AI remediation: a new category of work to check and correct AI outputs
According to an analysis by The Conversation, a new category of work is emerging called AI remediation – people checking and correcting erroneous outputs from language models. White-collar workers in finance, tech and retail report that this is actually adding to their workload.
An analysis by The Conversation describes a trend called AI remediation – a growing segment of work consisting of checking, correcting and fine-tuning the outputs of large language models (LLMs) and AI agents. According to the article, AI systems do produce large volumes of text, code and analysis quickly and cheaply, but they also generate errors, hallucinations and inaccurate conclusions that a human must detect and fix. Some AI companies are therefore hiring experienced professionals from various fields specifically to check the quality of their agents' outputs.
At the same time, according to a cited 2025 Harvard Business Review report, a portion of white-collar workers in finance, technology and retail say that AI actually adds to their workload, because they have to verify and fix so-called "workslop" – low-quality AI output. A cited retail director described spending more time verifying information from AI and then calling meetings to resolve errors than if they had done the task themselves.
The article points to the risk in the public sector, where, for example, the British government is experimenting with AI for summarizing comments or analyzing citizens' sentiment, while official guidelines require independent verification of outputs. As a cautionary example of failure without sufficient human oversight, it cites the Dutch tax scandal, in which an algorithm wrongly flagged more than 10 000 parents as fraudsters and drove them into debt. The text further notes that cheap AI token prices are subsidized by indebted AI companies, while human time for corrections is not subsidized, and that a similar pattern of "machine translation plus post-editing" has long been depressing pay in the translation industry.
Why it matters
The text shows that the promised time savings from deploying AI are, in many companies and institutions, partially or entirely lost to the need to check and correct outputs – which changes the expected economic benefit as well as the structure of jobs. For organizations and individuals alike, this means that without investment in human oversight, there is a risk of errors with real consequences, as the Dutch case demonstrates.
Two audiences, two different impacts
What this means
For individuals
Anyone using AI tools in their work must reckon with the fact that verifying and correcting outputs can take up a substantial part of the time saved, rather than counting on an automatic time saving.
For a business
According to the article, companies and institutions deploying generative AI must reckon with the fact that without investment in human review of outputs, the risk of errors and the hidden costs of fixing them grow, which can create new job positions focused solely on checking and correcting AI outputs.
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Event sources
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