looked carefully enough at human and computer capabilities to know the answer to that question, either for social skills or for many other major skill areas. Policy-makers should be directing researchers to answer that question.
Comparing computers and humans
In the coming years, we need to have a much better understanding of how the capabilities of computers and humans compare. In making this comparison, it is critical to consider the distribution of proficiency across the workforce for different skills, as well as the realistic potential for increasing the proficiency of the workforce for those skills where computers have already made substantial progress. It is not enough to say that some people have better skills than those provided by computers. If we are going to be able to continue a work-based economy, we need to know that most people can develop better skills than those provided by computers.
We know from the literature on the diffusion of technology that it often takes a substantial amount of time for industry to adopt and apply new technologies--time to learn about the technologies, refine them for particular applications, and invest in the technologies at scale. In many cases, widespread diffusion can take several decades. That means we have time to understand what computer capabilities currently exist and anticipate how they are likely to shift the skills needed by the workforce over the coming decade or two. However, we also know that improvements in education are often slow and difficult. So even a decade or two of warning may not be enough to develop the skills needed.
Much of the recent public conversation about AI, robots, and work has focused on observations by computer scientists about the technology and observations by economists about past changes in the labor force. However, these two perspectives are not sufficient for us to understand the likely effect of new computer capabilities on work and the way we need to respond. Crucially, we also need to hear from three other types of experts to understand whether AI and robots will cause a fundamental change in the nature of work and its role in the economy. First, we need to hear from psychologists to understand the capabilities that people have. Second, we need to hear from testing experts to understand the distribution of proficiency across the workforce for different types of skills. Finally, we need to hear from educators to understand what we know about improving human proficiency.
Only when we have assembled the insights from these three different perspectives on the capabilities of AI and robots--in addition to those from computer scientists and economists--will we be in a position to know whether this time is different.
The future of employment: How susceptible are jobs to computerisation?
Carl Benedikt Frey and Michael A. Osborne
Technological Forecasting and Social Change, Volume 114, January 2017, Pages 254-280
In this paper, we address the question: how susceptible are jobs to computerisation? Doing so, we build on the existing literature in two ways. First, drawing upon recent advances in Machine Learning (ML) and Mobile Robotics(MR), we develop a novel methodology to categorise occupations according to their susceptibility to computerisation. Second, we implement this methodology to estimate the probability of computerisation for 702 detailed occupations, and examine expected impacts of future computerisation on US labour market outcomes.
Our paper is motivated by John Maynard Keynes's frequently cited prediction of widespread technological unemployment “due to our discovery of means of economising the use of labour outrunning the pace at which we can find new uses for labour” (Keynes, 1933, p. 3). Indeed, over the past decades, computers have substituted for a number of jobs, including the functions of bookkeepers, cashiers and telephone operators (Bresnahan, 1999, MGI 2013). More recently, the poor performance of labour markets across advanced economies has intensified the debate about technological unemployment among economists. While there is ongoing disagreement about the driving forces behind the persistently high unemployment rates, a number of scholars have pointed at computer-controlled equipment as a possible explanation for recent jobless growth (see, for example, Brynjolfsson and McAfee, 2011).
The impact of computerisation on labour market outcomes is well-established in the literature, documenting the decline of employment in routine intensive occupations – i.e. occupations mainly consisting of tasks following well-defined procedures that can easily be performed by sophisticated algorithms. For example, studies by Charles et al., 2013, Jaimovich and Siu, 2012 emphasise that the ongoing decline in manufacturing employment and the disappearance of other routine jobs is causing the current low rates of employment.3 In addition to the computerisation of routine manufacturing tasks, Autor and Dorn (2013)document a structural shift in the labour market, with workers reallocating their labour supply from middle-income manufacturing to low-income service occupations. Arguably, this is because the manual tasks of service occupations are less susceptible to computerisation, as they require a higher degree of flexibility and physical adaptability (Autor et al., 2003, Goos and Manning, 2007, Autor and Dorn, 2013).
