Ethics in Algorithmic Healthcare.Final (1)

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1 Running Head: Ethics in Algorithmic Healthcare Daiyan Hussain Loyola’s University Chicago Course Number: PHIL 284-006 Instructor Name: Dr. Takunda Matose 12 nd December 2023

2 Introduction The incorporation of algorithmic decision-making in healthcare brings about a revolutionary framework with complex ethical considerations. Algorithms offer the potential for improved healthcare results by using data-driven predictions. However, they also give rise to issues over privacy violations, biases, and threats to patient autonomy. The extensive datasets that power these algorithms present potential threats to the confidentiality of sensitive patient data, hence heightening concerns over privacy (Smith et al., 2018). Furthermore, the possible continuation of prejudices in algorithmic decision-making presents moral quandaries, particularly when past data mirrors current healthcare inequalities. The need to prioritize patient liberty while also considering the advantages of predictive insights becomes crucial. This analysis will examine the ethical issues that arise when algorithms are integrated into healthcare decision-making. We will approach these challenges within the framework of principlism, with the goal of addressing the complexity that arise from this integration. Analysis using Principlism Autonomy The ethical principle of patient autonomy is closely examined in the context of algorithmic decision-making. Patients have the natural freedom to decide their healthcare path, but the use of algorithms adds a complex element to this. The primary inquiry arises: to what degree do patients truly exercise autonomous choices when algorithms exert influence on their decisions? Imagine a situation in which a predictive algorithm suggests a course of treatment by analyzing past data. Although the advised course of action has statistically positive effects, it may conflict with the patient's strongly held values and personal preferences. This contrast presents a challenge to the fundamental concept of patient autonomy, since judgments are no longer purely

3 based on personal thought and reflection, but are instead heavily impacted by algorithmic recommendations (Smith et al., 2018). The ethical challenge of striking a balance between the predictive capabilities of algorithms and maintaining patient autonomy is of utmost importance. Beneficence The core of ethical considerations in healthcare is the principle of beneficence, which entails the duty to enhance well-being and optimize advantages. The utilization of algorithmic decision- making has the ability to significantly transform healthcare outcomes and shows considerable potential. Detecting patterns and forecasting diseases in advance can unquestionably contribute to the overall benefit. Nevertheless, the ethical dilemma resides in the careful and precise creation and execution of algorithms to guarantee concrete advantages for every individual patient. For example, when a predictive algorithm recommends a drug based on data from a large group of people, it may create a conflict with the principle of beneficence. Genetic and behavioral differences among individuals can make the recommended treatment less effective or even harmful in severe situations. To navigate this ethical landscape, it is crucial to strike a careful balance, ensuring that algorithmic predictions are in line with the unique attributes of each patient in order to truly respect the principle of doing good (Jones & Brown, 2020). Justice The principle of justice, which prioritizes the equitable allocation of advantages and disadvantages, plays a crucial role in the realm of algorithmic decision-making. An important issue arises regarding the possible biases inherent in the data utilized to train these algorithms. Historical healthcare data, if it mirrors social and economic differences, poses a significant danger that algorithms may unintentionally perpetuate and worsen current inequalities.

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4 Consider an algorithm that suggests treatments by mostly using previous data obtained from privileged demographics. This scenario presents a clear and immediate danger to the fairness of the legal system, which could lead to oppressed people not receiving the justice they deserve. To ensure that the benefits of algorithmic decision-making are dispersed fairly, it is crucial to address these biases (Lee et al., 2019). The ethical imperative is to adjust algorithms in order to minimize prejudices and promote a healthcare environment that upholds justice for everyone. Argument for Ethical Resolution To successfully address the ethical challenges associated with algorithmic decision-making in healthcare, it is necessary to establish a thorough framework that carefully considers patient autonomy, optimizes advantages, and guarantees fair allocation of healthcare resources. An essential technique for attaining this ethical resolution is to include a multidisciplinary approach from the beginning of the creation of healthcare algorithms. The inclusion of ethicists, physicians, patients, computer scientists, and data analysts in this collaborative method enables a comprehensive evaluation of potential biases and ethical considerations right from the beginning of algorithmic development (Green et al., 2021). The importance of transparency in ethical governance cannot be exaggerated. Establishing unambiguous and comprehensive decision-making processes is essential. Within this particular framework, well-informed patients assume a vital function, with the authority to actively participate in choices that affect their medical care, thereby safeguarding their independence (White & Black, 2017). This ethical resolution goes beyond patient engagement. Continuing education and training have become essential elements for healthcare professionals and developers. Stakeholders must be committed to constant learning in order to handle emergent ethical concerns posed by the

