Abstract. In the geosciences, recent attention has been paid to the influence of uncertainty on expert decision-making. When making decisions under conditions of uncertainty, people tend to employ heuristics (rules of thumb) based on experience, relying on their prior knowledge and beliefs to intuitively guide choice. Over 50 years of decision-making research in cognitive psychology demonstrates that heuristics can lead to less-than-optimal decisions, collectively referred to as biases. For example, the availability bias occurs when people make judgments based on what is most dominant or accessible in memory; geoscientists who have spent the past several months studying strike-slip faults will have this terrain most readily available in their mind when interpreting new seismic data. Given the important social and commercial implications of many geoscience decisions, there is a need to develop effective interventions for removing or mitigating decision bias. In this paper, we outline the key insights from decision-making research about how to reduce bias and review the literature on debiasing strategies. First, we define an optimal decision, since improving decision-making requires having a standard to work towards. Next, we discuss the cognitive mechanisms underlying decision biases and describe three biases that have been shown to influence geoscientists' decision-making (availability bias, framing bias, anchoring bias). Finally, we review existing debiasing strategies that have applicability in the geosciences, with special attention given to strategies that make use of information technology and artificial intelligence (AI). We present two case studies illustrating different applications of intelligent systems for the debiasing of geoscientific decision-making, wherein debiased decision-making is an emergent property of the coordinated and integrated processing of human–AI collaborative teams.
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How can geologic decision making under uncertainty be improved?
Abstract. In the geosciences, recent attention has been paid to the influence of uncertainty on expert decision making. When making decisions under conditions of uncertainty, people tend to employ heuristics (rules of thumb) based on experience, relying on their prior knowledge and beliefs to intuitively guide choice. Over 50 years of decision making research in cognitive psychology demonstrates that heuristics can lead to less-than-optimal decisions, collectively referred to as biases. For example, a geologist who confidently interprets ambiguous data as representative of a familiar category form their research (e.g., strike slip faults for expert in extensional domains) is exhibiting the availability bias, which occurs when people make judgments based on what is most dominant or accessible in memory. Given the important social and commercial implications of many geoscience decisions, there is a need to develop effective interventions for removing or mitigating decision bias. In this paper, we summarize the key insights from decision making research about how to reduce bias and review the literature on debiasing strategies. First, we define an optimal decision, since improving decision making requires having a standard to work towards. Next, we discuss the cognitive mechanisms underlying decision biases and describe three biases that have been shown to influence geoscientists decision making (availability bias, framing bias, anchoring bias). Finally, we review existing debiasing strategies that have applicability in the geosciences, with special attention given to those strategies that make use of information technology and artificial intelligence (AI). We present two case studies illustrating different applications of intelligent systems for the debiasing of geoscientific decision making, where debiased decision making is an emergent property of the coordinated and integrated processing of human-AI collaborative teams.
more » « less- NSF-PAR ID:
- 10100737
- Date Published:
- Journal Name:
- Solid Earth Discussions
- ISSN:
- 1869-9537
- Page Range / eLocation ID:
- 1 to 34
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The current study examined the potential for cognitive bias in lay examiners' comparisons of footwear impressions within the technical review process while addressing limitations of previous research. Prior research has found inconsistent results regarding the extent to which cognitive bias may influence forensic comparisons, often asking non‐experts to review forensic stimuli above their competency level. Furthermore, past research has largely ignored the potential for cognitive bias during the technical review process. In collaboration with the Miami‐Dade Police Department's Forensic Services Bureau, we examined the effects of previous examiners' level of experience and prior knowledge of the previous examiner's decision on the technical review stage of footwear impression stimuli. Before lay examiners were presented with pairs of known match and nonmatch footwear impressions, they were either told that an expert or a novice had previously examined them and determined them to be either a match, nonmatch, or inconclusive (plus a no‐information condition). Participants then evaluated each pair of footwear impressions to make their own determinations of match, nonmatch, or inconclusive. Results support the technical review process for all decision types, as known nonmatch stimuli were generally more difficult for lay examiners to assess than known match stimuli. Knowledge of
a prior examiner's decision and status was observed only when the prior decision was inconclusive, suggesting the need for inclusion of inconclusive decisions in future research examining cognitive bias in forensic examination.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/se-2019-70
