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Hidden Layer: Intellectual Privacy and Generative AI

The Hidden Layer Workshop explores generative AI and its implications for intellectual privacy, intellectual property, and human agency.

Welcome to the Hidden Layer Workshop!

In this workshop we will:

  • Interact with generative AI to explore its possibilities and limitations
  • Discuss the intellectual privacy implications of generative AI, including intellectual property considerations
  • Evaluate generative AI for its impact on human agency

Stylized photograph of a circuit board in aquamarine against a black background.

Image adapted from "teal LED panel" by Adi Goldstein via unsplash.com.

  This workshop is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Hartman-Caverly, S. (2023). Hidden layer: Intellectual privacy and generative AI. https://guides.libraries.psu.edu/berks/AI

Prompt Design: Think-Pair-Ask AI-Share

Research question: What are the privacy implications of artificial intelligence?

  1. Think: On your own, draft some prompts for generative AI (like ChatGPT).
  2. Pair: With a partner, discuss your draft prompts. Develop 1-2 to use.
  3. Ask AI: Input your prompt(s) into one of the generative AI platforms below.
  4. Share: What worked well? What did you need to tweak? Did you get the information you were looking for? Does it seem accurate? How do you know? What did you learn from AI? What did AI learn from you?

Inspired by "Think-Pair-Share with ChatGPT" proposed by Sarah Dillard.

CLEAR Framework for Prompt Engineering based on (Lo, 2023) prompting Concise, Logical, Explicit, Adaptive, and Reflective GenAI prompts.

Made with Padlet

The Hidden Layer

Open the Hidden Layer Simulation to explore the deep learning work of nodes in a large language model neural network.

Hidden layer architecture of one input node, three parallel hidden layer nodes, and one output node.

From artificial intelligence to augmented intellect: AIM4AI Analysis

Select a case study and consider the following questions:

Impact Dimension

  1. Does the case address input or output from an AI system?
  2. At what point does human-machine interaction occur in your case (ex. training during machine learning, fine-tuning, or in response to output)?

Agency Dimension

  1. Who is doing the input - humans or machines?
  2. Who is impacted by the output? How is the output evaluated for fairness, accountability, and transparency?
  3. How transparent is the interaction? Does it enhance or undermine human agency?

Map your case onto the Agency-Interaction Matrix for AI (AIM4AI) (via Jamboard):

Cases for AIM4AI Analysis

Alignment

Hallucination

Data Sovereignty

Intellectual Property

Synthetic Media

Agency-Impact Matrix for Artifical Intelligence (AIM4AI) matrix showing an x axis of increasing impact (input-to-output) and a y axis with increasing agency (machine autonomy-to-human autonomy). In the top-left quadrant is supervised learning characterized by higher human autonomy in AI input; on the bottom-left quadrant is deep learning characterized by higher machine autonomy on input; on the bottom-right quadrant is artificial intelligence characterized by higher machine autonomy on output; and on the top-right quadrant is augmented intellect characterized by higher human agency on output.

Explore further

Prompt Design

Facilitator

Profile Photo
Sarah Hartman-Caverly
smh767@psu.edu
Contact:
Penn State Berks, Thun 107
(610-396)x6243
Website
Social: Twitter Page
Subjects: Campus: Berks