Hidden Layer: Intellectual Privacy and Generative AI
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Summary
Hidden Layer introduces key generative AI (genAI) concepts through a privacy lens. Participants probe the possibilities and limitations of genAI while considering implications for intellectual privacy, intellectual property, data sovereignty, and human agency. An original PROMPT Design Framework and worksheet guide participants through the iterative process of prompting generative AI to optimize output by specifying Persona, Requirements, Organization, Medium, Purpose, and Tone. In the centerpiece activity, participants engage in a hidden layer simulation to develop a conceptual understanding of the algorithms underlying LLMs and implications for machine bias and AI hallucination. Drawing on Richards’s theory of intellectual privacy (2015) and the movement for data sovereignty, and introducing an original framework for the ethical evaluation of AI, Hidden Layer prepares participants to be critical users of genAI and synthetic media.
Authors
Sarah Hartman-Caverly
Learning Outcomes
The purpose of this workshop is to reveal that 1) generative AI models are built on math, 2) the training data sets for large language models rely on web crawling lots of user-generated content, with implications for intellectual and personal privacy and intellectual property, and 3) because math doesn’t know meaning, AI output is subject to numerous forms of bias and error. These outcomes rely on a conceptual understanding of AI, but they don’t require a literal understanding of the underlying mathematics, programming, or particulars of different AI models.
Facilitator learning objectives
During this workshop, participants will
Apply prompt engineering techniques to elicit information from text-to-text generative AI (genAI) platforms
Appreciate a range of intellectual privacy implications posed by genAI, including:
personal data;
intellectual property (copyright, patent, proprietary and sensitive data);
AI alignment (social bias, content moderation, AI guardrails, censorship, prompt injection);
synthetic media;
AI hallucination and mis/dis/malinformation; and
data sovereignty and data colonialism.
Engage in a simulation to develop a conceptual understanding of how the hidden layer in the neural networks underpinning large language models works
Synthesize their knowledge of genAI intellectual privacy considerations to analyze an ethical case study using the Agent-Impact Matrix for Artificial Intelligence (AIM4AI).
The facilitator learning objectives can be used to convey the purpose of the workshop to peer instructors, event co-sponsors, and other collaborators; to adapt workshop activities; and to discuss workshop content in scholarly and professional communication.
Participant learning outcomes
During this workshop, participants will
Interact with genAI to explore its possibilities and limitations
Discuss the intellectual privacy implications of genAI, including intellectual property considerations
Evaluate the ethics of genAI for its impact on human agency
The participant learning outcomes describe the workshop for the intended audience (in this case, typically undergraduate students), and can be used in promotional materials.
Audience
Undergraduates
Curricular Context
Hidden Layer is designed as a one-hour introductory standalone or co-curricular workshop. Participants are not expected to have any prior knowledge on the topic of genAI or intellectual privacy; foundational concepts are covered in the workshop material. Hidden Layer can be delivered face-to-face, in a synchronous virtual format, or hybrid (synchronous face-to-face and online); co-teaching is recommended for hybrid delivery so that one instructor can facilitate in-person engagement and the other can facilitate online engagement. Hidden Layer is also readily adapted for asynchronous delivery (for example, by recording micro-lecture segments). Participants should have access to a computer or tablet to participate in learning activities.
Verbally describe visual elements that convey information, such as the Hidden Layer Simulation and Agent-Impact Matrix for Artificial Intelligence (AIM4AI).
Offer the ‘fillable’ version of the PROMPT Design worksheet to students using screen readers. It was prepared using Adobe’s accessibility checker and features.
Framework: Agent-Impact Matrix for Artificial Intelligence (AIM4AI).
Multiple versions of AIM4AI appear throughout the workshop slides to demonstrate ethical reasoning about AI and its impact on human agency in a variety of contexts, including machine learning, intellectual property, and data sovereignty. A summary version of AIM4AI positions all of the case studies featured in the workshop on the matrix. A modified version of the summary AIM4AI matrix optimized for use on a virtual whiteboard platform, and a text-only version for use with screen readers, are also provided.
This is a prompt engineering exercise in which participants will use text-to-text genAI platforms to elicit information about the privacy implications of AI.
Introduce the research question: What are the privacy implications of AI?
Direct participants to
Think: Brainstorm prompts on your own
Pair: Discuss prompts with a partner, and select 1-2 to prompt engineer and use.
Provide participants with prompt tips using the PROMPT Design Framework to optimize AI output by specifying the Persona, Requirements, Organization, Medium, Purpose, and Tone.
Transition: What is generative AI and how does it work?
[Lecture] Introduction to generative AI (10 mins.)
Note: For details, refer to the content and speaker notes in the workshop slides!
Explain ChatGPT, its relationship to large language models, and how they are trained.
Define neural networks and deep learning.
