Unit 5 explores the delicate dynamics of user trust in AI narrative systems, the importance of transparency, and the significant communication difficulties caused by the opacity of AI decision-making — the 'black box problem.' Using trust theory, transparency research, algorithmic accountability studies, and explainable AI literature.
Apply trust theory and transparency frameworks to human-AI storytelling relationships
Analyze mechanisms by which AI opacity creates communicative, psychological, and ethical challenges
Evaluate effectiveness of different transparency strategies for building user trust
Apply the 3D framework to detect trust dynamics in human-AI storytelling data
Design a governance architecture for transparency and trust accountability
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734.
Diakopoulos, N. (2019). Automating the news. Chapters 1–3.
Wachter, S., Mittelstadt, B., & Russell, C. (2017). Counterfactual explanations without opening the black box. Harvard Journal of Law & Technology, 31(2), 841–887.
Hancock, J. T., et al. (2020). AI-mediated communication. Journal of Computer-Mediated Communication, 25(1), 89–100.
Sunstein, C. R. (2021). Liars. Chapters 1–4.
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Comprehensive Transparency and Trust Governance Framework including Trust Architecture Analysis (500 words), Transparency System Design (700 words + visual diagram), Trust Monitoring Architecture (400 words), and CICI Transparency Appendix.