Observational analysis reveals improved efficiency in financial reports with generative AI, highlighting ethical risks like data privacy and algorithmic bias.
The application of generative AI in the preparation of financial reports has significantly improved efficiency and accuracy, but it has also triggered ethical risks such as data privacy, algorithmic bias, and ambiguous responsibilities. Based on the technology-policy-organization synergy framework, the innovation of corporate ethical review mechanisms needs to focus on the following dimensions: At the technology governance level, federated learning and zero-trust architecture are integrated to achieve controllable data security, algorithmic fairness detection tools are integrated to monitor model biases in real time, and blockchain technology is used to ensure full-process traceability; At the policy compliance level, dynamic hierarchical review standards are established, international mainstream regulatory requirements are integrated, and intelligent systems are relied on to achieve automated analysis and compliance adaptation of global regulatory policies; At the organizational execution level, a multi-level review framework is established, embedding abnormal decision warning and human intervention mechanisms. Case studies show that this mechanism can effectively reduce data security risks, enhance algorithmic fairness, and strengthen responsibility traceability. In the future, it is necessary to strengthen the integration and application of cutting-edge technologies, promote global ethical standard coordination, and build a people-oriented intelligent governance paradigm.
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Ma et al. (2025) studied this question.
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