At the same time, with falling prices of computing, problem-solving skills are becoming relatively productive, explaining the substantial employment growth in occupations involving cognitive tasks where skilled labour has a comparative advantage, as well as the persistent increase in returns to education (Katz and Murphy, 1992, Acemoglu, 2002, Autor and Dorn, 2013). The title “Lousy and Lovely Jobs”, of recent work by Goos and Manning (2007), thus captures the essence of the current trend towards labour market polarisation, with growing employment in high-income cognitive jobs and low-income manual occupations, accompanied by a hollowing-out of middle-income routine jobs.
According to Brynjolfsson and McAfee (2011), the pace of technological innovation is still increasing, with more sophisticated software technologies disrupting labour markets by making workers redundant. What is striking about the examples in their book is that computerisation is no longer confined to routine manufacturing tasks. The autonomous driverless cars, developed by Google, provide one example of how manual tasks in transport and logistics may soon be automated. In the section “In Domain After Domain, Computers Race Ahead”, they emphasise how fast moving these developments have been. Less than ten years ago, in the chapter “Why People Still Matter”, Levy and Murnane (2004) pointed at the difficulties of replicating human perception, asserting that driving in traffic is insusceptible to automation: “But executing a left turn against oncoming traffic involves so many factors that it is hard to imagine discovering the set of rules that can replicate a driver's behaviour […]”. Six years later, in October 2010, Google announced that it had modified several Toyota Priuses to be fully autonomous (Brynjolfsson and McAfee, 2011).
While computerisation has been historically confined to routine tasks involving explicit rule-based activities (Autor and Dorn, 2013, Autor et al., 2003, Goos et al., 2009), algorithms for big data are now rapidly entering domains reliant upon pattern recognition and can readily substitute for labour in a wide range of non-routine cognitive tasks (Brynjolfsson and McAfee, 2011, MGI 2013). In addition, advanced robots are gaining enhanced senses and dexterity, allowing them to perform a broader scope of manual tasks (IFR 2012, Robotics-VO 2013, MGI 2013). This is likely to change the nature of work across industries and occupations.
In this paper, we ask the question: how susceptible are current jobs to these technological developments? To assess this, we implement a novel methodology to estimate the probability of computerisation for 702 detailed occupations. Based on these estimates, we examine expected impacts of future computerisation on labour market outcomes, with the primary objective of analysing the number of jobs at risk and the relationship between an occupation's probability of computerisation, wages and educational attainment.
We distinguish between high, medium and low risk occupations, depending on their probability of computerisation. We make no attempt to estimate the number of jobs that will actually be automated and focus on potential job automatability over some unspecified number of years. According to our estimates around 47% of total US employment is in the high-risk category. We refer to these as jobs at risk – i.e. jobs we expect could be automated relatively soon, perhaps over the next decade or two.
Our model predicts that most workers in transportation and logistics occupations, together with the bulk of office and administrative support workers, and labour in production occupations, are at risk. These findings are consistent with recent technological developments documented in the literature. More surprisingly, we find that a substantial share of employment in service occupations, where most US job growth has occurred over the past decades (Autor and Dorn, 2013), are highly susceptible to computerisation. Additional support for this finding is provided by the recent growth in the market for service robots (MGI, 2013) and the gradual diminishment of the comparative advantage of human labour in tasks involving mobility and dexterity (Robotics-VO, 2013).
Finally, we provide evidence that wages and educational attainment exhibit a strong negative relationship with the probability of computerisation. We note that this finding implies a discontinuity between the nineteenth, twentieth and the twenty-first century, in the impact of capital deepening on the relative demand for skilled labour. While nineteenth century manufacturing technologies largely substituted for skilled labour through the simplification of tasks (Braverman, 1974, Goldin and Katz, 1998, Hounshell, 1985, James and Skinner, 1985), the Computer Revolution of the twentieth century caused a hollowing-out of middle-income jobs (Autor and Dorn, 2013, Goos et al., 2009). Our model predicts a truncation in the current trend towards labour market polarisation, with computerisation being principally confined to low-skill and low-wage occupations. Our findings thus imply that as technology races ahead, low-skill workers will reallocate to tasks that are non-susceptible to computerisation – i.e., tasks requiring creative and social intelligence. For workers to win the race, however, they will have to acquire creative and social skills.
Write your answer below.