Ethical Considerations in the Use of Artificial Intelligence and Big Data in Infectious Disease Control Among Researchers in Delta State: the Role of Legislation
CHAPTER ONE
Aims and Objectives
The main aim is to consider the various ethical concerns, regarding the translation of AI into healthcare contexts, by drawing on various ethical theories and perspectives.
Other research objectives:
- to examine and fully consider these ethical challenges to ensure that AI is used ethically and effectively in
- to address ethical concerns related to AI in healthcare using the Principlist
- to consider how three influential ethical theories – consequentialism, deontology, and virtue ethics – can inform the development of morally competent AI
- to suggest an ethics of responsibility as a way of complementing existing ethical approaches, and one which is particularly relevant for the application of AI in healthcare
CHAPTER TWO
LITERATURE REVIEW
Conceptual Review
The field of Artificial Intelligence (AI) has experienced radical change in recent years and is now in a position of considerable, and increasing, global relevance and interest. This field is, however, a vast one that defies easy definition, because its scope is continually changing due to rapid development in this area. In this chapter I start by briefly defining artificial intelligence before moving on to the focus of the chapter, and thesis, which is the application of AI in healthcare. After this overview, which introduces some of the ethical concerns that will be discussed in subsequent chapters, I then narrow the focus to explain the specific areas of artificial intelligence systems that elicit ethical concern, namely deep learning and machine learning as well as the risks associated with the ability to generate large datasets.
Defining artificial intelligence
Artificial intelligence (AI) can be broadly defined as the simulation of intellectual processes usually associated with intelligent human cognition, such as learning, decision-making, troubleshooting, problem-solving, executing tasks and self-correction. (10–12) AI is a vast field that encompasses various subfields, including Machine Learning (ML), which involves the development of computer algorithms1 that are designed with the capacity to learn and improve automatically through experience. (2,13) However, the terms AI and ML have fuzzy boundaries which are heavily debated in the literature. Kaplan and Haenlein define AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.” (14) Poole and Mackworth (2010) define the field of AI as focused on “the synthesis and analysis of computational agents that act intelligently.” (15) Here we can understand an agent as an entity capable of action, and an intelligent agent as an entity whose “actions are appropriate for its circumstances and its goal is flexible to changing environments and changing goals…. [can] learn from experience, [and] makes appropriate choices given its perceptual and computational limitations.” (15) While the development of AI systems has numerous applications in various industries (2,13), what is of interest for the focus of this thesis, is its application in health care.
An algorithm is a procedure used for solving a problem or performing a computation task. In AI, an algorithm enables a computer to learn from data and make decisions without explicit programming. AI algorithms in healthcare assist in radiographic image interpretation and skeletal age determination, improving diagnostic accuracy and efficiency.
How can AI be used in healthcare?
The introduction of AI into the healthcare system has the potential to transform how healthcare is delivered. (16) AI can help healthcare professionals make better decisions, improve patient outcomes, and increase healthcare delivery efficiency. More specifically, machine learning and natural (human) language processing2 are two AI technologies that can be used to analyze large amounts of data and extract meaningful insights that can be used to improve clinical decision-making and patient care. (16) AI systems also hold significant potential in aiding early disease diagnose in healthcare settings. For example, AI-driven technologies are trained to analyse medical images to diagnose and identify specific diseases, including being able to differentiate between benign and malignant tumours. (17) In addition, AI-enabled microscopes can scan for harmful microorganisms in blood or fluid samples and monitor viral transmission patterns in real-time, quicker, and more efficiently than manual scanning. In Low- and Middle-Income Countries (LMICs), for example, AI has been used to assist in the detection of tuberculosis by scanning for symptoms and signs of tuberculosis, X-ray scanning, and interpreting staining images, which allows for early identification of the disease. (18,19)
CHAPTER THREE
METHODOLOGY
Overview
As explored in the preceding chapter, the advancement and implementation of artificial intelligence (AI) in healthcare has immense potential to improve the efficiency, accuracy, and precision of disease diagnoses. AI technologies, such as machine learning and deep learning algorithms, can analyze vast amounts of medical data, including patient records, laboratory results, and imaging scans, to extract valuable insights and aid in diagnosing various medical conditions. In this chapter, I use the Principlist framework to discuss some of the ethical concerns that arise in the context of using AI in healthcare. I have chosen this framework because it provides a structured and widely recognized approach to addressing ethical issues, relevant to my focus, such as (1) obtaining consent to store and use data (2), ensuring adequate attention is paid to safety and the need for transparency, (3) algorithmic fairness and awareness of algorithmic biases (4) data security and privacy (5) dignity and solidarity and (6) trust in healthcare and technology. (54–57) As mentioned in the previous chapter, if these concerns are not adequately addressed, patients may be misdiagnosed, and AI could cause harm, including the exacerbation of existing inequities in society. It is crucial to examine and fully consider these challenges to ensure that AI is used ethically and effectively in healthcare. This chapter will address the ethical limitations that pose a significant threat to AI in healthcare.
