2023
DOI: 10.1136/jme-2022-108875
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Addressing bias in artificial intelligence for public health surveillance

Abstract: Components of artificial intelligence (AI) for analysing social big data, such as natural language processing (NLP) algorithms, have improved the timeliness and robustness of health data. NLP techniques have been implemented to analyse large volumes of text from social media platforms to gain insights on disease symptoms, understand barriers to care and predict disease outbreaks. However, AI-based decisions may contain biases that could misrepresent populations, skew results or lead to errors. Bias, within the… Show more

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Cited by 14 publications
(4 citation statements)
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“…Additionally, research indicates that artificial intelligence (AI) may enhance the capacity of public health to advance everyone’s health in every community [ 16 , 17 ]. Public health organizations must carefully consider their AI implementation techniques in order to properly fulfill this promise and apply AI to public health tasks [ 18 , 19 , 20 ].…”
Section: Discussionmentioning
confidence: 99%
“…Additionally, research indicates that artificial intelligence (AI) may enhance the capacity of public health to advance everyone’s health in every community [ 16 , 17 ]. Public health organizations must carefully consider their AI implementation techniques in order to properly fulfill this promise and apply AI to public health tasks [ 18 , 19 , 20 ].…”
Section: Discussionmentioning
confidence: 99%
“…Transparent data usage policies and compliance with the provisions of data protection regulations, such as the General Data Protection Regulation (GDPR) in the European Union and the Health Insurance Portability and Accountability Act (HIPAA) in the United States, are indispensable. Further concerns related to the lack of transparency (the “black box” problem) [ 22 ] and potential biases in AI predictions [ 23 ] may emerge. The unavailability of a clear audit trail for AI predictions could hamper accountability, while bias can inadvertently creep into AI outputs due to skewed representation or inherent prejudices in the training data.…”
Section: Discussionmentioning
confidence: 99%
“…The use of big chemical data and data-driven approaches, however, comes with risks. The problems of biased training data producing biased ML algorithms and, more generally, analysis of biased data producing biased results are well documented across the natural and social sciences. Misleading results will not only confuse and distract scientific progress but also undermine public confidence in science itself. To manage difficulties associated with big data, data scientists have developed several metrics for assessing the reliability and accessibility of results from big data use. The “five Vs” velocity, volume, value, variety and veracityserve as use principles for generating reliable results from big data (Figure ).…”
Section: Chemistry’s Big Data Eramentioning
confidence: 99%