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New Seminar Series:

Bayesian Networks—
Artificial Intelligence for Judicial Reasoning


Bayesian Networks—
Artificial Intelligence for Judicial Reasoning

"It is our contention that a Bayesian network (BN), which is a graphical model of uncertainty, is especially well-suited to legal arguments. A BN enables us to visualise the relationship between different hypotheses and pieces of evidence in a complex legal argument. But, in addition to its powerful visual appeal, it has an underlying calculus that determines the revised probability beliefs about all uncertain variables when any piece of new evidence is presented." (Fenton, Neil, & Lagnado, 2013)

While Bayesian networks have been recognized as a powerful research framework in many fields of study for decades, the practical application of Bayesian networks in the field of law remains in its infancy. With this new seminar series, we introduce law practitioners to the fundamental advantages of employing Bayesian networks and BayesiaLab for probabilistic reasoning and decision support.

Upcoming Seminar Dates & Locations

Date Event Location Registration
January 21, 2020 Bayesian Networks — Artificial Intelligence for Judicial Reasoning Washington, D.C. Eventbrite - Bayesian Networks — Artificial Intelligence for Judicial Reasoning in DC
January 30, 2020 Bayesian Networks — Artificial Intelligence for Judicial Reasoning Chicago, IL Eventbrite - Bayesian Networks—Artificial Intelligence for Judicial Reasoning in Chicago

If your organization is interested in a complimentary in-house seminar, please contact our education team at info@bayesia.us.

Seminar Program

In this free three-hour seminar for law professionals, we present a unified, normative framework for judicial reasoning by implementing the Bayesian network formalism with the BayesiaLab 9 software. This provides a visual and explainable approach to Artificial Intelligence that supports core reasoning tasks in the practice of law, such as:

Probabilistic Evidential Reasoning

  • Probabilistic reasoning with conflicting evidence 
  • Overcoming the Prosecutor's Fallacy (Fallacy of the Transposed Conditional)
  • Intercausal reasoning ("explaining away")
  • Quantifying the importance of observations and their sensitivity (Mutual Information)
  • Analyzing evidence consistency (Bayes Factor)
  • Using joint probability as a measure of plausibility
  • Reconstructing foreseeability
  • Eliciting knowledge from stakeholders, using the Bayesia Expert Knowledge Elicitation Environment (BEKEE)

Causal Inference

  • Formal and intuitive treatment of causation, prevention, omission, and omission of prevention
  • Evaluating bias claims and discrimination (dealing with Simpson's Paradox)
  • Causal inference for estimating effects from observational data and expert knowledge
  • Counterfactual causal analysis ("Had it not been for...")
  • Computing the "most relevant explanation" of an observed outcome
  • Contribution/attribution analysis for allocating damages between multiple defendants

Decision Support & Optimization

  • Developing adversarial reasoning strategies
  • Quantifying outcome uncertainty (Entropy)
  • Modeling jury perception
  • Estimating overall case risk

Seminar Format, Technology, and Materials

  • BayesiaLab SeminarIn this seminar, we alternate slides presentations, group discussions, and software demos of case studies.
  • The number of participants is limited to 20.
  • There will be one 10-minute break during the seminar.
  • After the event, you will receive all presentation slides in PDF format.
  • You can download all Bayesian network models used in the seminar.

Requirements for Participants

  • This seminar is intended exclusively for law practitioners, i.e., lawyers, attorneys, judges, corporate counsels, law clerks, arbitrators, plus law students and law school faculty.
  • No mathematical, statistical, or programming skills are required as background. 
  • Even though the seminar is free of mathematical formulas and statistical jargon, it is a fast-paced and intellectually challenging program. So, your full concentration will be required over three hours.

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