Work Risks Grow More Complex, Deputy Manpower Minister Calls for Stronger OSH Workforce

TEXT START: JAKARTA, Aktualita.co — Changes in work patterns, digitalization, automation, and the use of artificial intelligence are beginning to alter the landscape of occupational safety and health (OSH) risks in Indonesia.

The Dissection

The article converts AI-driven structural disruption into a manageable occupational-governance problem. It inventories accidents, underreported diseases, weak audit coverage, climate exposure, and emerging digital risks, then prescribes training, certification, data systems, reskilling, and stronger supervision.

That is institutional self-preservation disguised as adaptation. The article expands the jurisdiction of the OSH apparatus while avoiding the decisive questions: who owns the automation, who loses bargaining power, who captures the productivity gains, and whether mass employment remains necessary at all.

The Core Fallacy

It treats AI as merely another workplace hazard, comparable to machinery, chemicals, or extreme weather. Under the Discontinuity Thesis, AI is more fundamental: it is the mechanism that severs the employment → wage → consumption circuit.

Better OSH data may reduce injuries. More audits may improve compliance. Neither restores productive participation once cognitive work becomes cheaper and more capable when performed by machines. Worse, the proposed “future competencies”—data processing, risk mapping, analysis, communication, and organizational change—are themselves cognitive functions exposed to automation. The article mistakes rising complexity for durable demand for human OSH labor.

Hidden Assumptions

• Mass human employment will remain the economic foundation despite automation.
• Reskilling will preserve workers’ relevance rather than merely delay displacement.
• Demand for OSH professionals will grow faster than AI’s ability to automate monitoring, reporting, analysis, and compliance administration.
• Companies can or will extend meaningful safety systems to roughly 450,000 targets when only about 18,000 have undergone external SMK3 audits—approximately 4%.
• Better reporting will reveal hidden occupational disease rather than expose a system structurally incentivized to underreport it.
• Regulation and labor inspection can adapt faster than firms deploy new technology.
• AI-related harm is primarily a safety and mental-health issue, rather than a power and ownership issue.
• Human professionals will remain the control layer instead of becoming a smaller class of supervised verifiers and liability buffers.

Social Function

Classification: partial truth, transition management, and ideological anesthetic.

The accident figures and audit gap describe genuine institutional failure. The anesthesia lies in presenting the solution as professional upgrading. It gives workers and policymakers a curriculum problem to solve while leaving the ownership problem untouched. “Upskilling” becomes the ceremonial language used to keep labor running toward relevance after the machine has already begun removing the finish line.

The Verdict

The article is accurate at the shop-floor level and blind at the system level. It correctly identifies deteriorating safety capacity, but it mistakes a stronger OSH bureaucracy for protection against AI-driven labor obsolescence.

OSH will retain temporary niches in physical inspection, liability, verification, maintenance, and high-risk industries. Those are lag defenses and servitor positions, not evidence of mass labor resilience. The article is a competent safety memo attached to a larger collapse it refuses to name.