Implicit Bias and Reform Efforts in Philosophy: A Defence
Bibliographic record
Abstract
This paper takes as its focus efforts to address particular aspects of sexist oppression and its intersections, in a particular field: it discusses reform efforts in philosophy. In recent years, there has been a growing international movement to change the way that our profession functions and is structured, in order to make it more welcoming for members of marginalized groups. One especially prominent and successful form of justification for these reform efforts has drawn on empirical data regarding implicit biases and their effects. Here, we address two concerns about these empirical data. First, critics have for some time argued that the studies drawn upon cannot give us an accurate picture of the workings of prejudice, because they ignore the intersectional nature of these phenomena. More recently, concerns have been raised about the empirical data supporting the nature and existence of implicit bias. Each of these concerns, but perhaps more commonly the latter, are thought by some to undermine reform efforts in philosophy. In this paper, we take a three-pronged approach to these claims. First, we show that the reforms can be motivated quite independently of the implicit bias data, and that many of these reforms are in fact very well suited to dealing with intersectional worries. Next, we show that in fact the empirical concerns about the implicit bias data are not nearly as problematic as some have thought. Finally, we argue that while the intersectional concerns are an immensely valuable criticism of early work on implicit bias, more recent work is starting to address these worries.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".