The purpose of my dissertation is to examine jurors' evaluation of auditor negligence in response to auditors' use of natural language processing (NLP). To test my research objective, I conducted a 2x2 between-subjects experiment with 175 jury-eligible individuals. In the online experiment, I manipulated whether the audit team analyzes contracts with NLP software or by having human auditors read the contracts. I also manipulated task complexity as complex or simple. The dependent variables include a binary verdict variable and a scaled assessment of negligence. This dissertation makes several contributions to the accounting literature and practice. First, it contributes to the recent juror literature on emerging technologies by providing evidence that jurors attribute higher negligence assessments to auditors when auditors use NLP to examine contracts than when human auditors examine contracts. I also find that auditors' use of NLP leads to jurors' higher perceived causation, which, in turn, increases jurors' assessments of auditor liability. Second, this study answers the call of other researchers to examine the relationship between task complexity and negligence in different settings. I also find a marginally significant interaction effect of the use of NLP compared to human auditors to perform audit testing that is greater for complex tasks than for simple tasks. Third, this dissertation provides new insights for practitioners and accounting firms when using emerging algorithm-based AI technologies such as NLP. As more AI technologies are used in audit practice, the findings will provide helpful insights for audit practitioners to consider when they utilize technologies to design and implement audit procedures.