CHAPTER FOUR
RESEARCH METHODOLOGY
Overview
In the previous chapter, I provided an overview of the ethical concerns associated with the use of AI in healthcare, using the Principlist framework. In this chapter, I build on the technical definitions provided in chapter 2, and some of the points raised in chapter 3, to consider some of issues associated with the use of robots in healthcare and the development of morally competent or ‘ethical’ robots. I also applied the three famous ethical theories namely: consequentialism, deontology, and virtue ethics to analyse the development of a morally competent robots.
CHAPTER FIVE
CONCLUSION AND RECOMMENDATION
Historical Development of Ethics of Responsibility
Max Weber was the first to introduce the notion of an “ethics of responsibility” during his renowned speech, “Politics as a Vocation”, in 1919. (134) However, the German philosopher Hans Jonas expanded upon Weber’s concept and emphasized the “imperative of responsibility.” (80) According to Jonas, this imperative emphasises the importance of considering the future consequences of present actions. (80) By doing so, individuals and societies can take responsibility for their actions and ensure that they do not cause harm or negative consequences to future generations thereby fostering a sustainable and ethically conscious approach to technology and its impact on humanity and the world. The idea of an ethics of responsibility was further developed in different ways by the French phenomenologist Emmanuel Levinas and Polish sociologist Zygmunt Bauman. (135,136) Levinas’ exploration of the ethics of responsibility, emphasizes the primacy of ethical relationships and our responsibility towards one another. (141) Levinas argues that the ethics of responsibility emerges through our interactions with others. According to his perspective, forming ethical relationships with plants and animals is challenging due to their inability to communicate through language or exhibit human-like qualities. He refers to these interactions as “face-to-face” encounters with nonhuman entities. (142) He further posits that the human face carries a unique ethical significance, evoking a call for responsibility and ethical engagement. This encounter disrupts our self-centeredness and demands a response that transcends self-interest. While Levinas’s perspective holds value, it may conflict with contemporary thinkers who argue that current ethical priorities should be centred around the treatment of the natural world, including animals, plants, and the environment. (143) It is crucial to acknowledge this criticism and ensure that any framework built on Levinas’s ideas sufficiently addresses these broader ethical considerations.
Application of these perspectives on the ethics of responsibility
While Levinas, (136) Bauman, (135,136) Jonas, (80) and Butler (137) all address the ethics of responsibility, they do so from distinct perspectives. Levinas stresses the significance of engaging in a personal interaction and the value of establishing an ethical connection with others. He highlights the primacy of face-to-face encounters, the ethical demand they impose, and the transcendence of self- interest. (136) Bauman strongly emphasizes recognising and fulfilling our moral obligations in a rapidly changing and interconnected world. He unambiguously advocates for ethical behaviour that promotes social cohesion and sustainability, with a firm emphasis on acknowledging the consequential impact of our actions. (135,136) Jonas directs attention to environmental and technological considerations (80) whereas Butler focuses on power, social structures, and the politics of identity. Each philosopher offers unique insights into the ethical dimensions of responsibility, but their approaches differ in terms of the emphasis placed on power dynamics, interpersonal encounters, and environmental concerns. (137)
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