On Artificial Intelligence and Authenticity | DavidJoyner.net

🌈 Abstract

The article discusses the implications of using generative AI tools, particularly in the context of social media interactions and academic work. It explores the distinction between content generated by a human versus content selected from a pre-generated menu of options, and how this affects the perceived authenticity and value of the content.

🙋 Q&A

[01] The Unsettling Nature of AI-Generated Responses

1. What was the author's reaction to the AI-generated responses suggested by Facebook for their Facebook post? The author found the AI-generated responses to their Facebook post unsettling, not because the AI had become so advanced, but because the pattern of offering these pre-generated responses as a menu of reactions represents a misunderstanding of the function of these social interactions.

2. What is the key difference the author identifies between a human-generated response and an AI-generated response? The author argues that there is a fundamental difference between knowing someone typed out a response themselves versus selecting a pre-generated response, even if the actual text and emojis are identical. The process of generating a response oneself feels to represent something stronger than merely selecting a pre-generated response.

3. How does the author connect this to the distinction between recognition and recall? The author draws a parallel between the difference in human-generated vs. AI-generated responses and the distinction between recognition and recall. Generating a response oneself represents a stronger understanding, similar to the ability to recall information rather than just recognize it when prompted.

[02] The Value of Authenticity

1. What is the author's key point about the value of authenticity in different contexts? The author argues that the value of an artifact (e.g., a gift, a piece of code, a personal story) depends on whether its authenticity or just its existence is important. In some contexts, like the workplace, the artifact's functionality or inherent value is what matters. In other contexts, like education or personal relationships, the authenticity of the process that generated the artifact is crucial.

2. How does the author illustrate this point with the gift-giving analogy? The author uses the example of a child's birthday gift to illustrate the difference. If the gift is selected by an assistant rather than the parent, the child may be disappointed, even if the gift itself is identical. The gift's value is tied to the thought and effort behind it, not just its inherent value.

3. What are the implications of this view for both users and creators of generative AI tools? For users, the author suggests that they need to consider the trade-off between authenticity and the value that generative AI tools provide. For creators of these tools, they need to consider the extent to which their tools are helping users circumvent authenticity, and whether the tools are being used in contexts where authenticity is crucial to the value of the artifact.

[03] Applying the Authenticity Principle

1. How does the author apply the authenticity principle to the example of AI-generated content in the classroom versus the workplace? In the classroom, the author argues that the work generated by students is valuable because it represents their knowledge and understanding of the content. In the workplace, the value of the work is more about its functionality and ability to accomplish a task, rather than what it represents about the worker.

2. What is the author's perspective on using Jetpack's built-in AI summarizer to shorten the original blog post? The author suggests that using the AI summarizer raises questions about whether the difference in impact between the original post and the AI-shortened version is due to the different content or the process that generated it. The author also questions whether it matters that the reader knows the input was a full-length post by the author rather than a short prompt.

3. What is the author's overall conclusion about the use of generative AI tools? The author concludes that generative AI can be a useful tool for creating artifacts whose entire value is just the artifact itself, but if authenticity matters, generative AI is a poor fit. Users and tool creators need to carefully consider the trade-off between ease of use and the potential loss of authenticity when using these tools.