Bias of arbitrators: a critical analysis on the law post-Halliburton v. Chubb and a comparative approach
Bibliographic record
Abstract
The principle of independence and impartiality has been formed, over the course of time, into a well-established and simultaneously into a fundamental duty of the arbitrator. However, the question, which arises, pertains to what kind of duty it is, namely either a legal duty or one resembling professional ethics. As the case is with judges, arbitrators also shall not be biased or even give the impression of being biased. Unlike judges, however, arbitrators are nominated by the parties to the arbitration and therefore, concerns with regards to possible bias or lack of impartiality are likely to be raised to a greater extent. The principal triptych, which overrides this multifaceted subject, concerns mainly questions of disclosure, repeat appointments and apparent bias. The arbitrator’s duty to remain unbiased and impartial is stipulated as a soft law rule in the IBA Guidelines of 2014, which serves as the point of reference and according to which there has to be an equilibrium between the principle of party autonomy and the tribunal’s independence. In the present paper, a critical analysis is conducted as to the formation of the landscape regarding arbitrator’s bias, before and after the landmark decision of the Supreme Court in Halliburton Co v Chubb Bermuda Insurance Ltd (2020) UKSC 48. The lessons to be learned from this judgment are comparatively assessed alongside the position of arbitration laws of England, India, and China, and by illustrating how the duty has been incorporated and appeared in arbitration practice through the lenses of the arbitration laws in each of the examined legal regimes. Resultantly, the Arbitration Act 1996, the Arbitration and Conciliation (Amendment) Act, 1996, the Chinese Arbitration Law as well as the China International Economic and Trade Arbitration Commission (CIETAC) Rules, which apply to foreign-related arbitrations, will be analyzed in conjunction with case-law in the above-mentioned jurisdictions.
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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 | 0.032 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.019 | 0.041 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".