Question.1720 - Exploring the impact of artificial intelligence in decision-making with ethical and legal considerations.
Answer Below:
The 2023 research conducted by WHO claims the technological revolution of artificial intelligence tends to have a detrimental impact on the reliance on human intervention in decision-making within the field of healthcare (Hobbs, 2023). Particularly due to the rapid advancements in technology along with integration with the notion of reducing human error and improving the overall accuracy and credibility of the decisions made, and the underlying truth that it only operates with the data accessible to it. However, there is a lot of potential for the algorithm to breach the limitations to serve the purpose, which draws on several ethical challenges that will be discussed throughout the paper. According to Hobbs (2023), 2024 could experience several legal updates since 2023 already saw Congress hearings pertaining to the growth and infusion of AI in the healthcare sector in order to enact legislation. Which draws in the question that - should AI be employed in healthcare sector to make decisions with robust legal and ethical framework? The 2023 November congress session involved HELP - referred to as Senate Health, Education, Labor, and Pension and the House Energy and Commerce Subcommittee on Health since several hospitals across the states have started their pilot program to reduce physician paperwork, including relying on AI data analytics for better diagnosis in relation to prolonged illness (Hobbs, 2023). When employed with regulated usage, some of the possible benefits are assessing vast array of medical data from the past through cloud technology through dynamic patterns that are beyond human capabilities, which results in more accurate decision making, lower risk of failure or mishap, higher level of accuracy, and personalized treatments within lesser time when compared to humans. While under the Health Insurance Portability and Accountability Act of 1996 (HIPAA), there are several laws enacted in order to restrict the violation of patients privacy under the name of increasing the healthcare efficiency through AI ("Lexisnexis," 2023). When considering process operations AI has been consistent in streamlining the workflow through optimized resource allocation and minimal effort in staffing and equipment distribution to eliminate bias in decision-making, for instance, the AB1502 of California (as introduced by Schiavo) was proposed with the intent to abolish the healthcare services including health insurance from conduction discriminative behavior on the ground of race, gender, national origin, age or disability through the algorithms, with this understanding, regulation like HB203 Georgia's enactment tend to regulate and mitigate the employment of AI particularly in eye assessments, but in other states like Illinois such as HB1002 allows for a regulated utilization of AI to diagnose patients ("Lexisnexis," 2023). Considering an ethical standpoint in terms of assessing AI for decision-making through the lens of deontology that if an act by itself is within the moral bounds, wherein there has been several fictional adaptions that threaten the future with the advent of AI, but it started with the intention of reducing the human effort has led to posing a threat to human society; with ethics as underlying basis to understand the issue in terms of practicality that is either action or rule based when employed to human circumstances, faces the challenge of assessing the utility of every situation, as Immanuel Kant believed that any deed performed with right intent based on hypothetical and categorical imperatives. Although both utilitarian and deontological standpoints tend imply different ethics, they'd agree upon a common point - that the healthcare sector should operate with safety towards all the stakeholders involved, that has incorporated AI, but deontology tends to abide non-consequentially wherein the underlying belief comes from ethics serves as duty in order to incline towards what is referred to as morally right or good, rather than doing things that are wrong. The infusion of AI into the healthcare sector has more potential to do good than to cause havoc since it runs on the data accessible to it. But in practicality, congress works with utilitarianism as discussed, there are several enactments adding to restricting from employing AI or incorporating, some of the other examples - include Illinois's HB3338 which demands the hospitals that provide surgical treatments and other identical facilities defer to human nurse judgement relying on their expertise, then AI ("LexisNexis," 2023). On the contrary, New Jersey's SB1402 outlaws discrimination by utilizing an automated decision system by administrative and financial departments ("LexisNexis," 2023). Now, considering the ethical theories, some folks might argue against the cautious approach taken by deontology and advocate for the benefits that AI in healthcare can bring from a utilitarian standpoint. They may emphasize the potential positive impact on the majority, such as enhanced efficiency, reduced errors, and quicker treatments. In real-world terms, ongoing pilot programs using AI for diagnostics in hospitals have shown promising results, suggesting that the collective benefits might outweigh individual privacy concerns. On the legal side of things, counterarguments might question the need for extensive regulations. Supporters of a more flexible approach could argue that the rapid pace of technological advancement requires adaptability rather than stringent