Artificial intelligence is more, much more, than simply upping employee productivity, unleashing digital assistants, and making inventory predictions. AI — implemented fairly and evenly means more jobs and opportunities — not less — as it proliferates. It means liberating human endeavors above and beyond the rote and mundane tasks that have defined work since the dawn of time.
The workplace has become the vital core of AI’s potential, and its implementation may mean the difference between greater equality for workers across the globe or an ever-widening income gap, states a new report an AI’s global equity, published by the United Nations and International Labor Organization. “While AI will potentially affect many aspects of our daily lives, its impact is likely to be most acute in the workplace,” the report states.
Then there is AI’s effect on working conditions and job quality. The risk is AI may become too overbearing, Berg and her co-authors state. This is caused by the “growth of algorithmic management, essentially work settings in which human jobs are assigned, optimized, and evaluated through algorithms and tracked data.”
Algorithmic management tends to drive digital labor platforms, as well as industries such as the warehousing and logistics sectors. This diminishes workers’ autonomy “to organize their work or set its pace, workers also have little ability to provide feedback or discuss with management about the organization of work.”
In the process, “whether the effect of technology on working conditions is positive or negative depends in large part on the voice that workers have in the design, implementation and use of technology,” they state. “Having such agency relies in turn on the opportunities for worker participation and dialogue.”
Economic growth will hinge upon organizations’ abilities to bring their workers closer into the decisions about AI adoption, the report’s authors, Janine Berg, Mehdi Snene, and Lucia Velasco, state. “Whether the effect of technology on working conditions is positive or negative depends in large part on the voice that workers have in the design, implementation and use of technology.”
Opportunities for career advancement are emerging across several stages of the AI “value chain” identified in the report. The types of AI-related opportunities include the following:
- Data collection: “Data is fundamental to the development and operation of AI systems. Human-prepared data is fed into AI systems to help them learn the necessary connections and patterns for functionality. With global connectivity, data collection continues to provide the essential raw material for future AI applications.”
- Data curation and annotation: “When data is collected, it is usually unstructured. Highly skilled data engineers will pre-process the data into a usable format, but ‘data labelers’ are needed to label and classify data so that it is usable. “Workers are accessible through an application programming interface, allowing programmers to call on workers with a few simple lines of code when working on an algorithm. Although there are still many data labelers working in the United States in Europe, much of the work is being done in developing countries, given the low remuneration associated with the work.” Berg and her colleagues estimate that tens of millions of data labelers exist across the globe, and this low-paying occupation is “likely to experience double-digit growth over the next five years.”
- Content moderation: This is “the process of monitoring and filtering user-generated content on digital platforms, such as social media, forums, and websites, to ensure that it complies with the platform’s guidelines and policies. Content moderation can be performed manually by human moderators or automatically by using algorithms and machine learning tools. Even with the use of algorithms and machine learning tools for content moderation, there is typically always a human involved in the process. These technologies can help automate and scale the moderation process, but they are not perfect and can sometimes make mistakes or miss nuances that a human moderator would be able to pick up on.”
- Model design, model training and tuning, deployment and maintenance: Unlike data annotation work and content moderation, these areas of AI work, which involve designing and building the infrastructure required, “require the skills of highly qualified computer scientists or graduates from other STEM fields in addition to significant investments in research and development.”
Lower-wage jobs involving data collection, annotation, and content moderation are typically found in underdeveloped parts of the world, while the higher-level tasks around model design, training, and deployment are contributing to wide disparities in AI’s economic benefits.
These “disparities in access to digital infrastructure, advanced technology, quality education, and training are deepening existing inequalities, particularly as the global economy shifts towards AI-driven production and innovation,” the co-authors warn. “Less-developed countries risk being left behind, exacerbating economic and social divides. They call for a concerted effort to bring AI development to disadvantaged regions, by enhancing digital infrastructure, building AI skills, and ensuring good quality jobs.
As a result, “the AI divide is stark,” the co-authors state. “And such investments are expensive, putting developing countries and their home-grown start-ups at a severe disadvantage. For example, OpenAI spent approximately $78 million of compute to train GPT-4, while Google’s Gemini Ultra’s compute costs were estimated at $191 million.”
