Towards a Normative Assessment of Probative Value in International Criminal Adjudication

2021 ◽  
pp. 1-28
Author(s):  
Karen McGregor Richmond
Keyword(s):  
International Criminal ◽  
Evaluation Criteria ◽  
Network Models ◽  
Evidential Reasoning ◽  
Criminal Trials ◽  
Normative Model ◽  
Probative Value ◽  
Hybrid Approaches ◽  
The Face ◽  
New Evidence

Abstract The trial is an epistemic event. Yet, the manner in which the probative value of legal evidence is calculated remains largely unarticulated. Thus, in the face of an urgent requirement to advance a normative model of evidential reasoning which serves the needs of decision-makers, practitioners, and experts, this article assesses the utility of three dominant approaches, founded upon the exposition of inferences, scenarios, and probabilities. These find expression in Wigmorean, Narrative, and Bayesian network models, and a number of hybrid approaches. Through an analysis and critique of the foregoing models, the article attempts to discern the optimal normative model of evidential reasoning to be applied in international criminal trials, consonant with Twining’s formulation of rational adjudication, and assessed in accordance with a set of rational evaluation criteria drawn from New Evidence scholarship and its historical forbears.

The Burden of Bearing Witness: The Impact of Testifying at War Crimes Tribunals

2017 ◽  
Vol 63 (2) ◽  
pp. 348-372 ◽  
Author(s):  
Kimi King ◽  
James Meernik
Keyword(s):  
International Criminal ◽  
Theoretical Perspective ◽  
International Criminal Tribunal ◽  
Mass Violence ◽  
Bearing Witness ◽  
Post Conflict ◽  
Adverse Experiences ◽  
The Face ◽  
War Crimes Tribunals ◽  
The Impact

Intersections exist regarding how institutions and individuals respond in the wake of mass violence, and we explore one theoretical perspective: resilience—the ability to overcome in the face of adversity. By controlling for the institutional context, we analyze the microlevel impact of testifying on witnesses who testify. New survey data provide information from 300 prosecution, defence, and Chambers witnesses who appeared at the International Criminal Tribunal for the Former Yugoslavia. We test propositions about resilience related to trauma, motivations, contributions to justice, fair treatment, witness fatigue, and human security. Witnesses who experienced greater trauma, who were more highly motivated, who believed they contributed to justice, and who were satisfied with their current situation were more positive about testifying. Those who believed they were treated fairly by prosecution and defence were less negative. The findings add to the debate about the burden of bearing witness in post-conflict societies and why some overcome adverse experiences related to mass violence.

Transitional Justice, Truth and Reconciliation: An Under-Explored Relationship

2011 ◽  
Vol 11 (2) ◽  
pp. 241-261 ◽  
Author(s):  
Janine Natalya Clark
Keyword(s):  
Empirical Research ◽  
Transitional Justice ◽  
International Criminal ◽  
International Criminal Tribunal ◽  
Evidence Based ◽  
Criminal Trials ◽  
Truth And Reconciliation ◽  
Post Conflict ◽  
Truth And Reconciliation Commissions ◽  
Conflict Reconciliation

AbstractMuch of the literature on transitional justice suffers from a critical impact gap, which scholars are only now beginning to address. One particular manifestation of this aforementioned gap, and one which forms the particular focus of this article, is the frequently-cited yet empirically under-researched claim that "truth" fosters post-conflict reconciliation. Theoretically and empirically critiquing this argument, this article both questions the comprehensiveness of truth established through criminal trials and truth and reconciliation commissions (TRCs) and underscores the often overlooked problem of denial, thus raising fundamental questions about the reputed healing properties of truth in such contexts. Advocating the case for evidence-based transitional justice, it reflects upon empirical research on South Africa's TRC and the author's own work on the International Criminal Tribunal for the former Yugoslavia (ICTY).

scholarly journals Trial and (Potential) Error: Conflicting Visions on Reparations Within the ICC System

2018 ◽  
Vol 29 (3) ◽  
pp. 221-248 ◽  
Author(s):  
Alina Balta ◽  
Manon Bax ◽  
Rianne Letschert
Keyword(s):  
Criminal Justice ◽  
International Criminal Court ◽  
Rome Statute ◽  
International Criminal ◽  
Criminal Court ◽  
International Criminal Justice ◽  
Court Decisions ◽  
Criminal Trials ◽  
Conflict Situations ◽  
International Criminal Trials

Twenty years ago, the International Criminal Court (hereinafter ICC or the Court) was established holding the aim of placing victims at the heart of international criminal justice proceedings and delivering justice to them through, among others, reparations. Article 75 of the Rome Statute lays out the reparations regime, and, in practice, court-ordered reparations are a means of delivering such justice. Focusing on Court decisions on reparations, our analysis takes stock of all developments before the ICC and attempts to highlight the mismatch between characteristics inherent to the objectives of international criminal trials such as providing accountability and punishment of the accused and delivering justice for victims of mass crimes—the so-called procedural challenges. We also submit that the Court is facing conceptual challenges, related to an apparent misunderstanding of the various concepts at stake: reparations as such and the various modalities and channels of enforcing them. We conclude that although the ICC’s reparation regime may not be the best reparative response to provide justice to victims in conflict situations affected by mass victimization, we suggest that improving the ICC’s approach includes, at a minimum, tackling these challenges.

scholarly journals AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks

2000 ◽  
Vol 13 ◽  
pp. 155-188 ◽  
Author(s):  
J. Cheng ◽  
M. J. Druzdzel
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Importance Sampling ◽  
Network Models ◽  
Evidential Reasoning ◽  
The Other ◽  
Sampling Algorithm ◽  
Importance Function ◽  
Sampling Algorithms ◽  
Bayesian Network Models

Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance sampling algorithm, AIS-BN, that shows promising convergence rates even under extreme conditions and seems to outperform the existing sampling algorithms consistently. Three sources of this performance improvement are (1) two heuristics for initialization of the importance function that are based on the theoretical properties of importance sampling in finite-dimensional integrals and the structural advantages of Bayesian networks, (2) a smooth learning method for the importance function, and (3) a dynamic weighting function for combining samples from different stages of the algorithm. We tested the performance of the AIS-BN algorithm along with two state of the art general purpose sampling algorithms, likelihood weighting (Fung & Chang, 1989; Shachter & Peot, 1989) and self-importance sampling (Shachter & Peot, 1989). We used in our tests three large real Bayesian network models available to the scientific community: the CPCS network (Pradhan et al., 1994), the PathFinder network (Heckerman, Horvitz, & Nathwani, 1990), and the ANDES network (Conati, Gertner, VanLehn, & Druzdzel, 1997), with evidence as unlikely as 10^-41. While the AIS-BN algorithm always performed better than the other two algorithms, in the majority of the test cases it achieved orders of magnitude improvement in precision of the results. Improvement in speed given a desired precision is even more dramatic, although we are unable to report numerical results here, as the other algorithms almost never achieved the precision reached even by the first few iterations of the AIS-BN algorithm.

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