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CITIZEN SUIT WATCH: U.S. Supreme Court Agrees to Review Ninth Circuit Decision Requiring Clean Water Act Permits for Forest Roads

Client Alert | 1 min read | 06.25.12

On June 25, 2012, the U.S. Supreme Court granted two petitions for writs of certiorari to review the decision of the U.S. Court of Appeals for the Ninth Circuit in Northwest Environmental Defense Center v. Brown, 640 F.3d 1063 (2011). The Ninth Circuit had ruled that storm water runoff from forest roads is subject to permitting under Clean Water Act section 402, 33 U.S.C. § 1342. The Ninth Circuit rejected the longstanding position of the U.S. Environmental Protection Agency ("EPA") that section 402 permits were not required for such runoff under the Agency's 1976 Silvicultural Rule – a position that EPA reaffirmed in its 1990 stormwater regulations implementing the 1987 amendments to the statute. (For a more detailed discussion of the Ninth Circuit's decision, click here)

Crowell & Moring filed an amicus brief on behalf of the National Alliance of Forest Owners, the American Farm Bureau Federation, and eleven other state and national trade associations all representing forest landowners (together, "Amici";) in support of the two petitions, focusing on the Ninth Circuit’s jurisdictional analysis, in addition to the court’s analysis of the merits under the Clean Water Act. (For a more detailed discussion of the Amici brief, click here.)

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Client Alert | 9 min read | 10.21.26

Artificial Intelligence in Life Sciences: Reliability of AI-Generated Evidence in Drug Discovery and Development

The life sciences sector is uniquely vulnerable to the inherent risks of AI-generated evidence in drug development and discovery. This client alert focuses on five core challenges: hallucinations, skewed predictions from unrepresentative training data, the opacity of AI reasoning, input sensitivity that undermines reproducibility, and adversarial manipulation of training data. To address these risks, companies should use experimental validation and human oversight as the common legal and regulatory safety net. This entails among other measures auditing AI outputs, characterizing training data, and structural oversight. Companies also should prepare for evolving frameworks and guidance from the U.S. Food and Drug Administration (FDA) and the European Union (EU) that address use of AI in drug discovery and development .  ...