Who evaluates the AI evaluators? - No Jitter

TEXT START: AI is beginning to evaluate some employees in workplaces like call centers.

The Dissection

This is a governance manual for making workplace surveillance appear responsible. It accurately catalogues metric drift, biased rubrics, weak validation, missing context, and the need for worker appeals. But it treats these as engineering defects surrounding an otherwise legitimate system. Its central move is to preserve the authority of automated judgment by adding human review, recalibration, and feedback loops.

The article is not defending human productive participation. It is designing a cleaner control layer for the transition from human-supervised work to machine-supervised labor.

The Core Fallacy

The article assumes that better evaluation preserves the value of the evaluated worker. Under the Discontinuity Thesis, that is false. If AI can evaluate interactions at scale, it can also replace much of the coaching, supervision, quality assurance, and managerial labor surrounding those interactions. Better measurement does not save the employment circuit; it makes the remaining humans more legible, sortable, and disposable.

The proposed human-in-the-loop is treated as a durable safeguard. Under P1 and P2, it is mainly a lag defense. Human review survives where legally necessary, politically useful, or economically cheap. It does not reverse P3: the collapse of economically necessary human participation.

Hidden Assumptions

• Human-defined standards will remain authoritative after machines outperform humans at applying them.
• Employers will fund independent review at the scale required to audit consequential decisions.
• Workers will possess enough security to challenge scores without retaliation.
• A human reviewer will retain real power to overturn the system rather than merely legitimize it.
• More accurate attribution will lead to fairer treatment instead of more efficient discipline and labor reduction.
• The organization will care whether the worker was responsible once the worker is cheaper to replace than to investigate.
• Correcting a defective model will repair the damage rather than merely document how many people were already harmed.

Social Function

Classification: partial truth wrapped in transition management and ideological anesthetic.

The article is not pure copium. It correctly recognizes that scale amplifies bad metrics, that customer failure is not identical to employee failure, and that appeals are valuable sources of ground truth. But it confines the crisis to governance. The reader is encouraged to believe that transparency, human oversight, and recalibration can domesticate the machine.

That story protects institutions from the harsher conclusion: the evaluator is owned by capital, the rubric is chosen by management, and the feedback loop primarily improves the system’s ability to allocate punishment, coaching, and redundancy. “Humans define the standard” is presented as control. In practice, humans may define the standard only until doing so becomes an unnecessary cost.

The Verdict

The article correctly diagnoses the scoring machine’s failure modes while refusing to diagnose its purpose. Its feedback loop can make automated evaluation less stupid, less arbitrary, and more defensible. It cannot preserve mass employment once AI severs the labor-to-wage-to-consumption circuit.

The decisive question is not who evaluates the AI evaluator. It is who owns it, who can refuse its judgment, and whether the worker remains economically necessary after the system improves. Under DT mechanics, human review is temporary hospice care for the old order. The loop may correct the metric; it cannot resurrect the worker.