Kilpatrick’s David Hughes recently moderated a panel discussion at the ABA-IPT Advanced State Income, Advanced Sales/Use, and Advanced Property Tax Seminar in New Orleans addressing “Artificial Intelligence: What Tax Professionals Need to Know.” As you know, AI is everywhere - including the workplace. There are serious legal, ethical, and contractual considerations that need to be evaluated before using AI.
David’s key takeaways from the presentation include:
1. Importance of Company Policies for AI Use: Organizations leveraging AI tools must establish clear policies to ensure ethical and responsible use of these technologies. These policies should address key concerns, including data privacy, copyright infringement, and employment-based decisions. The policies must also define consequences for violations and include disclaimers to safeguard rights under federal labor laws, ensuring compliance and accountability.
2. Best Practices for Effective AI Integration: Successful AI implementation requires structured methodologies, including focusing on clear objectives, maintaining data quality and privacy, piloting projects, and ensuring scalability. Ethical considerations, such as addressing AI hallucinations and defining context limits, are crucial to avoid inaccuracies and ensure reliable.
3. Ethical Concerns and Mitigation Strategies for Generative AI: Generative AI poses risks like outdated or incomplete advice and vulnerabilities in data security. Mitigation strategies include regular updates of AI systems, human oversight, thorough audits of security protocols, and explicit client consent for sensitive data processing. Organizations must maintain ethical safeguards to prevent compliance errors and security breaches.
4. AI Hallucinations and Validation Measures: AI hallucinations—instances where systems generate false or fabricated information—can lead to costly errors and loss of trust. Warning signs include outputs that contradict known facts or lack logical consistency. Effective mitigation strategies include multi-layer validation, cross-referencing with trusted sources, human oversight, and routine auditing to ensure accuracy and reliability in AI-generated results.
5. Ethical Integration of AI Tools: Integrating AI ethically involves promoting diversity, prioritizing privacy, ensuring transparency, and addressing risks like bias and accountability. Organizations must develop a code of ethics, educate employees, and conduct regular reviews to ensure compliance with regulatory standards and uphold data quality and confidentiality.