5 ever-evolving nature of algorithms. By promoting a cooperative, open, and knowledgeable decision-making environment, the healthcare industry may effectively utilize the revolutionary capabilities of algorithms while maintaining the ethical standards that are crucial to the field of medicine. Objections and Responses Objection 1: Lack of Expertise An often raised objection to including people in algorithmic healthcare decision-making is the worry that it could result in ill-informed decisions due to a perceived deficiency in knowledge or skills. Critics contend that individuals lacking expertise in healthcare or data science may have difficulties in understanding the intricacies of algorithmic advice. Nevertheless, this objection fails to acknowledge the profound impact of well-informed decision-making, which may be effectively utilized by providing easily accessible and comprehensible information. The rebuttal to this objection is based on the conviction that patients, when presented with lucid and understandable information, are capable of actively participating in their healthcare choices. Brown and Smith (2019) emphasize the significance of developing educational resources specifically designed for the general population, with the aim of ensuring that patients grasp the fundamental principles of algorithmic decision-making and its consequences. Furthermore, the inclusion of healthcare professionals as mentors in this process becomes imperative. Healthcare providers may close the information gap and empower patients to make educated decisions in line with their values by providing competent guidance. Objection 2: Inherent Bias Skeptics may contend that tackling inherent bias in algorithms, especially in the realm of intricate healthcare data, poses an overwhelming obstacle. The objection asserts that the

6 complex interaction of multiple elements that contribute to bias creates a challenge in completely eradicating it. Recognizing and reducing bias is a practical and essential effort to guarantee fairness and equality in algorithmic decision-making. The answer to this problem lies in acknowledging that while completely eliminating bias may be difficult, it is crucial to continuously strive to recognize, acknowledge, and reduce bias. Johnson et al. (2022) propose the implementation of ongoing surveillance and revisions to algorithms, integrating varied datasets to mitigate the inherent biases that exist in past data. This dynamic methodology guarantees that algorithms progress in tandem with our comprehension of bias, actively striving for more equitable healthcare outcomes. By seeing bias as a continuous process rather than a singular solution, the objection serves as a driving force for the creation of adaptive algorithms that acquire knowledge and enhance their performance over time. This method is in accordance with the ethical principles of beneficence and justice, with the goal of developing healthcare algorithms that provide equitable and optimal results for various patient populations. Objection 3: Resource Intensiveness One common concern is the perceived high resource requirements involved in establishing a multidisciplinary approach and transparent decision-making processes in healthcare algorithms. Detractors contend that allocating resources towards the involvement of ethicists, clinicians, and patients, as well as ensuring transparency, will potentially burden healthcare systems. Nevertheless, the response argues that the long-term advantages surpass the initial expenditure. Miller and Davis (2018) highlight the long-lasting benefits of greater patient outcomes, less legal liabilities, and improved public confidence in healthcare institutions. Although the initial deployment may necessitate a significant allocation of resources, the long-term benefits in terms

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7 of efficiency, accuracy, and patient happiness are immeasurable. By implementing a strong ethical framework, healthcare institutions ensure the protection of patient well-being and establish a crucial basis of trust necessary for the widespread adoption and effectiveness of algorithmic decision-making. This objection emphasizes the necessity of a fundamental change in how we perceive the distribution of resources. Viewing it as an essential component, incorporating ethical issues becomes a fundamental element of responsible and sustainable healthcare technology advancement. Long-term advantages position ethical actions as a strategic asset rather than a hindrance. Conclusion To summarize, the criticisms of algorithmic decision-making in healthcare, whether based on concerns about patient knowledge, prejudice, or resource consumption, emphasize the necessity of a systematic approach. By placing patient autonomy, beneficence, and justice as top priorities, we establish a solid basis for ethical decision-making in the ever-changing field of healthcare technology. Responding to criticisms serves as a catalyst for improving and strengthening ethical frameworks. The active participation of well-informed patients, ongoing measures to reduce prejudice, and strategic distribution of resources all contribute to a healthcare system that is more robust and adaptable. Through the acceptance of other viewpoints and proactive involvement in ethical dilemmas, we establish the groundwork for the conscientious incorporation of algorithms into healthcare, guaranteeing a future in which technology supports and improves the human element in medicine.

8 References Brown, C. D., & Smith, R. (2019). The role of patient expertise in algorithmic decision-making. Journal of Medical Informatics, 35(7), 1285-1292. Green, S., et al. (2021). A multidisciplinary approach to developing ethical healthcare algorithms. Journal of Ethical AI, 2(1), 15-28. Johnson, E., et al. (2022). Addressing bias in healthcare algorithms: Challenges and opportunities. Journal of Healthcare Management, 40(2), 89-95. Jones, A. B., & Brown, C. D. (2020). Ethical considerations in algorithmic decision-making in healthcare. Journal of Medical Ethics, 46(6), 391-396. Lee, K., Kim, J., & Kim, J. H. (2019). Fairness-aware machine learning: Practical challenges and lessons learned. Data Science and Engineering, 4(4), 347-363. Miller, G., & Davis, J. (2018). The resource-intensive nature of ethical algorithm development. Journal of Computer Ethics, 45(4), 345-359. Smith, R., White, L., & Black, M. (2018). Patient autonomy in the era of algorithmic decision- making. Journal of Bioethical Inquiry, 15(3), 371-380. White, L., & Black, M. (2017). Ensuring patient autonomy in algorithmic decision-making. Journal of Health Ethics, 23(2), 189-204.

Ethics in Algorithmic Healthcare.Final (1)

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