From: aidotengineer
The rapid development of generative AI and voice AI applications necessitates a strong focus on ethical considerations [00:08:41]. With the ease of building Minimum Viable Products (MVPs) for these applications, AI engineers and leaders bear the primary responsibility for addressing potential issues [00:09:07].
Potential for Bias and Spooky Realism
Voice AI applications inherently carry the risk of bias [00:08:54]. Specifically, voice AI apps could be:
- Biased against certain demographics particularly individuals with specific accents or dialects [00:09:00].
- “Spooky” when overly realistic [00:09:02]. While advanced text-to-speech models offer low latency and high realism, they can sometimes produce “audio hallucinations” or say “weird things” when attempting to sound too real, which can be unsettling [00:01:09], [00:09:04]. This emphasizes a preference for reliability over extreme realism in production applications [00:01:58].
The Onus on AI Engineers
In regions like the US, a lack of AI regulation places the responsibility squarely on AI engineers and industry leaders to proactively consider and address these ethical problems [00:09:07], [00:09:16]. Given that voice AI represents a new form of interaction with artificial intelligence, the approach to its development is crucial [00:09:25].
Guidelines for Responsible Development
When making tooling and infrastructure choices, AI engineers should prioritize principles that ensure AI development is:
- Accessible [00:09:42].
- Collaborative [00:09:45].
- Beneficial for everyone [00:09:46].
A key part of achieving this is by selecting tools and infrastructure that allow a diverse range of stakeholders to be involved in the development process from the outset [00:09:49]. Additionally, when new models emerge, the ability to quickly and safely integrate them is paramount [00:18:34].