The article discusses a new AI tool developed by a team of researchers that aims to hold members of Congress accountable for potential conflicts of interest related to stock trading. The tool, called the Congressional Stock Trade Tracker, uses natural language processing and machine learning algorithms to analyze public financial disclosures and identify instances where lawmakers may have traded stocks in companies that could be impacted by their legislative actions or committee assignments. The key takeaways are: 1) The tool provides transparency by making it easier for the public to scrutinize lawmakers’ stock trades and potential conflicts of interest. 2) It addresses concerns about insider trading and the erosion of public trust in government. 3) The researchers plan to expand the tool’s capabilities to include tracking of other potential conflicts, such as campaign donations and lobbying activities. 4) The tool’s development highlights the growing role of AI in promoting accountability and ethical governance.