Digital Platforms and Algorithmic Erasure: What are the Implications?
Bibliographic record
Abstract
As technology advances, people of colour often fall victim to algorithm racial bias. This paper focuses on the problem of digital tools that misidentify, fail to recognize, or erase people of colour. On a spectrum, these issues can range from the annoyance of making people of colour invisible during online meetings, to the endangerment of falsely identifying people of colour of crimes that they did not commit. We encountered the former challenge in September 2020, during a faculty Zoom meeting. Our Zoom erasure experience and subsequent Twitter crop experience raised questions for our investigation: why do people of colour experience erasure on zoom and other digital platforms? Is this problem new? What are the outcomes of our experience? How could the problem be fixed? How is it that biases in technology seem to emulate those found in social life? In this paper we aim to raise awareness through sharing our experience and recommending the interrogation of algorithmic tools released for market, the creation of government policy and laws to hold software companies accountable, and the education about biases for IT professionals, educators, and students in the field.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".