Introducing the AI Contribution Scale (ACS): A framework for AI attribution

AI’s impact on productivity, innovation, and even leadership in creative projects is unmistakable. But, its use has created an ethical dilemma: How do we appropriately credit AI's involvement?

Having devoted hundreds of hours to using AI across a multitude of activities, I'm excited to share my thoughts on attributing credit based on the level of AI's contribution. To this end, I've developed the AI Contribution Scale (ACS), a framework aimed at effectively and succinctly categorizing and communicating the role of AI in projects.

The ACS framework: Categorizing AI's involvement

The ACS framework delineates AI's involvement across six levels, from no use to basic support to complete automation. It's important to bear in mind that this framework should be applied with consideration for the specific context and objectives of your project, as the distinctions between levels may vary with the complexity and nature of the tasks involved.

Level 0: No AI use

At level 0, a human operates independently from concept to completion, managing and executing complex marketing tasks.

Marketing example: A human writes a blog post to promote an upcoming event.

Level 1: AI-assisted

At this level, AI tools support basic tasks, enhancing the efficiency and accuracy of human efforts in areas such as grammar checks, basic data analysis, or generating initial copywriting prompts, without altering the core quality of the work.

Marketing example: AI chatbots, trained on company information, answer basic customer service inquiries on a website.

Level 2: AI-enhanced

Here, AI significantly elevates output quality or efficiency through advanced data analysis, predictive insights, or generating content drafts from detailed prompts.

Marketing example: AI tools suggest improvements for headlines based on engagement data.

Level 3: AI-augmented

This level represents a collaborative effort where humans establish strategic goals and frameworks, while AI enhances content, optimizes strategies, or analyzes complex data sets.

Marketing example: Joint development of a comprehensive content calendar, with AI proposing topics and humans refining for brand voice.

Level 4: AI-directed

Here, AI leads the creation or analytical process, handling comprehensive tasks like generating entire content pieces or devising marketing strategies, with humans providing oversight, direction, and nuanced adjustments.

Marketing example: AI produces blog graphics based on marketer inputs, requiring minimal adjustments.

Level 5: AI-automated

At this stage, AI operates independently from concept to completion, autonomously managing and executing complex marketing tasks.

Marketing example: An AI system autonomously manages and optimizes PPC campaigns, adjusting bids, and testing ad copy based on real-time data.

Applying the ACS framework in your work

The ACS is more than a classification system; it's a tool for normalizing and ethically promoting AI's role in marketing. With AI's advancements, its application can enhance virtually all marketing activities. Whether explicitly recognized as AI or not, tools that enhance our work—like Grammarly for copyediting (level 1) or SEM Rush for title improvements (level 2)—have been in use for years. These practices should not only continue but also accelerate, without necessitating additional attribution.

Beyond level 2, the attribution of AI becomes imperative. At level 3, AI's significant influence on the output demands acknowledgment. Moreover, the assessment of AI's contribution should be guided by both quantitative metrics and qualitative insights, recognizing the blend of tangible outputs and intangible enhancements. Here's how to approach AI attribution across the different levels:

• Level 0: No AI use: No attribution is needed as no AI was involved in the final output.
• Level 1: AI-assisted: Attribution recognizes AI's role in facilitating tasks without changing core content quality. Citation is optional.
• Level 2: AI-enhanced: Attribution credits AI for substantive improvements in output quality or efficiency. Citation is optional.
• Level 3: AI-augmented: Attribution acknowledges a joint effort between AI and humans in creative or analytical processes. Citation is required.
• Level 4: AI-directed: Attribution underscores AI's leading role, with human strategic direction and oversight. Citation is required.
• Level 5: AI-automated: Attribution highlights AI's independent management and execution of projects from start to finish. Citation is required.

How to attribute the use of AI in public content

While there are many ways that people can cite AI when publishing content, here are a couple of suggestions:

1. Images: In both the ALT Text and below the image, if possible, include “[source with version #], date” or “Image was [ACS level | AI-directed] using [source, version #] on [date/year].”
2. Text: If AI contributed to the entire project, include an AI disclosure at the bottom of the post, such as “AI disclosure: This post was [ACS level] with [source] on [date] and its graphics were [ACS level] using [source] on [date]. I, [human contributor], contributed as the human element in these activities.
3. Text: If only specific sections of content used AI, include a footnote at the end of the sentence or section, such as “[ACS level] by [source], [date].”

The ACS is intended to be adaptable and should be periodically reviewed and adjusted to reflect technological and industry advancements. I hope this framework will spark dialogue and collaboration, promoting a unified understanding of AI's role.

AI disclosure: This post was AI-augmented with ChatGPT-4 in February 2024 and its graphics were AI-directed using DALL-E 3 on February 8, 2024 I, Natalie Lambert, contributed as the human element in these activities.

This article was originally posted on the GenEdge Resources page.

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About the author

Natalie Lambert, Founder & Managing Partner at GenEdge Consulting, is a leader driving innovation in marketing through generative AI. Her journey into the world of AI began at Google, where she initiated AI pilot projects across the organization to identify practical use cases, tools, and strategies to enhance Google's marketing efforts.?Natalie also led the content strategy at Google Cloud, held CMO positions in two successful enterprise startups, and worked at Citrix in various marketing capacities. Her career began at Forrester Research, where she advised companies on tech investments and best practices.