rules. They may express concerns that overly restrictive regulations could impede the development of valuable AI applications in healthcare, affecting areas like telemedicine and access to critical medical information. A concrete example of this debate can be seen in the discussions surrounding specific state legislations. Some argue that strict limitations proposed in certain laws may hinder the progress of telemedicine or delay access to vital medical information. Striking a balance, therefore, becomes crucial in crafting legal frameworks that acknowledge potential benefits while addressing valid ethical concerns. From a deontological perspective, critics might contend that a steadfast adherence to moral duties can be inflexible and may not keep up with the dynamic nature of healthcare and technology. In a scenario of emergency, wherein immediate decision-making is critical, an AI system that rigidly follows deontological principles might face challenges in responding swiftly and flexibly, which questions the credibility of the solution. Considering the tension between patient privacy obligations that seems to be compromised due to the increased exposure to technology and accessibility, as outlined in laws like HIPAA, and the potential benefits of sharing anonymized healthcare data for research (Dove & Phillips, 2015). Finding a middle ground that upholds privacy while contributing to medical advancements is a delicate balancing act that requires careful consideration of deontological principles in the context of a rapidly evolving healthcare landscape. In essence, while ethical and legal frameworks are vital guides, the counterarguments stress the importance of adaptability and a nuanced approach. It's a delicate dance between ensuring ethical considerations and not unnecessarily hindering the potential benefits that AI integration can bring to healthcare. The ongoing dialogue between different ethical perspectives and legal considerations remains crucial for finding a balanced and ethically sound approach to AI implementation in healthcare. From the standpoint of utilitarianism, AI-powered cancer screening programs that analyze vast datasets to identify high-risk individuals, which benefits the majority by detecting cancer early, even if it involves collecting and analyzing personal data without explicit consent from everyone. Individuals, especially from marginalized groups, might fear discrimination based on AI predictions, violating their right to privacy and autonomy. From the standpoint of deontology, considering a scenario an AI system following deontological principles refuses to release anonymized patient data for research despite potential benefits for future treatments, prioritizing individual privacy; with a possible limitation the rigid adherence could hinder medical progress and violate the "duty to do good" principle in critical situations ("Lexisnexis," 2023). In terms of justice, considering a legal case, California AB1502 prohibits healthcare algorithms from discriminating based on protected characteristics that address bias concerns but raises questions about defining and detecting bias in complex algorithms ("LexisNexis," 2023). While considering ethical responsibility, ensuring equitable access to AI-powered healthcare requires addressing affordability, geographical disparities, and potential digital literacy gaps. In terms of legal aspects of data privacy, applying HIPAA to anonymized data used for AI training is complex, as anonymization techniques may not be foolproof (Gabriel, 2023). Balancing patient privacy with the potential benefits of data-driven research in healthcare remains an ongoing discussion. In terms of liability, that raises a question: who is liable in cases of AI-related misdiagnosis? Is it the manufacturer, healthcare provider, or the algorithm itself? The EU General Data Protection Regulation (GDPR) and ongoing US proposals attempt to address liability concerns for AI systems (Chen et al., 2023). Considering intellectual property for ownership, who owns the data generated by AI healthcare systems? Patients, developers, or healthcare institutions? This impacts patient rights and access to data for research while collaborative ownership models and data trusts are being explored to address these issues. Some of the real-world examples include AI-powered chatbots for mental health support. Although the pilot intervention seems to be working smoothly with benefits, including accessibility and anonymity, ethical concerns exist regarding data privacy and the limitations of AI in providing complex emotional support. AI-driven drug discovery harboring a promise for faster development of new medications, but raises concerns about algorithmic bias and potential conflicts of interest between developers and pharmaceutical companies since it relies on the data accessible to it; the trials ran through the judicial system, showed the AI judgments were biased towards black people while giving judgment (Chen et al., 2023). Discussing possible ways to combat algorithmic bias, imagine a scenario where an AI algorithm for cardiovascular risk assessment disproportionately misdiagnoses Black patients (Timmons et al., 2023). In response, IBM and the American College of Cardiology (ACC) partnered to develop a de-biased algorithm (Fan et al., 2023). Through ethical lens this initiative aligns with the principle of justice by addressing bias against marginalized groups and ensuring equitable healthcare access. While utilitarianism might prioritize the overall benefit of early detection, it's crucial to weigh individual cases and avoid