Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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Despite increased efforts to ensure diversity in genomic research, the exclusion of minority groups from data analyses and publications remains a critical issue. This paper addresses the ethical implications of these exclusions and proposes accountability for reasonableness (A4R) as a framework to promote fairness and equity in research. Originally conceived by Norman Daniels and James Sabin to guide resource allocation in the context of health policy, A4R emphasizes publicity, relevance of reasons, enforcement, and revision as essential for legitimacy and trust in the decision-making process. The authors argue that A4R is also relevant to resource allocation in research and that, if adequately informed and incentivized by funding agencies, institutional review boards, and scientific journals, researchers are well-positioned to assess data-selection justifications. The A4R framework provides a promising foundation for fostering accountability in genomics and other fields, including artificial intelligence, where lack of diversity and pervasive biases threaten equitable benefit sharing.
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Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11662771 | PMC |
http://dx.doi.org/10.1002/hast.4931 | DOI Listing |