Renewed War on Drugs, harsher charging policies, stepped-up criminalization of immigrants — in the current climate, joining the NACDL is more important than ever. Members of NACDL help to support the only national organization working at all levels of government to ensure that the voice of the defense bar is heard.
Take a stand for a fair, rational, and humane criminal legal system
Contact members of congress, sign petitions, and more
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Increase brand exposure while building trust and credibility
NACDL is committed to enhancing the capacity of the criminal defense bar to safeguard fundamental constitutional rights.
NACDL harnesses the unique perspectives of NACDL members to advocate for policy and practice improvements in the criminal legal system.
NACDL envisions a society where all individuals receive fair, rational, and humane treatment within the criminal legal system.
NACDL’s mission is to serve as a leader, alongside diverse coalitions, in identifying and reforming flaws and inequities in the criminal legal system, and redressing systemic racism, and ensuring that its members and others in the criminal defense bar are fully equipped to serve all accused persons at the highest level.
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NACDL has engaged in advocacy efforts to reform the discovery process in Connecticut.
NACDL is working to reform discovery laws in Oregon.
NACDL collaborated with partners to successfully advocate for major discovery reforms in Virginia.
In this episode, we explore the ongoing, multi-year effort to bring criminal discovery reform to the Commonwealth of Virginia, an effort in which NACDL has been a leader.
NACDL comments on proposed revisions to Rules 3A:11 and 3A:12 proposed by the Virginia State Bar’s Criminal Discovery Reform Task Force.
Joint letter from NACDL and VACDL to the Supreme Court of Virginia regarding the Court's decision to delay the effective date of the new criminal discovery rules until July 1, 2020.
NACDL comments to the Virginia Supreme Court on proposed revisions to Virginia's discovery rules: Proposed Revisions to Rules 3A:11 and 3A:12 Received from the Virginia State Bar Criminal Discovery Reform Task Force.
Letter to the Judicial Conference Committee on Practice & Procedure regarding proposed rule changes to the Federal Rules of Criminal Procedure and Rules Governing 2255 Proceedings.
Letter to the Advisory Committee on Local Rules chairman regarding proposed changes to criminal disclosure rules in the U.S. District Court for the District of Columbia.
Letter to the Judicial Conference Standing Committee on Rules of Practice & Procedure regarding proposed rule changes to the Federal Rules of Criminal Procedure, Federal Rules of Evidence, and Federal Rules of Appellate Procedure.
NACDL comments on S.B. No. 653, an act concerning open-file disclosure in criminal cases.
Letter from current and former judges, prosecutors, law enforcement, defense attorneys, and others to members of Congress on the need for reform and clarification of federal criminal discovery obligations.
Criminal discovery can be divided into two categories: (1) disclosure of so-called “exculpatory evidence” that is constitutionally required under the Supreme Court’s 1963 Brady opinion and (2) disclosure that is required by statute or court rule. Discovery allows you to build a fully-informed defense for your client. "Discover" resources for your case here.
A guide on Brady v. Maryland prepared by the Special Litigation Division of the Public Defender Services of District of Columbia. It is both a quick reference guide and a starting point for correspondence or pleading addressing the government’s Brady obligations.
Forensic software is used in the criminal justice context to make assertions about the presence and nature of DNA, to deploy police resources to certain areas, or to guide bail and sentencing determinations. Software, however, is far from impartial or infallible. The authors explain why law and public policy require disclosure to the public and independent experts of the software source code and other software development records, including training data sets.
