People tend to accept system advice without checking

What it is

Given advice that looks in order, people do less of the checking they would otherwise do, and release. Mosier and Skitka called this automation bias: cues that should have been inspected are not; if the system says yes it is treated as yes; if the system raises no alarm it is treated as nothing happening. It is not laziness. It is using automation as a heuristic shortcut at the moment of decision.

Omission is the system not pointing and the person not looking; commission is the system pointing wrong and the person following. Both are failures to check.

Why it happens

Checking costs working memory and time. Advice collapses the search space to one line or a few; cognitively it is cheap. Once the system is treated as a more reliable source than oneself, checking starts to feel like rudeness toward an authority rather than part of the job. If the UI presents advice as an already-filled form, an already-ticked option, an already-written mail, the cost of changing it looks higher than the cost of accepting, and the bias is amplified by structure.

The bias happens inside one decision: advice appears, the person nods. It does not need a long failure-free stretch to start. A new user will release a fluent draft the first time they see one.

Studying it

The classic paradigm is decision support: correct advice, incorrect advice, and system silence while an anomaly sits in the world. People can also see the raw data. Dependent variables: rate of following bad advice (commission), rate of missing an unflagged anomaly (omission), number and duration of checks on the raw data. Independent variables: how advice is presented (pre-filled versus options side by side), whether opening the source is forced, time pressure.

Eye movement or click logs separate “looked at the raw data” from “changed their mind because of it.” Looking and still following the advice is the bias; not having seen it is a different failure.

Where it stops holding

If the person has no independent source at all, not checking is not bias — it is having no material. When advice is blatantly absurd against domain common sense, the bias weakens; absurdity is not a protection you can design for. This entry does not treat monitoring going slack after a long quiet stretch — that is complacency, a different timescale. Nor does it treat how the bias intensifies as accuracy rises.

Applying it

• Do not pre-fill advice as “already done for you.” Show the raw object by default; keep advice as a second layer that can be opened, not as a ticked option.
• For high-consequence items, require an action that touches the source (open the original, check the amount) before release. The action must meet the content; an empty tick is not it.
• Check: plant advice that conflicts with the raw data. If the release rate is close to conflict-free advice, the UI is rewarding not checking. Then see whether any operation on the raw object happened before release — if none, checking did not exist.

Related

• Same group: L4.02.2 The bias strengthens as the system gets more accurate · L4.02.3 Providing explanations does not necessarily weaken the bias
• Nearby: L4.03 Automation Complacency · L1.09 Intervention Points in the Human Loop · L5.03 Trust Calibration
• Search terms: automation bias · commission error · omission error