Pith. sign in

REVIEW 1 cited by

Overcoming Anchoring Bias: The Potential of AI and XAI-based Decision Support

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2405.04972 v1 pith:IC2TNV6G submitted 2024-05-08 cs.CY cs.AIcs.HCcs.LGecon.GNq-fin.EC

classification cs.CYcs.AIcs.HCcs.LGecon.GNq-fin.EC
keywords anchoringbiasdecisionsdecisionsupportxai-basedadvanceseffect
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Information systems (IS) are frequently designed to leverage the negative effect of anchoring bias to influence individuals' decision-making (e.g., by manipulating purchase decisions). Recent advances in Artificial Intelligence (AI) and the explanations of its decisions through explainable AI (XAI) have opened new opportunities for mitigating biased decisions. So far, the potential of these technological advances to overcome anchoring bias remains widely unclear. To this end, we conducted two online experiments with a total of N=390 participants in the context of purchase decisions to examine the impact of AI and XAI-based decision support on anchoring bias. Our results show that AI alone and its combination with XAI help to mitigate the negative effect of anchoring bias. Ultimately, our findings have implications for the design of AI and XAI-based decision support and IS to overcome cognitive biases.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. BGM-HAN: A Hierarchical Attention Network for Accurate and Fair Decision Assessment on Semi-Structured Profiles

    cs.LG 2025-07 conditional novelty 4.0 of 10

    BGM-HAN, a hierarchical attention model enhanced with byte-pair encoding, multi-head attention, and gated residuals, reports 85% accuracy on a proprietary admissions dataset, beating several baselines but with no sign...

Pith tools