Personal Liability of Directors and Officers in Tort: Searching for Coherence and Accountability
Bibliographic record
Abstract
The 21st century has been marred by corporate scandal after scandal, including financial fraud, pyramid schemes, international bribery, and decades of sexual harassment. This raises an important question regarding the role of corporate and tort law in controlling the behavior of corporate executives more broadly. It is clear that directors and officers should not be overexposed to tortious liability – doing so would ultimately make them insurers of the firm’s obligations. Yet underexposure creates its own set of problems, including a lack of accountability when directors and officers are not required by law to conduct themselves reasonably. The purpose of this Article is to address how U.S. state courts attribute personal liability in tort to directors and officers in actions by non-shareholder third parties. It does so, in part, by relying on Canadian law as a comparator as well as on Professor Lewis Checchia's admonishment that the law must not "reward unreasonable and unethical conduct" nor "deny recovery to injured third parties with valid legal claims." The Article concludes that, contrary to the law in certain U.S. jurisdictions, directors and officers liability should be assessed according to the ordinary principles of tort law. Defenses based on the special status of directors and officers are objectionable because they degrade corporate culture, generate moral hazard, and deny justice to the otherwise worthy plaintiff.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".