Part 2.
Disentangling the Convergence of Robotics, AI/ML & Automation
Part 2
Excerpted from:
Primer on Artificial Intelligence and Robotics
Manav Raj and Robert Seamans
Journal of Organization Design
Artificial Intelligence, Robotics & Productivity
Research on robotics and artificial intelligence builds off of the substantial body of literature surrounding innovation and technological development. Innovation is a key factor in contributing to economic growth (Solow 1957; Romer 1990) and has been an area of interest for both theorists and policymakers for decades.
Literature on robotics and automation has pointed to the impressive potential of these new technologies. Brynjolfsson and McAfee (2017) claim that artificial intelligence has the potential to be “the most important general-purpose technology of our era.” Graetz and Michaels (2018) suggest that robotics added an estimated 0.37 percentage points to annual GDP growth for a panel of 17 countries from 1993 and 2007, an effect similar to that of the adoption of steam engines on economic growth during the industrial revolution.
Distributional effects of artificial intelligence and robotics
Existing work on artificial intelligence and robotics has also attempted to identify “winners” and “losers” and to understand the distributional effects of these new technologies.
A body of this work looks at cross-industry effects. Autor and Salomons (2018) show that industry-specific productivity increases are associated with a decrease of employment within the affected industry; however, positive spillovers in other sectors more than offset the negative own-industry effect. Similarly, Mandel (2017) examines brick-and-mortar retail stores during the rise of e-commerce and finds that new jobs created at fulfillment and call centers more than make up for job losses at department stores.
Other work looks at how skill composition can affect the potential complementary or substitution effects of these new technologies. A recent working paper by Choudhury et al. (2018) looks at performance effects of the use of artificial intelligence by workers with different types of training. They find productivity with artificial intelligence technology is highly affected by an individual’s background with computer science and engineering.
Individuals who have requisite computer science or engineering skills are better able to unlock superior performance using artificial intelligence technologies than individuals without those skills. Felten et al. (2018) use an abilities-based approach to assess the link between recent advances in artificial intelligence and employment and wage growth.
They find that occupations that require a relatively high proportion of software skills see growth in employment when affected by artificial intelligence, while other occupations do not see a meaningful relationship between the impact of artificial intelligence and employment growth.
Algorithmic Decision-making and Bias
There is a growing literature in economics, strategy, and information systems that studies the use of machine learning algorithms in decision-making. Some of the authors in this literature use disaggregated, micro-level data to draw insights as to how artificial intelligence affects firms or individuals differently depending on their background.
Some of this work examines whether and how the use of artificial intelligence and machine learning tools affects individual biases. For example, machine-based algorithms appear to outperform judges in making decisions regarding potential detainment pre-trial and also reduce inequities (Kleinberg et al. 2018). Hoffman et al. (2017) find that managers who choose to hire against recommendations constructed by machine-based algorithms choose worse hires. Together, these results appear to suggest that machine learning algorithms may have potential in improving decision quality and equity. However, other research cautions that machine learning algorithms often contain their own form of bias. For example, a machine learning algorithm designed to deliver advertisements for Science, Technology, Engineering, and Math occupations targeted men more than women, despite the fact that the advertisement was explicitly intended to be gender-neutral (Lambrecht and Tucker 2018); Google’s Ad Settings machine learning algorithm displays fewer advertisements for high-paying jobs to females than to males (Datta et al. 2015); and artificial intelligence-based tools used in judicial decision-making appear to display racial biases (Angwin et al. 2016).
While these biases are troubling, some argue that compared to the counterfactual of human decision-making, algorithmic processes offer improvements in quality and fairness, and in particular, machine learning tools are best able to mitigate biases when human decision-makers exhibit bias and high levels of inconsistency (Cowgill 2019).
Recommender systems are a common tool on e-commerce platforms and frequently incorporate machine learning or artificial intelligence algorithms in the creation of their recommendations (Adomavicius and Tuzhilin 2005). Barach et al. (2018b) show that the use of recommendation systems for sellers can substitute for explicit monetary incentives in online marketplaces, highlighting one method by which firms can use artificial intelligence technologies to cut costs. Barach et al. (2018a, 2018b) study recommendation systems in online labor marketplaces and find that firms use AI-driven recommendations to identify an initial set of generally acceptable partners before relying on internal capabilities to select the best match.
In particular, the use of the recommendation system is used less for specialized jobs and for experienced employees.
Implications of Artificial Intelligence & Robotics for Organizational Design
Historically, advances in technology have reshaped the workforce and our work habits and required organizations to adjust their design paradigms in dramatic ways. For example, in the last two decades, the rise of the Internet has led firms to increasingly embrace remote work and virtual teams which can cross geographic boundaries and use virtual means to coordinate actions (Kirkman and Mathieu 2005). A significant challenge for firms lies in recognizing when this reorganization is beneficial and what are the boundaries to adjusting to the new technology. Kirkman and Mathieu (2005) note the importance of weighing the “presses” that operate on real-world teams that influence the effectiveness of face-to-face interaction compared to virtual interactions.
Similarly, artificial intelligence and robotics technology have the capacity to reshape firms and change the structure of organizations dramatically.
As discussed above, the adoption of artificial intelligence and robotics technologies will likely alter the bundle of skills and tasks that many occupations are comprised of. By that aspect alone, these technologies will reshape organizations and force firms to restructure themselves to account for these changes.
Boundaries between occupations within firms are likely to shift as some tasks are automated, and individuals within firms that choose to adopt these technologies are likely to have greater exposure to computer technologies. In addition, the composition of the labor force may change to adopt to the new set of skills that are most valued. These changes are also likely to be reflected in the design of organizations as they seek configurations to get the most value out of their human capital. Interfirm boundaries are also likely to shift as robotics and artificial intelligence technologies are adopted more widely. In a seminal article, Coase (1937) argues that firms will expand until the cost of organizing an additional transaction within the firm equals the cost of carrying out the same transaction on the market. Increased usage of artificial intelligence and robotics technology has the potential to greatly reduce costs within firms, potentially leading to fewer transactions on the market.
Tasks that previously had to be contracted to other firms may now be able to be transferred in-house, or alternatively, firms may find that tasks that were done within the firm can be more efficiently done by other organizations with greater access and facility with these technologies. In addition, a firm may avoid adopting newer technologies such as robotics if the technology is highly specific to the firm and the firm faces risk of hold-up from an opportunistic downstream customer (Williamson 1985).
Regardless of what form the effect takes, the strategy literature consistently presents evidence that incumbent firms struggle during technological discontinuities (e.g., Tushman and Anderson 1986; Henderson and Clark 1990). Despite the challenges presented by radical innovation, incumbents can be successful when they are “pre-adapted,” and their historical capabilities and assets can be leveraged to take advantage of the new technology (Klepper 2002; Cattani 2006). In the specific context of robotics technology, Roy and Sarkar (2016) present evidence that the presence of in-house users of robots and access to scientific knowledge will best prepare firms to be flexible and adapt to new, “smarter” robotics technology. To the extent that this finding is generalizable, firms may consider employing individuals with experience with these technologies and increase their facility with scientific knowledge in the area to best be able to take advantage of potential benefits from adoption.
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