A major concern with generative AI is that biases in the training data get amplified in the synthesized output. For example, generative design models trained only on stock images of western culture could completely fail to represent global diversity. The need for high-quality training data that spans different demographics, cultures, and perspectives is paramount. However, assembling such diverse data sets poses challenges around ethics and representation. Even if diverse images are utilized, generative models can still exhibit biased behavior due to technical limitations. Overall, training data bias remains a key challenge for fair, inclusive generative AI design systems. Ongoing research into mitigating algorithmic bias and collecting ethical training data will be crucial. The benefits of generative design must not come at the expense of marginalized communities. Maintaining rigorous testing and auditing processes for generative models is essential.

Generative AI In Design Market: https://marketresearch.biz/report/generative-ai-in-design-market/