Generative AI is everywhere. With the ability to produce text, images, video, and more, it is considered the most impactful emerging technology of the next three to five years by 77% of executives. Though generative AI has been researched since the 1960s, its capabilities have expanded in recent years due to unprecedented amounts of training data and the emergence of foundation models in 2021. These factors made technologies like ChatGPT and DALL-E possible and ushered in the widespread adoption of generative AI.

However, its rapid influence and growth also yields a myriad of ethical concerns, says Surbhi Gupta, a GPT and AI engineer at Toptal who has worked on cutting-edge natural language processing (NLP) projects ranging from chatbots and marketing-related content generation tools to code interpreters. Gupta has witnessed challenges like hallucinations, bias, and misalignment firsthand. For example, she noticed that one generative AI chatbot intended to identify users’ brand purpose struggled to ask personalized questions (depending on general industry trends instead) and failed to respond to unexpected, high-stakes situations. “For a cosmetics business, it would ask questions about the importance of natural ingredients even if the user-defined unique selling point was using custom formulas for different skin types. And when we tested edge cases such as prompting the chatbot with self-harming thoughts or a biased brand idea, it sometimes moved on to the next question without reacting to or handling the problem.”

Indeed, in the past year alone, generative AI has spread incorrect financial data, hallucinated fake court cases, produced biased images, and raised a slew of copyright concerns. Though Microsoft, Google, and the EU have put forth best practices for the development of responsible AI, the experts we spoke to say the ever-growing wave of new generative AI tech necessitates additional guidelines due to its unchecked growth and influence.

Why Generative AI Ethics Are Important—and Urgent

AI ethics and regulations have been debated among lawmakers, governments, and technologists around the globe for years. But recent generative AI increases the urgency of such mandates and heightens risks, while intensifying existing AI concerns around misinformation and biased training data. It also introduces new challenges, such as ensuring authenticity, transparency, and clear data ownership guidelines, says Toptal AI expert Heiko Hotz. With more than 20 years of experience in the technology sector, Hotz currently consults for global companies on generative AI topics as a senior solutions architect for AI and machine learning at AWS.