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 significance testing.
Overcoming Anchoring Bias: The Potential of AI and XAI-based Decision Support
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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.
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BGM-HAN: A Hierarchical Attention Network for Accurate and Fair Decision Assessment on Semi-Structured Profiles
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 significance testing.