Board Clarifies that Claim Accrual Contains Implicit "Reasonableness" Standard
Client Alert | 1 min read | 06.07.17
In Sparton DeLeon Springs, LLC (ASBCA No. 60416, May 18, 2017), the Board denied the government’s request for reconsideration of an earlier Board decision, which had rejected the government’s claim for recoupment of alleged overpayments of direct costs as time-barred by the CDA's six-year statute of limitations (previously discussed in a blog post). In support of this reconsideration decision, and in response to the government’s argument that "the Board applied the wrong legal standard for determining whether the claim had accrued," the Board explained that it saw "no conceptual difference between the 'should have been known' standard set forth in [FAR] 33.201" and "the phrase 'reasonably should have known' recited by the government" because "the one expresses only what the other implies."
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Client Alert | 9 min read | 10.21.26
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 .
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