perpetuating societal inequities (Fan et al., 2023). From a deontological stance, the partnership adheres to ethical principles of non-discrimination and responsible development, embodying deontological values. It is also equally important to safeguard data privacy; for instance, consider a large hospital implementing federated learning, where machine learning models train on decentralized patient data within each institution without sharing individual records (Icheku, 2011). Also federated learning prioritizes privacy by keeping patient data within hospitals, respecting individual autonomy and informed consent; while utilitarianism might advocate for centralized data access for broader benefits, individual data rights remain paramount (Boch et al., 2023). This approach upholds the deontological principle of protecting patient autonomy by minimizing data-sharing risks. Fostering openness and collaboration for instance, initiatives like MLCommons promote open-source tools and datasets for collaborative AI development in healthcare (Huerta et al., 2022). Open-source development removes barriers to participation, potentially allowing diverse communities to benefit from and contribute to AI advancements, aligning with the principle of justice. By leveraging collective expertise and fostering transparency, open-source approaches aim to maximize societal good, reflecting utilitarian values. Another example, imagine an AI system that diagnoses skin cancer but cannot explain its reasoning (Chanda et al., 2024). XAI initiatives develop systems that can clearly explain their predictions to both doctors and patients. XAI promotes transparency and accountability, empowering medical professionals to understand AI decisions and ensure they align with ethical principles, upholding informed consent and patient autonomy. By exposing probable biases within the system, XAI could mitigate concerns about "black box" algorithms and fosters trust, aligning with the principle of justice (Chanda et al., 2024). Lastly, championing multi-stakeholder governance, with the intent to ethical review the board for AI in healthcare, comprising diverse stakeholders like doctors, patients, ethicists, and legal experts. Multi-stakeholder governance ensures diverse perspectives and values are considered with increased accessibility to dataset for easier incorporation of AI, this move also promotes fairness and inclusivity in terms of development and implementation of automated system with lesser human error in healthcare sector, embodying the principle of justice (Chanda et al., 2024). By providing oversight and guidance, such boards uphold ethical principles and accountability, reflecting deontological values. Possible solutions include, seek insights from bioethicists, legal scholars, and healthcare professionals involved in AI development and implementation. Propose multi-stakeholder collaborations to develop ethical guidelines and regulatory frameworks for responsible AI use in healthcare. Advocate for transparency in AI algorithms, diverse development teams, and ongoing monitoring for potential biases. Implement privacy-enhancing technologies and anonymization techniques that balance data utility with individual rights. References Boch, A., Ryan, S., Kriebitz, A., Amugongo, L. M., & Lütge, C. (2023). Beyond the Metal flesh: understanding the intersection between bio-and ai ethics for robotics in healthcare. Robotics, 12(4), 110. Chanda, T., Hauser, K., Hobelsberger, S., Bucher, T. C., Garcia, C. N., Wies, C., ... & Brinker, T. J. (2024). Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma. Nature Communications, 15(1), 524. Chen, W., Song, C., Leng, L., Zhang, S., & Chen, S. (2023). The Application of Artificial Intelligence Accelerates G Protein-Coupled Receptor Ligand Discovery. Engineering. Dove, E. S., & Phillips, M. (2015). Privacy law, data sharing policies, and medical data: a comparative perspective. Medical data privacy handbook, 639-678. Fan, L., Meng, K., Meng, F., Wu, Y., & Lin, L. (2023). Metabolomic characterization benefits the identification of acute lung injury in patients with type A acute aortic dissection. Frontiers in Molecular Biosciences, 10. Gabriel, O. T. (2023). Data Privacy and Ethical Issues in Collecting Health Care Data Using Artificial Intelligence Among Health Workers (Doctoral dissertation, Center for Bioethics and Research). Hobbs, L. (2023, November 30). Artificial Intelligence & Health Care: State Outlook and Legal Update for 2024 . American Action Forum. https://www.americanactionforum.org/insight/artificial-intelligence-health-care-state-outlook-and-legal-update-for-2024/#:~:text=States%20are%20introducing%20and%20enacting,algorithm%20use%20within%20clinical%20care. Huerta, E. A., Blaiszik, B., Brinson, L. C., Bouchard, K. E., Diaz, D., Doglioni, C., ... & Zhu, R. (2022). FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective. arXiv preprint arXiv:2210.08973. Icheku, V. (2011). Understanding ethics and ethical decision-making. Xlibris Corporation. Lexisnexis. (2023). State Legislators Look to Regulate Use of AI in Healthcare. State Net Insights . https://www.lexisnexis.com/community/insights/legal/capitol-journal/b/state-net/posts/state-legislators-look-to-regulate-use-of-ai-in-healthcare. Timmons, A. C., Duong, J. B., Simo Fiallo, N., Lee, T., Vo, H. P. Q., Ahle, M. W., ... & Chaspari, T. (2023). A call to action on assessing and mitigating bias in artificial intelligence applications for mental health. Perspectives on Psychological Science, 18(5), 1062-1096.More Articles From Business Law
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