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AI Algorithm Transparency and Bias Assessment

Active
Federal

Contract Overview

Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.

AI Contract Overview

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The contract requires an independent evaluation of artificial intelligence and machine learning models used in prior-art search and office action generation, with a focus on transparency, explainability, and the absence of bias in their outputs. The assessment must ensure that algorithmic decisions are interpretable and fair, particularly in contexts where accuracy and neutrality directly impact patent examination processes. The work is to be performed at a location in Alexandria, Virginia, with a NAICS code of 541690 indicating it falls under other scientific and technical consulting services. This is a subcontract under the Department of Commerce, Patent and Trademark Office, and the solicitation was posted on June 29, 2026. While no set-aside classification or specific point of contact is provided, the emphasis is on rigorous, objective analysis of AI systems to uphold the integrity of patent offices' automated functions. The evaluation must deliver actionable findings that support accountability, regulatory compliance, and public trust in AI-driven patent systems, without reliance on proprietary or opaque methodologies.

General Info

Evaluate AI/ML models for patent search and office actions in Alexandria, VA, ensuring transparency, fairness, and interpretability.

Agency

Department Of Commerce PtoView Agency

NAICS

541690 - Other Scientific and Technical Consulting ServicesView NAICS

Place of Performance

Alexandria, VA, 22313, USA

Set-Aside

NONE

Documents

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No documents available

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Timeline

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Organization & Contact Information

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AgencyDepartment Of Commerce Pto
ContactsNo contacts available
OfficeN/A
Organization / Agency
Department Of Commerce Pto
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Office AddressN/A
ContactsNo contact information available

Full Description

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Independently evaluate AI/ML models for transparency, explainability, and absence of bias in outputs related to prior-art search and office action generation.

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