Introduce the Agent-Impact Matrix for Artificial Intelligence (AIM4AI) and the intellectual privacy implications of genAI:
One axis of AIM4AI looks at agency on a spectrum from machine autonomy to human autonomy. Compared to other forms of model training, like supervised learning with trained data sets and target output values, the deep learning of neural networks is characteristic of machine autonomy.
The other axis of AIM4AI considers impact on the spectrum from input to output. Input broadly refers to ways in which these models are trained or prompted, including deep learning, while output refers to the ways these models are used. As you learn about and interact with AI, think about ways that it can be used to enhance human agency by augmenting our intellectual activities, rather than progressing solely as an independent form of machine intelligence.
Privacy is a critical element of human agency. The Six Private I’s framework demonstrates six ways that privacy benefits us in everyday life, including by protecting our sense of identity, safeguarding our intellect and the activities of our mind, maintaining the contextual integrity of our personal information flows and our bodily integrity through spatial privacy and medical autonomy, securing the intimacy of our closest personal relationships, and preserving our freedom of association or interaction, as well as our ability to voluntarily withdraw into seclusion or isolation.
This workshop will focus specifically on intellectual privacy, what Neil Richards calls “a zone of protection that guards our ability to make up our minds freely” (2015, p. 95). Intellectual privacy also protects your rights to your intellectual property, including any creative works that are eligible for copyright protection, or useful inventions that are eligible for patent protection.
Take a deeper dive into ChatGPT’s history with a focus on training data.
Connect this back to intellectual privacy, including the use of personal data and creative expressions in model training, the identity implications of AI output and hallucination, and intellectual property considerations.
Revisit AIM4AI by analyzing some case studies related to the impact of AI input and output on intellectual privacy.
Note: It is useful to demonstrate how the same case can be placed in a different quadrant on the matrix depending on whether it is considered from the perspective of input vs. output (impact) or machine autonomy vs. human autonomy (agent). In the example slide (Slide 18), the same article from Futurism about how leaky prompts from Amazon employees probably divulged sensitive company information that appeared in ChatGPT output is used. It is analyzed as an example of both human autonomy in the input domain resulting in privacy harms, and of human autonomy in the output frame by applying ChatGPT to coding tasks to augment intellect.
It is recommended to conclude the AIM4AI analysis with an example of AI hallucination to facilitate the transition to the Hidden Layer Simulation.
Transition: If genAI applications like ChatGPT have access to so much information, why do they hallucinate?
For this simulation, our neural network has one input node, three parallel hidden layer nodes, and one output node.
You will answer three questions to perform the analysis of the three hidden layer nodes, and answer a fourth and final question to predict the next token in the sequence as the output node.
Note: The Hidden Layer Simulation uses a source text written in Central Atlas Tamazight using the Tifinagh script, an indigenous language of Morocco. This is an intentional choice to surface the issues of data sovereignty, data colonialism, and the language gap of large language models along with their implications for intellectual privacy. Tamazight is of personal significance to Hidden Layer Simulation creator Sarah Hartman-Caverly. An alternate language, real or fictitious, can be used, as long as it is likely to be unfamiliar to participants and the source text contains the same features that are used in the simulation (verse with repeated words).
Transition:
This exercise simulates the activity of the hidden layer in a neural network to give you a conceptual understanding of how machine learning works in generative AI like ChatGPT.
You were able to predict the next token in a text sequence, despite not being able to comprehend or interpret the input text. (In this case, the input text is an AI-generated translation of a nursery rhyme in Central Atlas Tamazight, an indigenous language of Morocco!)
Similarly, AI does not interpret, understand, or create meaning - it is only performing sophisticated mathematical functions to predict the most likely desired output. It isn't magical - it's mathemagical.
[Lecture] Math and Meaning (6 mins.)
Revisit earlier concepts about deep learning in large language models by emphasizing that they are manifestations of statistical computations over large bodies of text. These include algorithms for unsupervised machine learning, like data clustering.
Note: In the workshop slides, a data flow diagram for the transformer – the key component of GPT (generative pre-trained transformer) AI models – depicts some of the mathematical formulae that are programmed into this neural network architecture. The point is not to understand the math so much as to recognize that the math is there!
Participants may ask why generative AI is so bad at solving basic math problems if its underlying models are math-based! Some explanations include the unstructured nature of many quantitative reasoning problems (Garisto, 2022), prompt design, and “drift” which describes tradeoffs in optimizing for model performance on other tasks (Zombrun, 2023).
Emphasize that math does not know its meaning.
Note: The workshop slides explore the process of generating the Central Atlas Tamazight translation of the nursery rhyme, Twinkle Twinkle Little Star, using Perplexity.ai. The purpose is to surface issues of data sovereignty, data colonialism, and the language gap of large language models along with their implications for intellectual privacy.
Introduce the concepts of data colonialism, data sovereignty, and the language gap.
