Paper Type

ERF

Paper Number

1251

Description

As AI becomes embedded in everyday professional and personal tasks, the need for accessible and understandable explanations for AI decisions is compelling. Accordingly, firms are developing solutions (either voluntarily or through government mandate) to provide explanations of AI decision-making to those who are subject the consequences of the decisions. Given the recency of this progress, limited research has been conducted as to how the introduction of these explanations may alter the relationship between humans, agents, model findings, and outcomes. First, humans have a long history of “outsmarting” systems that affect their welfare. Second, humans are not passive recipients of the explanations for model decision-making but will incorporate new information into future actions. Our research interest is examining how humans come to evaluate AI decision-making without explicit explanations and how contingencies in explanations alter the human’s behavior and model operations.

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Aug 9th, 12:00 AM

Interpretable Models & Metabehaviors: A Proposed Study of Microlending

As AI becomes embedded in everyday professional and personal tasks, the need for accessible and understandable explanations for AI decisions is compelling. Accordingly, firms are developing solutions (either voluntarily or through government mandate) to provide explanations of AI decision-making to those who are subject the consequences of the decisions. Given the recency of this progress, limited research has been conducted as to how the introduction of these explanations may alter the relationship between humans, agents, model findings, and outcomes. First, humans have a long history of “outsmarting” systems that affect their welfare. Second, humans are not passive recipients of the explanations for model decision-making but will incorporate new information into future actions. Our research interest is examining how humans come to evaluate AI decision-making without explicit explanations and how contingencies in explanations alter the human’s behavior and model operations.

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