There is no doubt that AI influences the way people live, work, see and think in the contemporary world. AI is a great step forward in the evolution of technological progress and the development of skills and knowledge for social, economic, cultural and political progress. However, the unbridled marketeers of AI and their corporate masters are trying to deepen the commercialisation of AI by adopting it in every sphere of life and society without critical human oversight. Such rampant commercialisation of AI development, adoption and usage to generate profit for AI companies is destroying both the essentialist and emancipatory potentials of AI as a skill set and as a knowledge tradition. Commercialisation for profit overlooks the existing challenges of AI in terms of its critical operational capabilities without human oversight. Such corporate assault is not only dangerous for people but also an impediment to the further scientific and technological development of AI.
In January 2026, the Utah Office of Artificial Intelligence Policy (OAIP) launched a pilot programme with the help of an AI health-tech company called Doctronic to use an AI-led automated system to process renewals of refill or repeat medications for 190 chronic conditions based on previous prescriptions by licensed physicians. The OAIP designed this pilot programme to reduce the workload of medical practitioners and improve patients’ access to medical services, with Doctronic functioning “within strict parameters and with physician oversight.” However, the Utah Medical Licensing Board argued that the pilot programme “compromises patient safety” and puts patients at risk. The board further argued that “We must not allow AI or other financial motivations to override this obligation, yet that is precisely what occurred here.” Within three months of the pilot programme, the Utah Medical Licensing Board called for the immediate suspension of a state-authorized pilot of AI prescription renewal in April 2026. The first legally AI-led healthcare system to renew prescriptions for chronic non-controlled medications came to an end.
However, AI companies are pressing governments around the world to implement the use of AI in healthcare facilities to generate limitless profit, while medical professionals, clinical practitioners, and medical researchers are arguing for a “human loop” in the AI life cycle—from problem formulation, data collection, storage, management, classification, curation, model development, validation, and generalisation, through origin, growth, development, functionality, and review, to final decision-making on AI products, processes, programmes, and their usage in medical practice—to ensure patient safety. These arguments apply not only to the healthcare sector but to all other sectors as well.
AI and its language models reflect demographic, racial, gender, sexual, social, cultural, and class biases in problem formulation, data collection, classification, management, model development, and validation. Biased AI-led algorithms, models, and processes produce wrong diagnoses and wrong prescriptions as solutions to problems. The uncritical development, adoption, and usage of AI create challenges that threaten human welfare in different spheres of life. It also undermines AI as a skill and as a knowledge tradition.
The unbiased scientific development of AI and the universalisation of its availability, access, and usage can develop AI’s greater potential, whereas commercialisation in the name of the intellectual property rights of AI companies is undermining AI, its reliability, and scientific progress. The prejudice, bias, and discrimination from data collection to the training of AI models create an unfair technological condition in which prevailing discrimination and bias are reproduced within technological development that does not contribute to progressive and egalitarian social change. Automation is a great way to get away from boring, tedious, and repetitive generic clerical work. The success of AI adoption for automation depends on human oversight and accountability of developers. AI governance must be organised transparently so that people control the technology, rather than being controlled by it in the name of automation.
The writer teaches at the London Metropolitan University.
