Curiosity Tank

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An AI Transparency Framework?

michele ronsen

This week, Josh LaMar, co-founder of Amplinate, published a compelling article exploring a critical question: Should insights professionals disclose when they’re using AI tools in their work—and if so, how? In it, he introduces a five-level framework to help guide transparency in AI-assisted research.

Image repurposed from Josh LaMar, CEO and Co-Founder of Amplinate.

Josh’s article resonated with me deeply, especially as I’ve been conducting enterprise AI user research for the past six months.​

His framework aligns with several challenges I’ve been grappling with, including:

  • The Growing Data Literacy Gap: I’ll be speaking about this topic at QRCA annual conference this week, where I’ll explore how the insights community can—and should—play a role in bridging this divide, particularly in the context of AI’s expanding influence.

  • PII Risks in Everyday Tools: The organization I’m working with uses Google Meet, which, when both transcription and video recording are enabled, automatically embeds participants’ full names into the transcript and displays their names in the video recording. This is inconvenient from a workflow perspective because it requires additional steps to manually remove participants’ names from both the transcript and the recording to omit PII. The bigger concern, however, is the increased exposure of PII, especially as custom AI agents and shared assistants become more prevalent.

  • Lack of Transparency from Research Platforms: Many research platform sales teams, and internal procurement teams, don’t fully understand how their AI features handle data—how third-party models access, store, or even share inputs, outputs, and files. Going through RFP processes with several vendors last fall highlighted just how opaque these practices can be, with AI features often enabled by default. These issues underscore the importance of Josh’s framework. It’s not just a theoretical exercise—it’s a call to action for our industry to set clearer standards around AI use in research.

​Josh's full article and five level framework is here >>​

I encourage you to read it and think critically about the pros, cons, and other considerations. Then contemplate the questions he poses in the last section, titled Encouraging reflection:

Finally, the framework can also be used to reflect on one or many of the following areas:

  • General reflection: Does this level of AI use enhance or compromise the quality and integrity of the work?

  • Intentionality: Am I being intentional about my use of AI or am I defaulting to a certain level of use?

  • Creativity: Is AI amplifying creativity or stifling originality?

  • Learning and growth: How does the use of AI impact my skill development and craftsmanship?

  • Inclusivity and bias: Is AI helping to amplify diverse perspectives, or reinforcing harmful generalizations?

  • Accountability: Can I clearly articulate which parts of the work were AI-generated versus human-driven?

  • Human flourishing: Does the use of AI affect my personal sense of purpose, fulfillment, and meaning?

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