Following the recent WEF Davos 2024 agenda, The Future of Artificial Intelligence (AI), Keyur Patel has discussed the Safe and Ethical AI application development in Public Health.
Keyur is a seasoned AI and Data Engineer expert with over twelve years of industry experience as a solution architect, holding a Master’s in Biomedical Engineering. His academic foundation uniquely blends with a passion for the latest AI developments in public health policies. With a proven track record of successfully managing critical projects in large, distinguished organizations, he brings a wealth of practical knowledge to the intersection of technology and healthcare. This article is offering a deep dive into the dynamic realm of AI in the context of public health policies.
The presence of AI in public health is accelerating as it shows the potential for revolutionizing the healthcare system. With great power, however, comes great responsibility, and the development of AI for the domain of public health requires vigilance in terms of safety and ethics. This review discusses how the safe and ethical development of AI in the healthcare domain can be facilitated by identifying key principles and practices.
First of all, transparency in AI algorithms is important. Developers should make it clear how AI systems work, allowing healthcare professionals and the general population to comprehend decision-making processes by humans. This transparency promotes trust, which enables successful human-AI collaboration and results in a more open and responsible healthcare system.
Privacy is a fundamental issue in public health AI. Data protection mechanisms must be strong and HIPAA compliant since sensitive health information requires high confidentiality levels. Privacy-preserving techniques like federated learning and differential privacy can help prevent data breaches, unauthorized access, etc.
Bias in AI algorithms is a major ethical issue. Biases related to factors such as race, gender, and socioeconomic status should be minimized during designing and testing of public health applications. To address these issues, bias mitigation strategies such as diverse and representative datasets, algorithmic fairness evaluations, and continuing monitoring are necessary.
Interpretability is another important component of ethical AI development in the public health sector. The outputs of AI models should be interpretable and validated by healthcare professionals, who should understand the logic behind recommendations. This interpretability does not only provide accountability but also allows for the improvement of AI algorithms based on real-world feedback.
Human values and preferences must be integrated into public health AI. Developers need to actively interact with healthcare professionals and communities in order to understand the different views and needs. This participatory approach assists in developing AI technologies that are consistent with the public’s values while making sure that the applications are culturally sensitive and contextually relevant.
It is crucial to monitor and evaluate AI continuously in public health to measure its performance and consequences. By conducting regular audits and assessments, emerging ethical issues and technical problems can be identified and resolved to ensure a perpetual cycle of improvement and creativity.
In summary, responsible and ethical AI development in public health settings demands an inclusive approach that takes into account transparency, privacy protection, bias control, interpretability of results and human values. If developers follow these principles, the full potential of artificial intelligence can be used to increase the effectiveness of health care while preserving individual rights and welfare. As public health embraces AI technologies, the commitment to safety and ethics will play a significant role in creating a more just future that is healthy.
