human touch can include:
When the data is source is unknown or untrusted.
When the output is for external use.
When using the output as a final deliverable.
When using the output to impersonate (e.g., a script for a sales call) rather than inform (e.g., information about a prospect to consume ahead of a cold call).
When the user is still learning prompt writing, since poor prompts lead to more effort to finesse outputs
For personal, bespoke, persuasive outputs (e.g., a marketing campaign).
When inputs needed to achieve desired output are qualitative, nuanced, implicit (not codified).
When inputs needed are recent (not yet in the dataset).
When users have experienced generative AI errors before (low-trust environments).
When stakes are high (e.g., high financial impact, broad reach, risk to end consumer, legal risk, when it’s not socially acceptable to use AI).
When humans are doing the job well today (and the tool is meant to supercharge those humans, not fill a gap in human resources).
In these situations, more deeply involving a human in the decision-making can help us achieve responsible AI by:
improving accuracy and quality – humans can catch errors, add america phone number list context or expertise, and infuse human tone;
ensuring safety – humans can mitigate ethical and legal risks;
and promoting user empowerment.
Human input spurs continuous improvement of the generative AI system and helps customers and users build trust with the technology.
Building trust takes time
Trust is the most critical aspect of generative AI, but trust is not a one-time conversation. It’s a journey that takes time to cultivate but can be quick to break. Nearly all research participants shared experiences of trusting generative AI that ultimately failed them. Customers hope for a reliable pattern of accuracy and quality over time.