Work

ChatGPT as Research Scientist: Probing GPT’s capabilities as a Research Librarian, Research Ethicist, Data Generator, and Data Predictor

Year: 2024

Type: article

Abstract: How good a research scientist is ChatGPT? We systematically probed the capabilities of GPT-3.5 and GPT-4 across four central components of the scientific process: as a Research Librarian, Research Ethicist, Data Generator, and Novel Data Predictor, using psychological science as a testing field. In Study 1 (Research Librarian), unlike human researchers, GPT-3.5 and GPT-4 hallucinated, authoritatively generating fictional references 36.0% and 5.4% of the time, respectively, although GPT-4 exhibited an evolving capacity to acknowledge its fictions. In Study 2 (Research Ethicist), GPT-4 (though not GPT-3.5) proved capable of detecting violations like p-hacking in fictional research protocols, correcting 88.6% of blatantly presented issues, and 72.6% of subtly presented issues. In Study 3 (Data Generator), both models consistently replicated patterns of cultural bias previously discovered in large language corpora, indicating that ChatGPT can simulate known results, an antecedent to usefulness for both data generation and skills like hypothesis generation. Contrastingly, in Study 4 (Novel Data Predictor), neither model was successful at predicting new results absent in their training data, and neither appeared to leverage substantially new information when predicting more vs. less novel outcomes. Together, these results suggest that GPT is a flawed but rapidly improving librarian, a decent research ethicist already, capable of data generation in simple domains with known characteristics but poor at predicting novel patterns of empirical data to aid future experimentation.

Source: Proceedings of the National Academy of Sciences

DOI: 10.1073/pnas.2404328121

Authors: Steven A. Lehr, Aylin Caliskan, Suneragiri Liyanage, Mahzarin R. Banaji

Institutions: University of Washington, Harvard University

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

Funder: National Institute of Standards and Technology

Award: PURPOSE: ARTIFICIAL INTELLIGENCE (AI) IS WIDESPREAD IN TODAY'S SOCIETY, WITH AI ALGORITHMS BEING INCREASINGLY RELIED ON TO MAKE CONSEQUENTIAL DECISIONS ABOUT INDIVIDUALS. ALTHOUGH THERE WAS INITIAL OPTIMISM THAT AI APPLICATIONS WOULD MAKE A MORE EFFICIENT AND FAIR SOCIETY, ANECDOTAL EXAMPLES OF BIASED OUTCOMES IN AI APPLICATIONS NOW ABOUND FROM DOMAINS AS WIDE AS WEB SEARCH, MACHINE TRANSLATION, UNIVERSITY ADMISSIONS, RESUME SCREENING, AND JOB CANDIDATE ASSESSMENT. YET, BEYOND THESE ANECDOTES, THERE REMAINS NO COMPREHENSIVE STUDY THAT EXAMINES (1) HOW THE EXTENT OF BIASES IN GENERAL-PURPOSE MODALITY REPRESENTATIONS THAT AI RELIES ON PROPAGATE TO DOWNSTREAM DECISIONS, (2) HOW BIASED AI DECISIONS INTERACT WITH THE BIASES OF HUMANS-IN-THE-LOOP, AND (3) HOW AI-MEDIATED OUTCOMES IMPACT THE BIASES OF FUTURE GENERATIONS OF AI MODELS. THE PROPOSED RESEARCH SEEKS TO ADDRESS THESE NEEDS, THEREBY CONTRIBUTING TO NIST'S TRUSTWORTHY AND RESPONSIBLE AI EFFORTS. ACTIVITIES TO BE PERFORMED: IN THIS PROJECT, THE PROPOSERS WILL ANALYZE THE IMPACT OF BIASED GENERAL-PURPOSE MACHINE REPRESENTATIONS GENERATED VIA UNSUPERVISED MACHINE LEARNING ON THE AUTOMATED DECISIONS OF SUPERVISED MACHINE LEARNING ALGORITHMS THAT RELY ON STRUCTURED DATA IN THE EMPLOYMENT DOMAIN. AS A STARTING POINT, THE PROPOSERS WILL FOCUS ON GENDER AND RACIAL BIAS IN RESUME SCREENING AND JOB CANDIDATE ASSESSMENT, AN APPLICATION DOMAIN WITH CLEAR CONSEQUENCES FOR LIFE'S OPPORTUNITIES AND OUTCOMES. THE PROPOSERS WILL DEVELOP A BIAS PROPAGATION AND INTERACTION AUDIT FRAMEWORK THAT GENERALIZES ACROSS MODALITIES, BIAS TYPES, AND APPLICATION DOMAINS. EXPECTED OUTCOMES: THE PROPOSED RESEARCH WILL CONDUCT TWO COMPLEMENTARY STUDIES TO ANSWER THE FOLLOWING IMPORTANT QUESTIONS: (1) GIVEN THAT BIAS REFLECTED FROM SOCIETY TO LARGE-SCALE DATASETS GETS EMBEDDED IN GENERAL-PURPOSE MACHINE REPRESENTATIONS USED FOR PROCESSING DATA, HOW DOES REPRESENTATIONAL BIAS PROPAGATE TO DOWNSTREAM APPLICATIONS? (2) HOW ARE HUMANS-IN-THE-LOOP COLLABORATING WITH AI IN DECISION MAKING AFFECTED BY THE BIASED OUTCOMES OF MACHINES,GIVEN THEIR OWN IMPLICIT BIASES? THE RESEARCH STUDIES SUPPORTED BY THIS GRANT WILL BE PUBLISHED IN THE SCIENTIFIC LITERATURE, SPECIFICALLY BY CONTRIBUTING PAPERS ON AN ANNUAL BASIS AT ARTIFICIAL INTELLIGENCE AND FAIRNESS VENUES FOR COMPUTER AND INFORMATION SCIENCES. INTENDED BENEFICIARIES: SOCIOTECHNICAL ARTIFICIAL INTELLIGENCE (AI) SYSTEMS WITH HUMANS-IN-THE-LOOP MAKE CONSEQUENTIAL DECISIONS ABOUT INDIVIDUALS IN SCENARIOS THAT DETERMINE LIFE'S OPPORTUNITIES AND OUTCOMES. DEVELOPING METHODS AND METRICS TO MEASURE HOW THE BIASES OF SOCIETY, AI, AND HUMANS-IN-THE-LOOP INTERACT IN DECISION MAKING WILL PROVIDE QUANTITATIVE EVIDENCE ON THE IMPACT OF AI ON SOCIETY. THIS PROJECT WILL BENEFIT THE GENERAL PUBLIC BY PROVIDING MUCH-NEEDED RESEARCH TO SUPPORT DEVELOPING STANDARDS FOR IDENTIFYING AND MANAGING BIAS IN AI. FAIR WITH HARMFUL BIAS MANAGED IS A KEY CHARACTERISTIC OF TRUSTWORTHY AI AS DEFINED IN NIST'S AI RISK MANAGEMENT FRAMEWORK, THEREFORE THIS BIAS RESEARCH WILL CLEARLY BENEFIT NIST'S LARGER TRUSTWORTHY AND RESPONSIBLE AI PROGRAM AND ADVANCE THE STATE-OF-THE-ART FOR BIAS EVALUATIONS IN AI SYSTEMS. SUBRECIPIENT ACTIVITIES: THERE ARE NO PLANNED SUBAWARDS.