A Responsive Engagement Approach to Promote the Development of ‘Fairer’ Algorithms
Bibliographic record
Abstract
There is much contemporary concern about ‘unfairness’ in algorithmic systems. Public controversies have arisen over lack of transparency and accountability in the development and application of algorithmic systems, as well as their potential to produce outcomes that are systematically unfavourable to certain groups. As a result, a variety of fairness criteria and metrics have been proposed to guide the development of algorithmic systems. However, it is unclear whether and how wider society can be involved in deciding which of these different fairness criteria should be favoured. Our work addresses this question by drawing on Responsible Innovation (RI) and ‘society in the loop’ (STIL) approaches. These suggest that the development of ‘fairer’ algorithmic systems may be facilitated through responsive engagement with societal stakeholders. We conducted an exploratory study to determine whether it is possible to present a complex set of algorithms to lay stakeholders in a way that enables them to make informed decisions about them. We presented participants with two limited resource allocation scenarios and a set of algorithms; we then asked them to select which of the algorithms they most and least preferred for the allocation. We collected quantitative data recording participant selections and qualitative data capturing how participants explained and justified their selections. We found that participants were able to meaningfully interrogate the algorithms presented to them and displayed grounded understanding of the consequences of different selections. Whilst there was no overall consensus in either scenario, participants displayed patterns in their reasoning. They consistently treated their decisions as contingent on specific understandings of fairness and context, and different interpretations of these matters accounted for different preference selections. These insights and the approach itself can be incorporated into co design processes for contemporary algorithmic systems.
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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 | 0.054 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".