Revisit AIM4AI by analyzing case studies related to data sovereignty, data colonialism, and the language gap as they relate to intellectual privacy.
Facilitate a brief discussion about their observations of AI bias based on the galleries.
Transition: Bias isn’t only an artifact of the hidden layer of AI - it is also present in the ‘human layer.’ The decisions we make, from what data sets to use for model training, to how the data is labeled, to the selection and tuning of model parameters, to the evaluation of AI output for reinforcement learning, to the implementation of AI guardrails and other alignment, safety, and content moderation strategies can all introduce bias to generative AI.
[Activity] AIM4AI Case Study Analysis (12 mins.)
Reintroduce AIM4AI in the context of Neil Richard’s definition of intellectual privacy:
“a zone of protection that guards our ability to make up our minds freely” and “protection from surveillance or unwanted interference by others when we are engaged in the processes of generating ideas and forming beliefs” (2015, p. 5, 95).
Provide a curated collection of case studies in categories related to intellectual privacy like Alignment, Hallucination, Data Sovereignty, Intellectual Property, and Synthetic Media.
Direct participants to select and skim a case study and consider the following questions:
Impact Dimension
Does the case address input or output from an AI system?
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
Who is doing the input - humans or machines?
Who is impacted by the output? How is the output evaluated for fairness, accountability, and transparency?
How transparent is the interaction? Does it enhance or undermine human agency?
Provide an online posting board (ex. Padlet, Markup.io) to preserve participant anonymity.
Facilitate a large-group discussion based on the impact of AI on intellectual privacy and human agency based on participant responses.
Workshop review and closing (3 min.)
Assessment
This workshop is assessed with a brief web form presenting three Likert scale questions that evaluate the participant learning outcomes, and a free-text response:
This workshop taught me something new about generative AI, including its possibilities and limitations. [Likert scale 1 = strongly disagree 5 = strongly agree]
This workshop gave me a new way to think about intellectual privacy, including how it is impacted by generative AI. [Likert scale 1 = strongly disagree 5 = strongly agree]
This workshop gave me a new way to think about the ethics of generative AI, including how it can impact human agency and augment human intellect. [Likert scale 1 = strongly disagree 5 = strongly agree]
My top takeawayorsuggestion for improvement is: [free-text response]
Adaptability
Discrete learning activities from this lesson, such as Think-Pair-Ask AI-Share, the Hidden Layer Simulation, and exploring AI stereotypes with Rest of World’s Midjourney image gallery are highly modular and easily adapted into other lesson plans. For example, I integrated the Think-Pair-Ask AI-Share, exploration of AI stereotypes image gallery, and Hidden Layer Simulation as standalone learning activities in course-related instruction sessions for digital marketing, hospitality management, entrepreneurship and innovation, and first-year seminar courses.
I piloted the Hidden Layer Workshop in an honors section of a second-year undergraduate writing in the disciplines course (Penn State’s ENGL 202: Effective Writing) in which students were exploring the use of generative AI for a variety of writing tasks. The Hidden Layer Workshop is also readily adapted as a course-related instruction session for computer and information science or as an outreach event for student organizations related to technology.
Reflection
AI is extraordinarily complex and constantly evolving, making it a challenging topic to teach. I sometimes find myself overwhelmed and reviewing introductory materials to reorient myself. I strive to balance the self-efficacy needed to deliver this kind of learning experience, with the intellectual humility that enables me to process the inevitable experience of making a mistake or knowing less about a topic than my participants. I try to maintain a posture of co-learning with my participants; it is good to model intellectual humility, open-mindedness, and curiosity to students!
The real purpose of this workshop is to reveal that 1) generative AI models are built on math, 2) the training data sets for large language models rely on web crawling lots of user-generated content, with implications for intellectual and personal privacy and intellectual property, and 3) because math doesn’t know meaning, AI output is subject to numerous forms of bias and error. These outcomes rely on a conceptual understanding of AI, but they don’t require a literal understanding of the underlying mathematics, programming, or particulars of different AI models. Hidden Layer is a content-rich workshop, but the lesson plan should only guide the learning experience, not dictate it: if participants are really engaging with a concept or activity, or want to explore in a different direction, you should not feel pressured to rush to cover everything!
Results from the workshop reflection assessment indicate that the majority of participants learned something new about generative AI, including its possibilities and limitations (87.5%, n=24); learned a new way to think about intellectual privacy, including how it is impacted by generative AI (91.7%, n=24); and learned a new way to think about AI ethics, including its impact on human agency (87.5%, n=24). In the free-text comments, participants expressed appreciation for the interactive design of the workshop, found the exploration of AI bias enlightening, and specifically valued the Hidden Layer Simulation:
“I really liked the hidden layer simulation, it helped me to better understand how AI works. Even though I had seen similar diagrams discussing hidden layers, I had never really understood what they meant.” (quoted anonymously with permission)
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