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Unveiling Health Equity Harms and Biases in Large Language Models


Core Concepts
Large language models can introduce health equity harms, necessitating evaluation and mitigation strategies.
Abstract
Large language models (LLMs) have the potential to both serve complex health information needs and exacerbate health disparities. Evaluating equity-related model failures is crucial for developing systems that promote health equity. This work presents resources and methodologies for identifying biases in LLM-generated medical answers, including a multifactorial framework for human assessment and newly-released datasets enriched for adversarial queries. Through an empirical study, diverse assessment methodologies surface biases missed by narrower approaches, emphasizing the importance of involving raters of varying backgrounds. While the framework identifies specific bias forms, it's not sufficient to holistically assess equitable health outcomes.
Stats
Reliably evaluating equity-related model failures is crucial. The study involved 806 raters across three distinct groups. Over 17,000 human ratings were conducted. The dataset includes 4,668 examples across seven datasets. The study utilized eleven physician raters and nine health equity expert raters.
Quotes
"We emphasize that while our framework can identify specific forms of bias, it is not sufficient to holistically assess whether the deployment of an AI system promotes equitable health outcomes." "Our experience underscores the importance of using diverse assessment methodologies and involving raters of varying backgrounds and expertise." "If models were widely used in healthcare without safeguards, the resulting equity-related harms could widen persistent gaps in global health outcomes."

Deeper Inquiries

How can LLMs be effectively evaluated to mitigate potential equity-related harms?

To effectively evaluate Large Language Models (LLMs) and mitigate potential equity-related harms, a multifaceted approach is essential. Here are some key strategies: Multifactorial Assessment Rubrics: Develop assessment rubrics that cover various dimensions of bias, including inaccuracy for different axes of identity, lack of inclusivity, stereotypical language or characterization, failure to challenge biased premises, and more. These rubrics should be designed through iterative processes involving experts in health equity. Diverse Dataset Creation: Create diverse datasets like EquityMedQA that include adversarial questions enriched for equity-related content. These datasets should cover a wide range of topics and contexts relevant to health disparities. Human Evaluation with Diverse Rater Groups: Involve diverse rater groups such as physicians, health equity experts, and consumers in the evaluation process. This diversity ensures varied perspectives are considered when assessing biases in LLM-generated content. Counterfactual Testing: Use counterfactual evaluation methods where questions differ only in identifiers of demographics or context to understand how changes impact model responses based on these factors. Iterative Review Processes: Continuously review model failures and refine assessment methodologies based on real-world examples to improve the effectiveness of evaluations over time. Transparency and Accountability: Ensure transparency in evaluation processes by documenting methodologies used and sharing results openly to promote accountability within the AI community.

What are the implications of relying on narrow evaluation approaches for assessing biases in LLM-generated content?

Relying solely on narrow evaluation approaches for assessing biases in LLM-generated content can have several implications: Limited Scope: Narrow evaluations may overlook certain dimensions or forms of bias present in the model outputs due to focusing on specific aspects only. Incomplete Understanding: Without considering a broad range of factors that contribute to bias, there is a risk of incomplete understanding regarding how biases manifest across different axes of identity or contexts. False Sense of Security: Narrow evaluations may provide a false sense of security about the fairness and accuracy of LLMs if they fail to uncover hidden biases that could potentially lead to harmful outcomes. 4Lack Of Generalizability: Evaluations limited by narrow criteria may not capture the full spectrum 0of potential issues present across diverse populations or use cases where models might be deployed.

How can involving diverse rater groups improve the identification 0of biasesin AI systems?

Involving diverse rater groups plays a crucial role n improvingthe identification fbiasesinAI systemsfor several reasons: 1**Varied Perspectives: Different raters bring unique perspectives basedontheir professional backgrounds,lived experiences,and cultural insights.This diversity helps identifya widerange fbiasesandensures comprehensiveevaluationofmodeloutputs. 2**Reduced Bias Blind Spots: Diverse ratergroupsaremore likelyto catchbiasesthatmightbe overlookedbyahomogeneous group.By incorporating arangeoexpertiseandbackgrounds,raterscanidentifybiaseseffectivelyacrossdifferentaxesidentityorcontexts. 3**Cultural Sensitivity: Ratersfromdiversebackgroundscanrecognizecultural nuancesandcontextual informationthatmay influencehowbiasesmanifestinAIgeneratedcontent.Thiscanleadtoa more nuancedunderstandingoffactorscontributingtobias. 4*Enhanced Fairness: Inclusiveparticipationbydiverseraterrgroupshelpsensurefairnessandinclusivityinthemodelassessmentprocess.ItalsodemonstratesavaluetoallstakeholdersinvolvedinthedevelopmentanddeploymentoAItechnologies.
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