How cognitive biases influence the data verification of safety indicators

How cognitive biases influence the data verification of safety indicators: A case study in rail

Burggraaf, J.M. (TU Delft Safety and Security Science)
Groeneweg, J. (TU Delft Safety and Security Science; Universiteit Leiden; TNO)
Sillem, S. (TU Delft Values Technology and Innovation)
van Gelder, P.H.A.J.M. (TU Delft Safety and Security Science)

Values Technology and Innovation

2019

The field of safety and incident prevention is becoming more and more data based. Data can help support decision making for a more productive and safer work environment, but only if the data can be, is and should be trusted. Especially with the advance of more data collection of varying quality, checking and judging the data is an increasingly complex task. Within such tasks, cognitive biases are likely to occur, causing analysists to overestimate the quality of the data and safety experts to base their decisions on data of insufficient quality. Cognitive biases describe generic error tendencies of persons, that arise because people tend to automatically rely on their fast information processing and decision making, rather than their slow, more effortful system. This article describes five biases that were identified in the verification of a safety indicator related to train driving. Suggestions are also given on how to formalize the verification process. If decision makers want correct conclusions, safety experts need good quality data. To make sure insufficient quality data is not used for decision making, a solid verification process needs to be put in place that matches the strengths and limits of human cognition.

Cognitive bias
Human factors
Incident prevention
OHS management
Safety data
Safety indicator
Verification

http://resolver.tudelft.nl/uuid:c5ebdbd4-2bc1-4d31-9a9d-a2d279b35fc0

https://doi.org/10.3390/safety5040069

2313-576X

Safety, 5 (4)

Institutional Repository

journal article

© 2019 J.M. Burggraaf, J. Groeneweg, S. Sillem, P.H.A.J.M. van Gelder