Principles and Challenges of Responsible Generative AI

Generative AI has the potential to revolutionize many aspects of our lives, but it also raises important ethical and social concerns. Responsible generative AI development requires a thoughtful and proactive approach to addressing these challenges.Principles of Responsible Generative AISeveral principles can guide the development and deployment of responsible generative AI:Transparency and Explainability: Generative AI models should be transparent and explainable, allowing users to understand how they generate outputs and make decisions. This transparency is crucial for building trust and ensuring that models are not used in harmful or discriminatory ways.Fairness and Non-discrimination: Generative AI models should be fair and non-discriminatory, avoiding biases that could perpetuate or exacerbate existing inequalities. This requires careful consideration of the data used to train models and the potential for bias to be amplified through the generative process.Accountability and Responsibility: Developers and deployers of generative AI should be accountable for the impacts of their models.

Autorentext

Dr. Wilfred W.K. Lin ist Mitbegründer und leitender Direktor für Datenanalyse bei HerbMiners Informatics Limited. Wilfred W.K. Lin erwarb 2000, 2002 und 2005 die Abschlüsse B.A. (1st Hon.), M.Phil. und Ph.D. an der Fakultät für Informatik der Polytechnischen Universität Hongkong. Dr. Wilfred Lin ist professionelles Mitglied von ACM, IEEE, IES und AAAI.