Artificial Intelligence, Human Capital, and Innovation

Gofman, M. N.; Jin, Zhao

doi:10.2139/ssrn.3449440

Cited by 8 publications

(5 citation statements)

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“…As stated by Gofman and Jin (2019), human capital is essential for AI-driven innovation, which increases the scarcity of qualified experts and raises their price. The value of AI technologies being very high for large IT and software companies, they attract most of the engineering force with attractive financial conditions (Rock, 2019;Alekseeva et al, 2020) even drying out the academic market of qualified AI professors (Gofman and Jin, 2019). In healthcare, it seems that the value hospitals can draw from ML engineers is not yet perceived as high enough to compete for this valuable human capital.…”

Section: Demand-side: Hospitals Transformation and Needsmentioning

confidence: 99%

Machine learning in healthcare: Mirage or miracle for breaking the costs dead-lock?

Ayoubi¹

2023

Preprint

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The ageing population in all developed economies and the limited productivity characterizing the healthcare sector are leading to alarmingly increasing costs. The current rapid advances in machine learn-ing (ML), a subfield of artificial intelligence (AI), offer new automation and prediction capabilities that could, if properly integrated, help address the healthcare costs deadlock. Are ML-driven solutions the ap-propriate ingredient to produce this necessary transformation, or are they condemned to face the same destiny as previous attempts to remodel healthcare delivery? This paper aims at bringing first elements to answer this question by providing both qualitative and quantitative evidence on the development of ML in healthcare and discussing the organizational and institutional conditions for the ML potential to be real-ized. Building on a novel search methodology for publications and patents in ML and on hospital surveys, our results reveal two major observations. On the one hand, while the publication rate in the field has tripled in the last decade, the level of patenting in ML applied to healthcare has so far been relatively low. This result has several potential explanations, such as the early stage of the technology, its rapid growth, and the emergence of new business models based on data accumulation and appropriation rather than patenting. On the other hand, the bulk of firms’ publications are produced by IT firms rather than by com-panies in healthcare. This last observation seems to be driven by the disruptiveness of the new ML tech-nology allowing the entry of new actors in healthcare. The technology producers benefit from their mas-tery of ML and the lack of investment and capabilities among health experts.

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Section: Demand-side: Hospitals Transformation and Needsmentioning

confidence: 99%

Machine learning in healthcare: Mirage or miracle for breaking the costs dead-lock?

Ayoubi¹

2023

Preprint

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“…Their data scientists publish most of the research papers in AI, “an extraordinary situation compared with any other field of science” (Jacobides et al, 2021, p. 420). With so few teaching faculty, colleges and universities have had to ration computer science enrollments, which has significantly disrupted the knowledge transfer between generations (Gofman & Jin, 2022). In the UK, university administrators contemplate a “missing generation” of data scientists (Sample, 2017).…”

Section: Stage Two: the Concentration Of Computational Knowledge Prod...mentioning

confidence: 99%

Surveillance Capitalism or Democracy? The Death Match of Institutional Orders and the Politics of Knowledge in Our Information Civilization

