Probability, Professionalism, and Protecting Taxpayers

103 Pages Posted: 23 Apr 2014 Last revised: 19 Feb 2015

Dennis J. Ventry Jr.

University of California, Davis - School of Law

Bradley T. Borden

Brooklyn Law School

Date Written: April 21, 2014

Abstract

This Article — the first in a three-part series — analyzes the affirmative and disciplinary duties imposed on tax lawyers that require them to make probability assessments about the merits of a client’s tax position or tax-favored transaction and to reflect those estimates with numerical precision. It describes how the Treasury, Congress, and the American Bar Association (often in concert, occasionally at odds) forged this obligatory standard of care over the last three decades with the shared goal of facilitating accurate advice, accurate reporting positions, and compliance with the law. The resulting regulatory standard of care (which swept aside the old regime of self-regulation) assists tax lawyers in avoiding flawed methodological processes and in minimizing psychological biases and misaligned incentives that can distort professional judgment. In this way, the standard of care for tax lawyers — particularly its emphasis on improving accuracy and reducing errors by updating subjective beliefs with new, relevant information — reflects a branch of probabilistic decision theory known as Bayesian reasoning.

Suggested Citation

Ventry, Dennis J. and Borden, Bradley T., Probability, Professionalism, and Protecting Taxpayers (April 21, 2014). Tax Lawyer, Vol. 68, No. 3, 2014; UC Davis Legal Studies Research Paper No. 377; Brooklyn Law School, Legal Studies Paper No. 385. Available at SSRN: https://ssrn.com/abstract=2427498

Dennis J. Ventry Jr. (Contact Author)

University of California, Davis - School of Law ( email )

UC Davis School of Law
400 Mrak Hall Drive
Davis, CA 95616-5201
United States
530-752-4566 (Phone)

Bradley T. Borden

Brooklyn Law School ( email )

250 Joralemon Street
Brooklyn, NY 11201
United States

Paper statistics

Downloads
159
Rank
152,464
Abstract Views
1,653