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This project focuses on using Large Language Models (LLMs) to provide annotations of evaluation data (a.k.a., LLM as judge), and the design of an Inter-Annotator Agreement study to assess the reliability of both human and LLM annotations. The candidate will explore assessing the indicators of a given AI-related risk, determining how to identify them, and providing annotators with examples to annotate the presence of various risks. The project aims to develop an annotation framework for AI risk assessment and establish metrics for data quality in AI risk research, supporting broader work at NIST in assessing and measuring the validity and reliability of AI-related risks in data annotation.
Research Title
Reliability of Human and LLM Annotations for AI Risk Assessment
Key Responsibilities
-Gain familiarity with existing literature on data annotation and LLM as judge
-Understand NIST's role and ongoing efforts in assessing and measuring the validity and reliability of AI-related risks in data annotation
-Contribute to developing an annotation framework for AI risk assessment
-Collaborate effectively with cross-functional and interdisciplinary stakeholders to ensure successful project outcomes
Deliverables
Contributions to a NIST report that supports ongoing NIST AI evaluation efforts focused on the design of an Inter-Annotator Agreement to assess the reliability of both human and LLM annotations.
Qualifications
Background in Computer Science, Data Science, or related field.
Education level : undergraduate or graduate student
Strong interest in data annotation and AI risks
Familiarity with scientific reading and technical writing
Number of Positions: 1
The details of the work arrangement can be discussed and agreed upon, but the position is expected to be primarily telework-based, with some in-person meetings at NIST's Gaithersburg campus as needed.
Average Hours Per Week: 20
Application Instructions
Please upload the following with your application:
-CV/Resume
*Please limit C.V to 3 pages only and ONLY include a valid email address for your contact info. Your resume will not be considered if the following information is included on your CV/resume.
-Self portraits
-Phone number
-Home address/Country
-Citizenship status
-Languages spoken
-Sex/Gender
Privacy Act Statement
Authority: 15 U.S.C. § 278g-1(e)(1) and (e)(3) and 15 U.S.C. § 272(b) and (c)
Purpose: The National Institute for Standards and Technology (NIST) hosts the Professional Research Experience Program (PREP) which is designed to provide valuable laboratory experience and financial assistance to undergraduates, post-bachelor's degree holders, graduate students, master's degree holders, postdocs, and faculty.
PREP is a 5-year cooperative agreement between NIST laboratories and participating PREP Universities to establish a collaborative research relationship between NIST and U.S. institutions of higher education in the following disciplines including (but may not be limited to) biochemistry, biological sciences, chemistry, computer science, engineering, electronics, materials science, mathematics, nanoscale science, neutron science, physical science, physics, and statistics. This collection of information is needed to facilitate administrative functions of the PREP Program.
Routine Uses: NIST will use the information collected to perform the requisite reviews of the applications to determine eligibility, and to meet programmatic requirements. Disclosure of this information is also subject to all the published routine uses as identified in the Privacy Act System of Records Notices: NIST-1: NIST Associates.
Disclosure: Furnishing this information is voluntary. When you submit the form, you are indicating your voluntary consent for NIST to use the information you submit for the purpose stated.
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