Algorithmic Equity: A Framework for Social Applications

Osoba, Osonde A.; Boudreaux, Benjamin D.; Saunders, Jessica; Irwin, J. O.; Mueller, Pam; Cherney, Samantha

doi:10.7249/rr2708

Cited by 11 publications

(8 citation statements)

References 41 publications

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“…Community stakeholders should be involved at all stages of production, including deciding whether an application should be built, setting goals for the model, defining fairness [48], and guarding against unintended consequences after deployment [21,[49][50][51]. Improving governance is usually advocated in the form of guiding principles for AI use [25,52] or "soft governance" such as industry-organized protocols [53,54]. Regulation is not frequently advocated, although it is unclear whether this is because researchers believe regulation would be ineffective or because they prefer to focus on technical solutions.…”

Section: Strategies Are Multipurposementioning

confidence: 99%

Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review

Berdahl¹,

Baker²,

Mann³

et al. 2023

JMIR AI

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Background Emerging artificial intelligence (AI) applications have the potential to improve health, but they may also perpetuate or exacerbate inequities. Objective This review aims to provide a comprehensive overview of the health equity issues related to the use of AI applications and identify strategies proposed to address them. Methods We searched PubMed, Web of Science, the IEEE (Institute of Electrical and Electronics Engineers) Xplore Digital Library, ProQuest U.S. Newsstream, Academic Search Complete, the Food and Drug Administration (FDA) website, and ClinicalTrials.gov to identify academic and gray literature related to AI and health equity that were published between 2014 and 2021 and additional literature related to AI and health equity during the COVID-19 pandemic from 2020 and 2021. Literature was eligible for inclusion in our review if it identified at least one equity issue and a corresponding strategy to address it. To organize and synthesize equity issues, we adopted a 4-step AI application framework: Background Context, Data Characteristics, Model Design, and Deployment. We then created a many-to-many mapping of the links between issues and strategies. Results In 660 documents, we identified 18 equity issues and 15 strategies to address them. Equity issues related to Data Characteristics and Model Design were the most common. The most common strategies recommended to improve equity were improving the quantity and quality of data, evaluating the disparities introduced by an application, increasing model reporting and transparency, involving the broader community in AI application development, and improving governance. Conclusions Stakeholders should review our many-to-many mapping of equity issues and strategies when planning, developing, and implementing AI applications in health care so that they can make appropriate plans to ensure equity for populations affected by their products. AI application developers should consider adopting equity-focused checklists, and regulators such as the FDA should consider requiring them. Given that our review was limited to documents published online, developers may have unpublished knowledge of additional issues and strategies that we were unable to identify.

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Section: Strategies Are Multipurposementioning

confidence: 99%

Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review

Berdahl¹,

Baker²,

Mann³

et al. 2023

JMIR AI

Self Cite

View full text Add to dashboard Cite

show abstract

“… 13 , 14 Precision equity 15 should be an integral part of precision health 16 and health AI. According to the World Economic Forum’s call for more inclusive AI infrastructure, 17 AI scientists and designers “should identify and partner with representatives of these impacted stakeholders on data collection methods, especially when identifying new or non-traditional resources for gathering data.” Algorithmic equity 18 is also an important area that needs special attention to ensure that decisions and policies made based on AI algorithms are nondiscriminatory.…”

Section: The Road Aheadmentioning

confidence: 99%

Applied artificial intelligence in healthcare: Listening to the winds of change in a post-COVID-19 world

Shaban-Nejad

Michalowski

Bianco

et al. 2022

Exp Biol Med (Maywood)

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“…The interactivity between human and machine intelligence also addresses important concerns about bias and errors (Osoba et al, 2019;Osoba and Welser, 2017;Tambe, Cappelli, and Yakubovich, 2019). Humans and ML are both fallible, but they fail in different ways.…”

Section: Leveraging New Forms Of Machine Intelligence To Enhance Deci...mentioning

confidence: 99%

How the U.S. Air Force Can Incorporate New Data Technologies into Its Talent Management System: Framework and Use Cases for Technology-Enabled Talent Management

2022

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Technology-enabled practices have potential to greatly enhance how the U.S. Air Force (USAF) manages its people. These technologies include data management, information technologies, and artificial intelligence (AI). These technologies can enhance human resource management by making available new sources of data and by using AI to extract new information and guide decisions using these data. By adopting technologyenabled practices, the USAF can shift from being governed by broad, standardized policies informed by periodic analyses to a system in which decisions are continually refined to meet goals using the best available information. This report builds on prior research to present a framework to help policymakers understand the distinctive elements of technology-enabled practices, along with descriptions of use cases in each area of talent management. The report closes with a description of three broad implementation challenges and a structured approach to move forward with adoption.This report is one in a set aimed at helping the USAF understand the elements necessary for technologyenabled talent management. Other reports in this set are

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Algorithmic Equity: A Framework for Social Applications

Cited by 11 publications

References 41 publications

Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review

Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review

Applied artificial intelligence in healthcare: Listening to the winds of change in a post-COVID-19 world

How the U.S. Air Force Can Incorporate New Data Technologies into Its Talent Management System: Framework and Use Cases for Technology-Enabled Talent Management

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