The negotiations should build on existing tripartite consultation mechanisms and structures and use the already existing labour rights and norms as the point of departure. Giving the quickly evolving nature of AI and its iterative learning process, mechanisms for ex-post evaluation and tripartite governance need to be built into the regulation. The care economy, comprising both health care and education, traditionally employs a greater share of women, yet these are also sectors that suffer from underinvestment.
Digital UX/UI Designer
While the publicly available ILO statistics are at the 2-digit ISCO-08 level, the ILO holds a wealth of additional information from labour force surveys (LFS) and other national surveys in the ILO Harmonized Microdata collection. Its statistical repository contains microdata on employment at the 4-digit ISCO level for some 73 countries, and 3-digit employment data for over 117 countries. This gives us access to a sizeable repository of harmonized survey data that can be used to analyse labour market information in a wide range of countries, including the detailed distributions of employment across occupations. The internal processing of LFS data also captures additional parameters of interest, such as variations in job titles that belong to each ISCO 4-digit category across different countries. As of 2023, there are some 7,500 jobs titles mapped to ISCO at 4-digits, which we also use as a robustness test for our analysis (see Section 3). While it is impossible to predict how generative AI will further develop, the current capabilities and future potential of this technology are central to discussions of its impact on jobs.
Methods and Data
Taking GPT’s current capabilities at face value and applying it to the distribution of labour markets around the world gives us an indicative picture that suggests greater potential for job augmentation as opposed to automation. This finding represents a continuum with previous waves of technological progress, despite recurring bouts of anxiety (Autor 2015, Cherry 2020). The occupational group with the highest share of tasks exposed to GPT technology are the clerical jobs, where the majority of tasks fall at least into medium-level exposure, and about a quarter of tasks are highly exposed to potential automation.
2 Ensuring job quality under augmentation
GPT-4 API was used to generate alternative occupational definitions, tasks and task-level scores, as discussed in the text. We placed some 25,000 API requests to GPT-4 and used Ada model to generate task-level embeddings for 7,482 tasks. OpenAI provided us with a research credit in API tokens with a total value of US$ 1,000, out of which some US$ 600 have been used for this research. We are grateful to Elizabeth Proehl and Pamela Mishkin from OpenAI for their openness about the methods applied in Eloundou et al. (2023), for responding to our request for GPT-4 API access, and for the research credit of GPT tokens. Ultimately, we argue that in the realm of work, generative AI is neither inherently good nor bad, and that its socioeconomic impacts will largely depend on how its diffusion is managed. The questions of power balance, voice of the workers affected by labour market adjustments, respect for existing norms and rights, and adequate use of national social protection and skills training systems will be crucial elements for managing AI’s deployment in the workplace.
These references are then used as a proxy for the demand for specific skills and, by its extension, a proxy for the rate of technological adoption at the enterprise level. This approach works well in countries with a high online presence in recruitment, though it does not always capture the industries affected as a result of subcontracting. The approach, however, is less well suited for a global study covering countries with less online presence, as most vacancies are not advertised on online platforms but recruited through other means of communication (Georgieff and Hyee 2021).
Digital Designer
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What it costs to build a simple one-page website is far different than what it costs for a multi-page e-commerce store. Read through reviews on sites, such as Capterra, DesignRush, Clutch and G2, to get an idea of the pros and cons of the service. These reviews can often be far more enlightening than reading a company’s website copy and give tales of real-life experiences. Here are some of the top factors to think about when you’re narrowing down your options. In particular, it creates websites that are optimized for conversion, engagement and traffic.
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More importantly, the global estimate confirms the trends already observed based on the analysis of microdata for 59 countries (Figures 7a-b). Specifically, it confirms that the number of jobs in the augmentation category is significantly higher than the number of jobs that have a high automation potential. Calculating the global figures leads to an adjustment in the ranking of income groups in the augmentation category, with UMICs and HICs having the largest share of employment with high augmentation potential (13.5 and 13.4 per cent respectively) and the LICs having the lowest share (10.4 per cent).
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It uses the GPT-4 model to estimate task-level scores of potential exposure and then estimates potential employment effects at the global level as well as by country income group. Despite representing an upper-bound estimate of exposure, we find that only the broad occupation of clerical work is highly exposed to the technology with 24 per cent of clerical tasks considered highly exposed and an additional 58 percent with medium-level exposure. For the other occupational groups, the greatest share of highly exposed tasks oscillates between 1 and 4 per cent, and medium exposed tasks do not exceed 25 per cent.
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Sceptics tend to believe that these machines are nothing more than “stochastic parrots” – powerful text summarizers, incapable of “learning” and producing original content, with little future for general purpose use and unsustainable computing costs (Bender et al. 2021). This effect becomes even more apparent if we present the jobs with high automation and augmentation potential as a share of total employment for each sex. As demonstrated in Figure 7b, in high-income countries, jobs with high automation potential constitute 8.5 per cent of female employment, compared to 3.9 per cent of male employment. In addition, the share of jobs with high augmentation potential is visibly higher among women than among men in all income groups. We hope that this research will contribute to needed policy debates on digital transformation in the world of work. While the analysis outlines potential implications for different occupational categories, the outcomes of the technological transition are not pre-determined.
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