HomeEthics & SocietyLong hours and low wages: the human labour powering AI’s development

Long hours and low wages: the human labour powering AI’s development

The Finnish tech firm Metroc recently began using prison labour to coach a big language model to enhance artificial intelligence (AI) technology. For 1.54 euros an hour prisoners answer easy questions on snippets of text in a process referred to as data labelling.

Data labelling is commonly outsourced to labour markets within the Global South where firms can find staff who’re fluent in English and willing to work for low wages.

Due to the shortage of Finnish speakers in these countries, nonetheless, Metroc has tapped into an area source of low cost labour. Were it not for the prison labour program, Metroc would likely be hard-pressed to search out Finns willing to take data-labelling jobs that pay a fraction of the typical salary in Finland.

These cost-cutting strategies not only highlight the numerous amount of human labour still required to advantageous tune AI, but in addition they raise vital questions on the long-term sustainability of such business models and practices.

AI’s labour problem

The ethical ambiguity of prison labour-sourced AI is a component of a bigger story in regards to the human cost behind AI’s significant growth in recent times. One issue that has change into more evident over the past 12 months revolves across the query of labour.

Leading AI firms should not denying their use of outsourced and low-wage labour to do work like data labelling. However, the hype around tools like OpenAI’s ChatGPT has drawn attention away from this aspect of the technology’s development.

As researchers, including myself, try to grasp the perceptions and use of AI in higher education, the moral problems related to current AI models proceed to pile up. These include the biases that AI is susceptible to reproducing, the environmental impact of AI data centres, and privacy and security concerns.

Current practices of outsourcing data labelling work expose an uneven global distribution of AI’s costs and advantages, with few proposed solutions.

The implications of this example are twofold.

First, the huge amount of human labour that remains to be required to shape the “intelligence” of AI tools should give users pause when evaluating the outputs of those tools.

Second, until AI firms take serious steps to handle their exploitative labour practices, users and institutions will want to reconsider the so-called values or advantages of AI tools.

What is data labelling?

The “intelligence” component of AI still requires significant human input to develop its data processing capabilities. Popular chatbots like ChatGPT are pre-trained (hence, the PT in GPT). A critical phase within the pre-training process consists of supervised learning.

During supervised learning, AI models learn learn how to generate outputs from data sets which might be labelled by humans. Data labellers, just like the Finnish prisoners, perform different tasks. For example, labellers might need to verify whether a picture comprises a certain feature or to flag offensive language.

In addition to improving accuracy, data labelling is obligatory to enhance the “safety” of AI systems. Safety is defined in keeping with the goals and principles of every AI firm. A “protected” model for one company might mean avoiding the danger of copyright infringement. For one other, it would entail minimizing false information or biased content and stereotypes.

For hottest models, safety implies that the model mustn’t generate content based on prejudiced ideologies. This is partly achieved through a properly labelled training data set.

Tech firms depend on low-wage labour around the globe to develop the programs that power their AI systems.

Who are data labellers?

The job of combing through hundreds of probably graphic images and snippets of text has fallen on data labellers largely concentrated within the Global South.

In early 2023, magazine reported on OpenAI’s contract with Sama, an information labelling firm based in San Francisco. The report revealed that employees at a Kenyan satellite office were paid as little as US$1.32 per hour to read text that “appeared to have been pulled from the darkest recesses of the web.”

also investigated the worldwide economic realities of knowledge labellers in South America and East Asia, a few of whom worked greater than 18 hours per day to earn lower than their country’s minimum wage.

The has taken a detailed take a look at ScaleAI which employs a minimum of 10,000 staff within the Philippines. The newspaper revealed the San Francisco-based company “paid staff at extremely low rates, routinely delayed or withheld payments and provided few channels for staff to hunt recourse.”

The data labelling industry and its required workforce is ready to expand drastically in the approaching years. Consumers who increasingly use AI systems must understand how they’re built in addition to the harm and inequities being perpetuated.

Transparency needed

From prisoners to gig staff, the potential for exploitation is real for all entwined in big AI’s thirst for data to fuel greater (and possibly more unpredictable) models.

As institutions and individuals are swept up by the momentum of AI and all of its guarantees, the general public tends to pay less attention to moral elements of the technology’s development.

Researchers at Stanford University recently launched a web site showcasing their Foundation Model Transparency Index. The index provides metrics on measures of transparency for essentially the most widely used AI models. These metrics range from how transparent firms are about where they source their data to how clear they’re on the potential risks of their models.

Ten AI models were examined based on criteria of how transparent the corporate that operates them is about its labour practices. The index shows that tech firms have much work to do to enhance transparency.

AI is becoming a growing a part of our increasingly digital lives. That is why we must remain critical of a set of technologies that, unchecked and unexamined, may cause more problems than they solve and deepen divides on this planet somewhat than eliminate them.


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