Abstract
Although Artificial Intelligence (AI) is being increasingly applied, considerable distrust about introducing “disruptive” technologies persists. Intrinsic and contextual factors influencing where and how such innovations are introduced therefore require careful scrutiny to ensure that health equity is promoted. To illustrate one such critical approach, we describe and appraise an AI application – the development of computer assisted diagnosis (CAD) to support more efficient adjudication of compensation claims from former gold miners with occupational lung disease in Southern Africa. In doing so, we apply a bio-ethical lens that considers the principles of beneficence, non-maleficence, autonomy and justice and add explicability as a core principle. We draw on the AI literature, our research on CAD validation and process efficiency, as well as apprehensions of users and stakeholders. Issues of concern included AI accuracy, biased training of AI systems, data privacy, impact on human skill development, transparency and accountability in AI use, as well as intellectual property ownership. We discuss ways in which each of these potential obstacles to successful use of CAD could be mitigated. We conclude that efforts to overcoming technical challenges in applying AI must be accompanied from the onset by attention to ensuring its ethical use.
Highlights
The COVID-19 pandemic has sharpened attention to the potential for innovative uses of Artificial Intelligence (AI) to provide timely access to information while minimizing personal exposures to the virus [2]. To illustrate what this can mean for a marginalized population, we focus here on an AI application in the form of computer aided detection (CAD) of silicosis and tuberculosis in active and former gold miners suffering from occupational lung disease in Southern Africa
Our perspective is based on our CAD research; the general AI implementation literature; clinical site visits; and a workshop with those responsible for the statutory compensation process and members of the Tshiamiso Trust, set up following the class action suit [30]. It draws on our interaction with involved health practitioners, government decision-makers, AI vendors, and interested parties representing both workers and mining company interests, including in a final Zoom-workshop of over 50 participants to discuss the results presented here [33, 38]
As a specific example of this, we note that fewer than 10% of the high-risk workforce are women, and less than 2% of the claims in the Medical Bureau for Occupational Diseases (MBOD) database for silicosis and/or TB to date are from women
Summary
At a moment when the need to turn to online solutions is greater than ever, and artificial Intelligence. Even when the ability of the AI technology to improve health equity has been demonstrated, the process by which an AI application is introduced and implemented still merits attention, recognizing that the need for investment in such pursuits may well prompt a comparative neglect of economically marginalized parties’ priorities While it is not uncommon for AI technology itself to be developed with the aid of public sector resources, either directly in the development process or through sharing of datasets for training purposes or validation, subsequent commercialization and implementation by private companies can lead to barriers to subsequent access by potential beneficiaries with limited economic resources. This puts the spotlight on the timely use of technological innovations to benefit those in need
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