Automation bias

Automation bias is a specific cognitive bias where humans disproportionately favor information or suggestions output by automated systems, sometimes to the detriment of other important data or their own judgment. This bias can lead individuals to overlook errors or incorrect recommendations made by machines. It is particularly prevalent in situations where automated systems are designed to aid decision-making processes.

How it works

Automation bias occurs when individuals rely too heavily on automated systems, assuming that these systems are invariably correct, and, as a result, undervaluing or ignoring other sources of information, such as their own knowledge, experience, or common sense. This overreliance arises from the perceived infallibility of technology, further encouraged by the complexity and sophistication of modern automation tools.

Examples

• Pilots ignoring manual flight instruments and relying solely on autopilot data, which could perpetuate errors in navigation.
• Medical professionals depending on diagnostic software for patient treatment decisions, potentially overlooking symptoms not recognized by the system.
• Financial analysts trusting automated trading bots, possibly ignoring market signals that do not conform to algorithm predictions.

Consequences

The consequences of automation bias can be significant, including errors in judgment, critical oversight, and at the extreme, catastrophic failures such as accidents and financial losses. Healthcare misdiagnoses, aviation accidents due to incorrect autopilot data interpretation, and significant losses in automated financial transactions are some real-world examples.

Counteracting

To counteract automation bias, it's crucial to implement strategies that emphasize the value of human judgment alongside automated tools. Training individuals to critically assess automation-generated outputs, setting up checks and balances that require human oversight, diversifying information input channels, and building interfaces that encourage user engagement with data can mitigate this bias.

Critiques

Automation bias has sparked debates regarding the extent to which humans should depend on technology, highlighting the critical balance needed between automation and human oversight. Critics argue that this bias underscores the overestimated trust in technology, often at the expense of human intuition and adaptability.

Fields of Impact

Also known as

Relevant Research

• Automation bias in intelligent time-critical decision support systems

  Linda J. Skitka, Kathleen L. Mosier, Mark Burdick (1999)

  Journal of Human Performance in Extreme Environments

• The responsibility-number lens in automation bias

  Anna C. Cox, Jonathan R. Flanagan, Ann Blandford (2005)

  British Journal of Psychology