DDA500: Creative Practice with AI — Bibliography & Reading List
A curated bibliography of foundational texts, critical theory monographs, seminal research papers, and contemporary essays examining artificial intelligence as a creative medium, technical system, and cultural force.
1. Critical Theory, Authorship & Philosophy of Creative AI
Books
- Benjamin, Ruha. Race After Technology: Abolitionist Tools for the New Jim Code. Polity, 2019.
Investigates how automated systems, predictive algorithms, and machine learning models encode and reinforce racial hierarchies and social discrimination under the guise of technological neutrality. - Boden, Margaret A. The Creative Mind: Myths and Mechanisms. Routledge, 2nd ed., 2004.
Foundational cognitive science exploration classifying creativity into combinational, exploratory, and transformational paradigms. - Bridle, James. Ways of Being: Animals, Plants, Machines: The Search for a Planetary Intelligence. Farrar, Straus and Giroux, 2022.
Expands definitions of intelligence beyond anthropocentric computational models to embrace non-human, ecological, and hybrid machine intelligences. - Crawford, Kate. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press, 2021.
Unpacks AI not as an ethereal computational cloud, but as a material industry extractive of natural energy resources, labor, and massive unconsented datasets. - Hayles, N. Katherine. How We Became Posthuman: Virtual Bodies in Cybernetics, Literature, and Informatics. University of Chicago Press, 1999.
Traces the historical separation of information from embodiment across cybernetics, cognitive science, and digital culture. - Manovich, Lev. Cultural Analytics. The MIT Press, 2020.
Examines the computational analysis of visual culture, big visual data, and digital aesthetic trends at macroscopic scale. - Narayanan, Arvind, and Sayash Kapoor. AI Snake Oil: What Computers Can Do, What They Can’t, and How to Tell the Difference. Princeton University Press, 2024. (Wiki Source Card)
Demystifies generative and predictive AI systems, breaking down statistical limits, capability boundaries, and the socioeconomic mechanisms of artificial intelligence hype. - Pasquinelli, Matteo. The Eye of the Master: A Social History of Artificial Intelligence. Verso, 2023.
Reframes the genealogy of machine learning as the mechanization and extraction of human social labor and collective mental division. - Zylinska, Joanna. AI Art: Machine Visions and Warped Dreams. Open Humanities Press, 2020.
Critically interrogates human and non-human agency, algorithmic curation, and creative authorship in machine vision.
Articles & Essays
- Agüera y Arcas, Blaise. “Do Large Language Models Understand Us?” Noēma Magazine, Dec 2021.
Argues that large neural representations construct genuine semantic meaning and pragmatic world models through dialogue. - Chiang, Ted. “ChatGPT Is a Blurry JPEG of the Web.” The New Yorker, Feb 9, 2023.
Theoretical critique conceptualizing LLMs as lossy compression artifacts that repackage information while lacking intentional understanding. - Chiang, Ted. “Why A.I. Isn’t Going to Make Art.” The New Yorker, Aug 31, 2024. (Wiki Source Card)
Argues that art requires thousands of human choices, emotional vulnerability, and communicative intent that prompt-driven generation bypasses. - Narayanan, Arvind, and Sayash Kapoor. “AI as Normal Technology.” AI Snake Oil / Knight First Amendment Institute, 2024. (Wiki Source Card)
Argues against existential exceptionalism, establishing that AI should be governed and understood as standard general-purpose computing technology with mundane institutional trade-offs. - Pasquinelli, Matteo, and Vladan Joler. “The Nooscope Manifested: AI as Instrument of Knowledge Extractivism.” KIM HfG Karlsruhe, 2020.
A visual essay and diagrammatic map dissecting training sets, statistical classifiers, and algorithmic bias. - Steyerl, Hito. “Mean Images.” New Left Review, 140/141, 2023.
Analyzes diffusion models as statistical aggregations (‘means’) that average cultural memory and obscure aesthetic anomalies.
2. Technical Foundations, Latent Space & Generative Media
Books
- Foster, David. Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play. O’Reilly Media, 2nd ed., 2023.
Hands-on technical guide to VAEs, GANs, Diffusion Models, Transformers, and multimodal architectures using Python and PyTorch. - Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016.
