Systemic Bias in Artificial Intelligence: Focusing on Gender, Racial, and Political Biases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper examines systemic bias in artificial intelligence (AI), focusing on gender, racial, and political dimensions. As AI technology has evolved from theoretical frameworks to practical applications across social and cultural realms—ranging from collaborative robots to natural language processing—it has achieved significant advancements. However, this transition has highlighted a critical tension between AI's precise algorithms and the intricate dynamics of human society, revealing how systemic biases can diverge from ethical standards and perpetuate inequality. By delving into these biases, this study aims to illuminate the ways AI can unjustly advantage or disadvantage specific groups, ultimately contributing to a deeper understanding of the ethical implications of AI technologies in contemporary society.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it