Design Heuristics for Emerging Technologies: AI, Data, & Human-Centered Futures – Considerations for the Rights of Women
Bibliographic record
Abstract
In Design Heuristics for Emerging Technologies, Kem-Laurin Lubin delivers a visionary exploration of the intersection between artificial intelligence, systemic bias, and the urgent need for equity in digital design. Grounded in feminist theory and critical data studies, this book tackles the complex ways patriarchal systems shape AI-powered technologies, with a particular focus on their impact on women’s rights and reproductive healthcare—an issue made even more pressing in the wake of the reversal of Roe v. Wade. Through compelling case studies, such as the controversial AI project in Salta, Argentina, Lubin illustrates the tangible consequences of algorithmic design in real-world contexts. Organized under six heuristic categories, these examples provide a robust framework for practitioners and policymakers to design equitable systems. Engaging deeply with seminal works like Algorithms of Oppression by Safiya Noble and Race After Technology by Ruha Benjamin, Lubin critiques current AI frameworks and offers a path forward. The book examines global initiatives, from Europe’s GDPR to Canada’s Artificial Intelligence and Data Act, highlighting their limitations and proposing actionable, equity-centered heuristics. With a critical lens on existing policies, Lubin integrates insights from global policy examinations and targeted case studies to propose scalable, human-centered solutions for more inclusive technologies. A masterful blend of theory and practice, Design Heuristics for Emerging Technologies invites scholars, technologists, and policymakers to reimagine AI systems that prioritize the rights of women and marginalized communities. Lubin’s work is a call to action to build a future where emerging technologies serve as tools of empowerment and justice, fostering human-centered design and ethical innovation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".