The canonical textbook for mathematical and algorithmic foundations of neural networks and optimization. - Mitchell, Melanie. Artificial Intelligence: A Guide for Thinking Humans. Farrar, Straus and Giroux, 2019.
Clear, accessible examination of neural network architectures, computer vision, reinforcement learning, and the common pitfalls of AI anthropomorphism.
Articles, Seminal Papers & Visual Culture
- Carter, Shan, and Michael Nielsen. “Using Artificial Intelligence to Augment Human Intelligence.” Distill, 2017.
Interactive visual essay exploring generative models as cognitive interfaces for thought and creative exploration. - Olah, Chris, Alexander Mordvintsev, and Ludwig Schubert. “Feature Visualization.” Distill, 2017.
Interactive methods for inspecting what hidden layers inside deep convolutional and transformer vision models actually detect. - Radford, Alec, et al. “Learning Transferable Visual Models From Natural Language Supervision (CLIP).” ICML, 2021.
The foundational paper bridging natural language and image semantics, enabling zero-shot classification and text-conditioned generative guidance. - Rombach, Robin, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. “High-Resolution Image Synthesis with Latent Diffusion Models.” CVPR, 2022.
Introduces Latent Diffusion Models (Stable Diffusion), moving diffusion processes from pixel space to compressed latent representations. - Vaswani, Ashish, et al. “Attention Is All You Need.” NeurIPS, 2017.
Introduces the self-attention Transformer architecture that powers modern Large Language Models and Vision Transformers. - Vincent, James, and Visual Media Critics. “The ‘Balenciaga Pope’: Diffusion Models, Viral Verisimilitude, and Synthetic Realism.” The Verge / Digital Media Culture, 2023. (Wiki Source Card)
Case study analyzing the March 2023 viral AI image of Pope Francis in a white puffer coat as a global watershed moment for synthetic photographic realism and perceptual truth.
3. Autonomous Agents, Prompting, Code & Orchestration
Books & Reference Guides
- Mollick, Ethan. Co-Intelligence: Living and Working with AI. Portfolio / Penguin Random House, 2024. (Wiki Source Card)
Essential guide to collaborating with AI in creative and professional workflows, introducing the ‘jagged frontier’ of model capability and Centaur vs. Cyborg modes of human-machine co-creation. - Russell, Stuart, and Peter Norvig. Artificial Intelligence: A Modern Approach. Pearson, 4th ed., 2020.
Comprehensive reference on rational agent design, state-space search, decision theory, and multi-agent coordination. - Wooldridge, Michael. An Introduction to MultiAgent Systems. John Wiley & Sons, 2nd ed., 2009.
Game-theoretic foundations, emergent consensus, communication languages, and auction mechanisms in autonomous multi-agent systems.
Articles & Papers
- Clark, Jack. “You Will Know It By Its Signs.” Import AI, 2026. (Wiki Source Card)
Reflections on the societal and infrastructure phase transitions marking the transition to background autonomous agent workflows. - Narreddi, Surya. “Training AI to Paint with Code: Reinforcement Learning in Visual Action Spaces.” 2026. (Wiki Source Card)
Formulates creative mark-making as executable vector code generation (SVG) optimized via visual reward functions. - Park, Joon Sung, et al. “Generative Agents: Interactive Simulacra of Human Behavior.” ACM UIST, 2023.
Demonstrates computational agents exhibiting emergent social behaviors, memory reflection, and coordinated events in an interactive virtual town. - Patel, Dwarkesh, and Oak Hu. “The Rise and Fall of Agent Civilizations.” Dwarkesh Podcast / Essay Series, 2026. (Wiki Source Card)
Macroeconomic analysis of emergent credit markets, task specialization, and institutional dynamics among autonomous LLM agent ecosystems. - Wang, Guanzhi, et al. “Voyager: An Open-Ended Embodied Agent with Large Language Models.” arXiv:2305.16291, 2023.
Lifelong learning agent utilizing LLM code generation, an iterative skill library, and automatic curriculum prompting.
4. Ethics, Copyright, Data Extraction, Algorethics & Environmental Infrastructure
Books
- Noble, Safiya Umoja. Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press, 2018.
