From Risk Appetite to Values at Risk: Exploring the Ethical Turn in Risk Management
Bibliographic record
Abstract
In the wake of the 2007–2009 financial crisis and subsequent financial services scandals, there are numerous normative and regulatory demands on risk managers to assess the ethicality of corporate plans and actions. An ethical turn in risk management looms, focusing the attention of boards and executive teams on a plurality of ValueS at Risk, rather than on a single or composite—and primarily financial—Value at Risk. The controller’s question of what risks an organisation is running is increasingly seen as intertwined with the ethical question of whose risks an organisation is managing—or even taking into account—a discussion that needs to be addressed in what Power (AOS, 2009) conceptualised as the “risk appetising process”. Based on a longitudinal case study conducted at a Canadian electric utility, I show empirical evidence that some risk managers have created tools and processes that tangibly link risk management and business ethics. Drawing on the literature of behavioural ethics, I outline a conceptual framework that also links risk management and business ethics. I conclude that an ethical turn in risk management will require the ability to bring the ethical dimension to the fore by making ValueS at Risk graphically visible as part of strategic decision-making and formal control processes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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 teacher head, 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".