Looking Beyond the Lineup: Evaluating the Fairness of 'Digiboard' Identification Evidence for First Nations Peoples
Bibliographic record
Abstract
Research has established that mistaken identification is a ‘particularly prevalent’ cause of wrongful convictions of First Nations persons in Australia. Identification evidence has long been understood to be vulnerable to factors which make it unreliable. The potential for unreliability is compounded in the case of a witness identifying somebody of a different ethnic or racial identity to themselves, and in Australia this has a disproportionate effect on First Nations peoples. For these reasons, the rules relating to the admissibility of identification evidence in criminal trials are important. Western Australia (WA) has recently introduced a bill which, if passed, will substantially adopt the Uniform Evidence Law (which already operates in the Commonwealth jurisdiction, as well as in New South Wales, Victoria, Tasmania, and both Territories). However, one significant area in which the proposed WA law differs from the Uniform Evidence Law is in relation to identification evidence. In particular, whilst s 114 of the Uniform Evidence Law typically requires identification to occur through an identification parade (also colloquially called a ‘police lineup’), the proposed WA law does not include such a requirement. In WA, police typically use ‘digiboards’ when facilitating witness identification. Digiboards are essentially an array of photographs from which the witness can attempt to identify the person they saw connected with the offence. A digiboard usually shows 12 photographs — one of the suspect and the others being fillers (ie, they are similar to a traditional ‘photo board’ used for identification). However, unlike a traditional photo board, digiboard photographs are digitally altered with a computer program to ensure the greatest possible similarity between the suspect and the filler photographs. The proposed WA law enables this practice to continue. This article considers whether this divergence in the WA law is likely to be consequential for First Nations peoples. Is WA’s decision not to require identification parades problematic, or is there no material difference between the identification procedures?
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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 | 0.485 | 0.763 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".