During the weekdays, Michelle Curtis, a busy homeschooling mother, spends a few hours working as an AI rater. She is presented with Google search results, chatbot responses, and other algorithm outputs, and she must assess their usefulness, accuracy, and quality within a limited timeframe. This work, primarily focused on AI-related tasks, lacks guidance and time for thorough evaluation, making it a demanding and ill-defined job.
Michelle works for Appen, a data company subcontracted by Google to evaluate their AI products and search algorithm. Many individuals worldwide, employed by Google, OpenAI, and other tech firms, perform similar tasks. These human raters play a crucial role in the development of chatbots, search engines, social media feeds, and targeted advertising systems, which are integral to the digital economy.
Michelle describes her job as physically and mentally exhausting, underpaid, and lacking clear instructions. While Google provides a comprehensive 176-page guide for search evaluations, the instructions for AI tasks are often brief and complicated. She feels a heightened moral responsibility when rating AI responses since chatbots provide authoritative answers, making accuracy vital. However, the time constraints make it impossible to perform the tasks thoroughly. Long days of work can take a toll on her well-being, especially on Sundays when she works a full eight hours.
Appen’s CEO, Armughan Ahmad, claims the company complies with minimum wages and is investing in better training and benefits for employees. However, a Google spokesperson states that Appen is solely responsible for the raters’ working conditions and training. It’s noteworthy that these workers are rarely acknowledged in tech companies’ narratives surrounding the rise of intelligent machines. Despite their significant contribution to the generative AI boom and the overall tech industry, these workers are often mentioned vaguely as providers of “human annotations” and “quality tests,” without recognition of their efforts.
As AI becomes increasingly integrated into our daily lives, the tension between tech companies promoting self-propelling software and the AI raters and workers supporting these products is starting to surface. In 2021, Appen raters formed an alliance with the Alphabet Workers Union-Communications Workers of America to demand better recognition and compensation. Michelle joined the union last year. The core question in this battle is whether these workers can be acknowledged and treated as human beings instead of tireless machines in the upcoming AI era.
The use of human ratings to improve AI models is known as reinforcement learning with human feedback (RLHF) and is employed by OpenAI, Google, Anthropic, and other companies. After processing vast amounts of text, chatbots are fine-tuned through human feedback. Although AI programs excel at pattern detection, they lack contextual understanding and the ability to differentiate between AI-generated and human-written text. Only a human evaluator can make that determination.
For example, an AI program might generate multiple recipes for a chocolate cake, which a rater then evaluates and edits. These evaluations help inform the chatbot’s statistical language model and improve its capability to write recipes that mimic human style. Evaluators also check for factual accuracy, relevance to the prompt, and flag any toxic outputs. Subject experts are particularly valuable in this process and are often paid more.
Using human evaluations to enhance algorithmic products has been a common practice for Google and Facebook for nearly a decade. However, the extent to which human ratings shape current algorithms is a point of contention. Tech companies that develop and profit from search engines, chatbots, and other algorithmic products tend to downplay the significance of human raters’ work. They claim that ratings are just one data point among many, with extensive internal development and testing playing a more substantial role. OpenAI emphasizes that training on extensive text data, rather than RLHF, is responsible for the capabilities of their models.
However, AI experts outside these companies argue differently. They believe targeted human feedback has been the most impactful factor in improving AI models, leading to significant advancements in their capabilities. Tech companies purposely downplay this human intervention, concealing the unseemly elements of their technologies, such as identifying hateful content and misinformation. Moreover, acknowledging the extent of human involvement risks dispelling the illusion of intelligent machines, which these companies find marketable.
Despite tech companies’ public statements, closer examination of their press releases and research papers reveals that they do acknowledge the value of human labor. They describe human evaluations as necessary for creating safer and more effective AI products. These companies recognize the importance of human feedback for fine-tuning their algorithms and investing in human annotations. The significant financial investment in AI ratings also confirms the significance of human involvement in improving AI models.
In conclusion, the job of an AI rater, like Michelle Curtis, is demanding and undervalued. The growing tension between tech companies framing their software as self-propelling and the workers behind these products is becoming more apparent. Human feedback plays a vital role in the development of AI technology. Despite tech companies downplaying the importance of human ratings, the value of this human labor is evident in the financial investments made. Recognizing these workers as human beings and providing fair compensation and working conditions is crucial in the future era of AI.
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