Critique of commercial search index biases and data monopolization. - O’Neil, Cathy. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown, 2016.
Examines opaque, unregulated mathematical scoring models that amplify socio-economic inequality. - Zuboff, Shoshana. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs, 2019.
Traces the unilateral commodification of behavioral data into predictive behavioral futures.
Articles, Legal Reviews & Ethical Frameworks
- Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜.” ACM FAccT, 2021.
Seminal critique of the environmental, social, and epistemic hazards of training ever-larger language models on uncurated internet data. - Francis, Pope. “Address of His Holiness Pope Francis to the G7 Summit Session on Artificial Intelligence.” The Holy See, Apulia, Italy, June 14, 2024. (Wiki Source Card)
Historic papal address urging world leaders to preserve human decision-making, protect human dignity, and implement ‘algorethics’ (algor-etica) across algorithmic systems. - Hao, Karen. “Empire of AI: Water Footprint and Data Center Extraction.” 2025. (Wiki Source Card)
Investigative documentation of regional water table depletion and evaporative cooling demands in hyperscale compute hubs. - Joler, Vladan, and Kate Crawford. “Anatomy of an AI System: The Amazon Echo as an Anatomical Map of Human Labor, Data and Planetary Resources.” 2018.
Exhaustive anatomical diagram of the human labor, mineral extraction, and planetary logistics required to power a brief voice command. - Lemley, Mark A., and Bryan Casey. “Fair Learning.” Texas Law Review, 99(4), 2021.
Legal analysis arguing that machine learning training falls squarely under fair use doctrine. - Masley, Andy. “Deconstructing AI Water Footprint Misconceptions: Thermodynamics and Closed-Loop Cooling.” 2026. (Wiki Source Card)
Thermodynamic clarification between consumptive evaporation and thermoelectric water withdrawal in computing facilities. - Pontifical Academy for Life. Rome Call for AI Ethics: Principles of Transparency, Inclusion, Responsibility, Impartiality, Reliability, and Security. Vatican City / RenAIssance Foundation, 2020/2024.
Multi-stakeholder ethical pact establishing human-centric algorithmic governance principles signed by global tech leaders and international institutions. - Sobel, Benjamin L. W. “Artificial Intelligence’s Fair Use Conundrum.” Columbia Journal of Law & the Arts, 41(3), 2017.
Examines whether machine training constitutes transformative fair use when models generate competitive expressive substitutes.
5. Historical Precedents, Cybernetics & Symbiosis
Books
- Reichardt, Jasia. Cybernetic Serendipity: The Computer and the Arts. Studio International, 1968.
The landmark exhibition catalog documenting early generative art, algorithmic music, computer graphics, and cybernetic kinetic sculpture. - Wardrip-Fruin, Noah, and Nick Montfort (eds.). The New Media Reader. The MIT Press, 2003.
Collected primary source historical essays spanning Bush, Turing, Engelbart, Nelson, Kay, and Weiner. - Wiener, Norbert. The Human Use of Human Beings: Cybernetics and Society. Houghton Mifflin, 1950.
Foundational cybernetic treatise on feedback loops, communication theory, and societal consequences of automation.
Classic Articles
- Engelbart, Douglas C. “Augmenting Human Intellect: A Conceptual Framework.” Stanford Research Institute, 1962.
The seminal vision proposing computers not as autonomous replacements for humans, but as interactive cognitive prostheses. - Licklider, J. C. R. “Man-Computer Symbiosis.” IRE Transactions on Human Factors in Electronics, 1960.
Proposes tight coupling between human creative intuition and computer algorithmic speed. - Turing, Alan M. “Computing Machinery and Intelligence.” Mind, 59(236), 1950.
Introduces the Imitation Game (Turing Test) and foundational philosophical questions regarding machine thought. - Vinge, Vernor. “The Coming Technological Singularity: How to Survive in the Post-Human Era.” NASA VISION-21, 1993. (Wiki Source Card)
Seminal essay positing superhuman intelligence emergence through machine learning and human-computer interfaces.
Return to DDA500 Course Overview · Knowledge Graph