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Ambiguity Remains After Escobar

Client Alert | 1 min read | 11.09.16

In U.S. ex rel. Nelson v. Sanford-Brown Ltd. (Oct. 24, 2016), the Seventh Circuit, applying the materiality standard articulated by the Supreme Court in Escobar (discussion available here), held that the relator’s allegations that the college inflated grades and job placement figures and paid bonuses to employees for recruitment to fraudulently obtain federal student aid money failed because there was no evidence that the college had made any express or implied representations with its claims for payment or evidence that the government’s payment decision would likely have been different had it known of the alleged misrepresentations. In contrast, the Eighth Circuit in U.S. ex rel. Miller v. Weston Educ. Inc. (Oct. 19, 2016) held that similar allegations withstood summary judgment (as noted by C&M here), suggesting that the Supreme Court’s decision in Escobar may not have resolved the circuit split on implied certification after all.

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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 .  ...