privacy literacy

Submitted by Sarah Hartman-Caverly on December 20th, 2023
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Short Description: 

The Hidden Layer Workshop 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. In the centerpiece activity, participants engage in a hidden layer simulation to develop a conceptual understanding of the algorithms in the neural networks underlying LLMs and their 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.

The workshop is designed for a 60-minute session, but can be extended to fill the time available.
Includes workshop guide, presentation slides, learning activities, and assessment instrument.

Attachments: 
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HiddenLayer_LessonPlan_CCBYSA_HartmanCaverly_2023.pdfdisplayed 1435 times117.63 KB
Relevant links: 
[workshop guide] Hidden Layer: Intellectual Privacy and Generative AI[slides] Hidden Layer: Intellectual Privacy and Generative AI[activity] AI Playground[activity] Perplexity.ai[activity] ChatGPT[activity] Google Bard[activity] Microsoft Copilot[activity] Hidden Layer Simulation[activity] How AI Reduces the World to Stereotypes - Rest of World[assessment] Hidden Layer Workshop Reflection
Learning Outcomes: 

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).

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

Discipline: 
Library and Information Science

Information Literacy concepts:

Individual or Group:

Ability Level:

Tags:

Suggested Citation: 
Hartman-Caverly, Sarah. "Hidden Layer: Intellectual Privacy and Generative AI." CORA (Community of Online Research Assignments), 2023. https://projectcora.org/assignment/hidden-layer-intellectual-privacy-and-generative-ai.
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All assignments contributed to this site are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike United States License
Submitted by Sarah Hartman-Caverly on August 4th, 2023
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Short Description: 

This workshop engages participants in exploring corporate data collection, personal profiling, deceptive design, and data brokerage practices. Workshop content is contextualized with the theoretical frameworks of panoptic sort (Gandy), surveillance capitalism (Zuboff), and the four regulators (Lessig) and presented through a privacy and business ethics lens. Participants will learn how companies make money from data collection practices; explore how interface design can influence our choices and behaviors; and discuss business ethics regarding privacy and big data.
The workshop is designed for 75-minute class sessions, but can be compressed into 60-minute sessions.
Includes workshop guide, presentation slides, learning activities, and assessment instrument.

Attachments: 
AttachmentSize
DarkPatternsWorkshopLessonPlan_HartmanCaverly_CCBYNCSA.pdfdisplayed 1040 times84.44 KB
Relevant links: 
[workshop guide] Dark Patterns: Surveillance Capitalism and Business Ethics[slides] Dark Patterns: Surveillance Capitalism and Business Ethics
Learning Outcomes: 
  1. Learn how companies make money from data collection practices
  2. Explore how interface design can influence our choices and behaviors
  3. Discuss business ethics regarding privacy and big data.
Discipline: 
BusinessLibrary and Information Science

Individual or Group:

Ability Level:

Tags:

Course Context (e.g. how it was implemented or integrated): 
Additional Instructor Resources (e.g. in-class activities, worksheets, scaffolding applications, supplemental modules, further readings, etc.): 
Potential Pitfalls and Teaching Tips: 
Suggested Citation: 
Hartman-Caverly, Sarah. "Dark Patterns: Surveillance Capitalism and Business Ethics." CORA (Community of Online Research Assignments), 2023. https://projectcora.org/assignment/dark-patterns-surveillance-capitalism-and-business-ethics.
Copyright icon to go along with copyright rules.
All assignments contributed to this site are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike United States License
Submitted by Sarah Hartman-Caverly on August 4th, 2023
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Short Description: 

This sex-positive privacy literacy workshop engages participants in exploring how sex tech impacts intimate privacy and intimate relationships. Workshop content is contextualized with the theoretical frameworks of artificial intimacies (Brooks) and consentful tech (The Consentful Tech Project) and the concept of intimate privacy (Citron) and presented through a privacy literacy lens. Participants will identify artificial intimacies in order to assess real-world examples and their impact upon intimate privacy; evaluate the privacy of digital bodies under conditions of data promiscuity using a consentful tech framework; and understand intimate privacy and the impact of technology on intimate relationships and wellbeing.

The workshop is designed for a 60-minute session, but can be extended to fill the time available.
Includes workshop guide, presentation slides, learning activities, inclusive pedagogy tool, and assessment instrument.

Attachments: 
AttachmentSize
PrivateBits_LessonPlan_CCBYSA_HartmanCaverly_Chisholm_2023.pdfdisplayed 755 times105.27 KB
PrivateBits_PrintableHandout_CCBYSA_HartmanCaverly_Chisholm_2023.pdfdisplayed 677 times7.03 MB
PrivateBits_ASKMEconversationhearts_HartmanCaverly_Chisholm_2023.pdfdisplayed 707 times2.38 MB
PrivateBits_Fillable_DigitalBodiesWorksheet_CCBYSA_HartmanCaverly_Chisholm_2023.pdfdisplayed 681 times4.73 MB
PrivateBits_Fillable_ConsentfulTechWorksheet_CCBYSA_HartmanCaverly_Chisholm_2023.pdfdisplayed 660 times61.94 KB
Relevant links: 
[workshop guide] Private Bits: Privacy, Intimacy, and Consent[slides] Private Bits: Privacy, Intimacy, and Consent
Learning Outcomes: 
  1. Identify artificial intimacies in order to assess real-world examples and their impact upon intimate privacy
  2. Evaluate the privacy of digital bodies under conditions of data promiscuity using a consentful tech framework
  3. Understand intimate privacy and the impact of technology on intimate relationships and wellbeing.
Discipline: 
Library and Information ScienceWomen's Studies

Information Literacy concepts:

Individual or Group:

Ability Level:

Tags:

Course Context (e.g. how it was implemented or integrated): 
Additional Instructor Resources (e.g. in-class activities, worksheets, scaffolding applications, supplemental modules, further readings, etc.): 
Potential Pitfalls and Teaching Tips: 
Collaborators: 
Alexandria Chisholm
Suggested Citation: 
Hartman-Caverly, Sarah. "Private Bits: Privacy, Intimacy, and Consent." CORA (Community of Online Research Assignments), 2023. https://projectcora.org/assignment/private-bits-privacy-intimacy-and-consent.
Copyright icon to go along with copyright rules.
All assignments contributed to this site are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike United States License