More Than ‘‘Responsible AI” Bridging Artificial Intelligence Systems (AIS) and AISystems Ethics into Practice
Bibliographic record
Abstract
This article reflects on the question ‘‘how should we approach the ethics of AI and technology?” through the example of how the Canada Revenue Agency (CRA) is working within this space to develop its Artificial Intelligence Systems (AIS) Ethics Lifelong Learning and Professional Development Strategy. This strategy is connected, but also acts as a critical counterpoint, to approaches to AI governance and accountability that are reliant on a notion of ‘‘Responsible AI”. In these contexts, responsible AI is understood as the regulatory adoption of ‘‘rules” diffused through a normative structure of hierarchical authority within the organization or business. Rather, this article demonstrates how considerations for AISystems ethics should be necessarily diffused and distributed through many diverse types of structures and people, and how the CRA is doing this in practice. Professional development and lifelong learning approaches to ethics posit the learner themselves as central and accords that learner with both the responsibility and the possibility of transformative action through inclusive participation. The CRA’s AIS Ethics Lifelong Learning and Professional Development Strategy foregrounds an understanding of ethics, and AISystems ethics, as a balancing or redistribution of power relations with a view to how those systems are implicated within that context. In viewing AISystems ethics as implicated not in rules but in relations, we also are better positioned to develop impactful policy and programs to address how AISystems function as a disruptive force, technology, and set of practices (both negative and positive) for those who are disproportionately affected by the harmful aspects of new technologies.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.102 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".