Using machine learning for fraud detection in accounting raises ethical concerns and potential biases. Algorithms can inadvertently perpetuate existing biases in the data, leading to unfair targeting of certain groups1. Privacy issues arise from the extensive data collection required2. To mitigate these, it’s crucial to ensure diverse and representative training data, implement robust privacy safeguards, and regularly audit algorithms for bias12. Transparency in model decision-making processes and involving ethicists in the development stages can also help address these challenges1.