Zuboff¹

2022

Organization Theory

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Surveillance capitalism is what happened when US democracy stood down. Two decades later, it fails any reasonable test of responsible global stewardship of digital information and communications. The abdication of the world’s information spaces to surveillance capitalism has become the meta-crisis of every republic because it obstructs solutions to all other crises. The surveillance capitalist giants–Google, Apple, Facebook, Amazon, Microsoft, and their ecosystems–now constitute a sweeping political-economic institutional order that exerts oligopolistic control over most digital information and communication spaces, systems, and processes. The commodification of human behavior operationalized in the secret massive-scale extraction of human-generated data is the foundation of surveillance capitalism’s two-decade arc of institutional development. However, when revenue derives from commodification of the human, the classic economic equation is scrambled. Imperative economic operations entail accretions of governance functions and impose substantial social harms. Concentration of economic power produces collateral concentrations of governance and social powers. Oligopoly in the economic realm shades into oligarchy in the societal realm. Society’s ability to respond to these developments is thwarted by category errors. Governance incursions and social harms such as control over AI or rampant disinformation are too frequently seen as distinct crises and siloed, each with its own specialists and prescriptions, rather than understood as organic effects of causal economic operations. In contrast, this paper explores surveillance capitalism as a unified field of institutional development. Its four already visible stages of development are examined through a two-decade lens on expanding economic operations and their societal effects, including extraction and the wholesale destruction of privacy, the consequences of blindness-by-design in human-to-human communications, the rise of AI dominance and epistemic inequality, novel achievements in remote behavioral actuation such as the Trump 2016 campaign, and Apple-Google’s leverage of digital infrastructure control to subjugate democratic governments desperate to fight a pandemic. Structurally, each stage creates the conditions and constructs the scaffolding for the next, and each builds on what went before. Substantively, each stage is characterized by three vectors of accomplishment: novel economic operations, governance carve-outs, and fresh social harms. These three dimensions weave together across time in a unified architecture of institutional development. Later-stage harms are revealed as effects of the foundational-stage economic operations required for commodification of the human. Surveillance capitalism’s development is understood in the context of a larger contest with the democratic order—the only competing institutional order that poses an existential threat. The democratic order retains the legitimate authority to contradict, interrupt, and abolish surveillance capitalism’s foundational operations. Its unique advantages include the ability to inspire action and the necessary power to make, impose, and enforce the rule of law. While the liberal democracies have begun to engage with the challenges of regulating today’s privately owned information spaces, I argue that regulation of institutionalized processes that are innately catastrophic for democratic societies cannot produce desired outcomes. The unified field perspective suggests that effective democratic contradiction aimed at eliminating later-stage harms, such as “disinformation,” depends upon the abolition and reinvention of the early-stage economic operations that operationalize the commodification of the human, the source from which such harms originate. The clash of institutional orders is a death match over the politics of knowledge in the digital century. Surveillance capitalism’s antidemocratic economic imperatives produce a zero-sum dynamic in which the deepening order of surveillance capitalism propagates democratic disorder and deinstitutionalization. Without new public institutions, charters of rights, and legal frameworks purpose-built for a democratic digital century, citizens march naked, easy prey for all who steal and hunt with human data. Only one of these contesting orders will emerge with the authority and power to rule, while the other will drift into deinstitutionalization, its functions absorbed by the victor. Will these contradictions ultimately defeat surveillance capitalism, or will democracy suffer the greater injury? It is possible to have surveillance capitalism, and it is possible to have a democracy. It is not possible to have both.

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“…Authors may also be interested in studying the extent to which the current structure of the AI industry depends on the AI production function. For example, many recent state-of-theart results have come out of private AI labs rather than universities, with many researchers moving from university positions into private industry [14]. To what extent are these changes a result of e.g.…”

Section: Surveillance and Privacymentioning

confidence: 99%

Social and Governance Implications of Improved Data Efficiency

Tucker

Anderljung

Dafoe

2020

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

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Many researchers work on improving the data efficiency of machine learning. What would happen if they succeed? This paper explores the social-economic impact of increased data efficiency. Specifically, we examine the intuition that data efficiency will erode the barriers to entry protecting incumbent data-rich AI firms, exposing them to more competition from data-poor firms. We find that this intuition is only partially correct: data efficiency makes it easier to create ML applications, but large AI firms may have more to gain from higher performing AI systems. Further, we find that the effect on privacy, data markets, robustness, and misuse are complex. For example, while it seems intuitive that misuse risk would increase along with data efficiency -as more actors gain access to any level of capability -the net effect crucially depends on how much defensive measures are improved. More investigation into data efficiency, as well as research into the "AI production function", will be key to understanding the development of the AI industry and its societal impacts.

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Artificial Intelligence, Human Capital, and Innovation

Cited by 8 publications

References 53 publications

Machine learning in healthcare: Mirage or miracle for breaking the costs dead-lock?

Machine learning in healthcare: Mirage or miracle for breaking the costs dead-lock?

Surveillance Capitalism or Democracy? The Death Match of Institutional Orders and the Politics of Knowledge in Our Information Civilization

Social and Governance Implications of Improved Data Efficiency

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