Critical Science

An approach to science that emphasizes questioning assumptions, examining power relations, and attending to the social and political dimensions of scientific knowledge. Critical Science doesn't reject science; it insists that science be examined critically, that its claims be interrogated, that its institutions be held accountable. It asks: who funds this research? Whose interests does it serve? What assumptions are built into the methods? What alternatives are excluded? Critical Science is science with its eyes open, aware of its own contingency, committed to self-examination. It's the opposite of scientism—science that knows itself, rather than science that thinks it's above examination.

Example: "She practiced Critical Science: always asking who funded the research, what assumptions shaped the questions, whose voices were excluded. She didn't reject science; she demanded that it be accountable. Her colleagues sometimes found her exhausting; she found them naive."

by Abzugal March 9, 2026

Critical Sciences

The plural form, recognizing that multiple scientific disciplines each require their own critical approaches—that physics has different power dynamics than biology, which has different ones than sociology. Critical Sciences is the collective enterprise of examining science from within, discipline by discipline, asking field-specific questions about assumptions, methods, and social relations. It's the recognition that critique must be tailored to context, that what works for one science may not work for another. Critical Sciences is the ongoing project of making science more self-aware, more accountable, more reflexive.

Example: "The Critical Sciences network brought together scholars from every discipline, each applying critical tools to their own field. Physicists examined funding patterns; biologists questioned research priorities; sociologists analyzed institutional power. Together, they were making science examine itself."

by Abzugal March 9, 2026

Critical Science Communication

An approach to science communication that emphasizes questioning assumptions, examining power relations, and attending to the social and political dimensions of how science is communicated. Critical Science Communication doesn't just transmit scientific findings; it also communicates about the context, limits, and politics of those findings. It asks: who funded this research? What are its limitations? How might it be used? What perspectives are missing? Critical Science Communication is science communication with its eyes open, aware of its own role in shaping public understanding and public policy.

Example: "The journalist practiced Critical Science Communication: she didn't just report findings; she also reported who funded them, what limitations existed, what alternatives were being studied. Her readers were better informed—not just about what was known, but about how it came to be known."

by Abzugal March 9, 2026

Social Sciences Applied to AI

The practice of using insights from sociology, anthropology, psychology, and political science to design, understand, and regulate artificial intelligence. It recognizes that AI systems are not neutral math problems but are embedded in human social contexts. This field asks: How will this algorithm affect community dynamics? What social biases is it learning? How does it change power structures? It's the antidote to the naive view that AI is just code, reminding us that every AI is also a social actor.

Example: "They built a great recommendation engine, but without social sciences applied to AI, they accidentally created filter bubbles that radicalized their users."

by Dumu The Void March 11, 2026

Human Sciences Applied to AI

A broader term encompassing all humanities and human-centered disciplines (philosophy, history, linguistics, arts) brought to bear on the development and deployment of artificial intelligence. It goes beyond fixing bias to ask fundamental questions: What does it mean to be human in an age of intelligent machines? How do we preserve dignity, creativity, and meaning? It's the practice of ensuring that as we build smarter machines, we don't build dumber or lesser humans in the process.

Example: "The ethics board was useless until they brought in a philosopher for human sciences applied to AI—he asked questions about personhood that the engineers had never even considered."

by Dumu The Void March 11, 2026

Cognitive Sciences Applied to AI

The practice of using our understanding of the human mind—perception, memory, reasoning, language, and learning—to inspire and improve artificial intelligence. It's the belief that the best way to build a smart machine is to reverse-engineer the only working example we have: the human brain. From neural networks (loosely inspired by neurons) to reinforcement learning (inspired by animal conditioning), this field has been central to AI's development, for better and for worse.

Cognitive Sciences Applied to AI Example: "The chatbot was terrible at conversation until they applied cognitive sciences to AI and taught it to manage turn-taking and context like a real human would."

by Dumu The Void March 11, 2026

Data Science Applied to AI

The engineering and methodological discipline of preparing, cleaning, analyzing, and governing the data that powers artificial intelligence. It recognizes that AI models are only as good as the data they're trained on. This field focuses on the entire data pipeline: sourcing high-quality data, removing bias, ensuring privacy, and managing the massive datasets required to train modern AI. It's the unglamorous but absolutely essential grunt work that makes the magic happen.

Data Science Applied to AI Example: "The model kept failing, and they realized it was a data science applied to AI problem—the training data was full of duplicates and errors they'd never bothered to clean."

by Dumu The Void March 11, 2026