# Does "AI" stand for augmenting inequality in the era of covid-19 healthcare?

arXiv: https://arxiv.org/abs/2105.07844 · HTML: https://mlsanity.com/paper/2105.07844/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 47 references that are in the map.
- First posted on arXiv 30 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Problem Formulation and Fairness](https://mlsanity.com/paper/1901.02547/) (2019)
- [A Review of Challenges and Opportunities in Machine Learning for Health](https://mlsanity.com/paper/1806.00388/) (2018)
- [COVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model](https://mlsanity.com/paper/2007.10286/) (2020)
- [Tackling COVID-19 through Responsible AI Innovation: Five Steps in the Right Direction](https://mlsanity.com/paper/2008.06755/) (2020)
- [A Survey on the Use of AI and ML for Fighting the COVID-19 Pandemic](https://mlsanity.com/paper/2008.07449/) (2020)

## Built on by (most cited first, 10 of 10)

- [Detecting Shortcut Learning for Fair Medical AI using Shortcut Testing](https://mlsanity.com/paper/2207.10384/) (2022)
- [Learning from data with structured missingness](https://mlsanity.com/paper/2304.01429/) (2023)
- [Data-SUITE: Data-centric identification of in-distribution incongruous examples](https://mlsanity.com/paper/2202.08836/) (2022)
- [Beyond Model Interpretability: Socio-Structural Explanations in Machine Learning](https://mlsanity.com/paper/2409.03632/) (2024)
- [Quantifying Health Inequalities Induced by Data and AI Models](https://mlsanity.com/paper/2205.01066/) (2022)
- [Computationally Assisted Quality Control for Public Health Data Streams](https://mlsanity.com/paper/2306.16914/) (2023)
- [SoK: Security and Privacy Risks of Healthcare AI](https://mlsanity.com/paper/2409.07415/) (2024)
- [Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairness](https://mlsanity.com/paper/2407.03133/) (2024)
- [Agentic AI for Scaling Diagnosis and Care in Neurodegenerative Disease](https://mlsanity.com/paper/2502.06842/) (2025)
- [Toward Fairness in Machine Learning Models for Predicting Treatment Retention and Premature Discontinuation in Medication for Opioid Use Disorder](https://mlsanity.com/paper/2609.22113/) (2026)

## Abstract (arXiv)

Among the most damaging characteristics of the covid-19 pandemic has been its disproportionate effect on disadvantaged communities. As the outbreak has spread globally, factors such as systemic racism, marginalisation, and structural inequality have created path dependencies that have led to poor health outcomes. These social determinants of infectious disease and vulnerability to disaster have converged to affect already disadvantaged communities with higher levels of economic instability, disease exposure, infection severity, and death. Artificial intelligence (AI) technologies are an important part of the health informatics toolkit used to fight contagious disease. AI is well known, however, to be susceptible to algorithmic biases that can entrench and augment existing inequality. Uncritically deploying AI in the fight against covid-19 thus risks amplifying the pandemic's adverse effects on vulnerable groups, exacerbating health inequity. In this paper, we claim that AI systems can introduce or reflect bias and discrimination in three ways: in patterns of health discrimination that become entrenched in datasets, in data representativeness, and in human choices made during the design, development, and deployment of these systems. We highlight how the use of AI technologies threaten to exacerbate the disparate effect of covid-19 on marginalised, under-represented, and vulnerable groups, particularly black, Asian, and other minoritised ethnic people, older populations, and those of lower socioeconomic status. We conclude that, to mitigate the compounding effects of AI on inequalities associated with covid-19, decision makers, technology developers, and health officials must account for the potential biases and inequities at all stages of the AI process.