AI Ethics Through a Kantian Deontological Lens

Introduction

Today, the proliferation of artificial intelligence (AI) has revolutionized numerous industries through the automation of a wide array of labor-intensive tasks, as well as through the facilitation of decision-making processes in ways that encourage innovation. This essay explores the positive and negative impacts of AI technologies on individuals and groups from the perspective of Immanuel Kant's deontological ethics. A deontological approach is particularly relevant for this type of analysis because it focuses on adherence to ethical principles and the intrinsic morality of actions, including a so-called "duty of beneficence" — the obligation to act in the interests of others so as to maximize present and future benefits while minimizing harms for all stakeholders (Mansell, 2019).

Using this approach, the essay examines the historical context of AI technologies and analyzes what is currently taking place as increasing numbers of public and private sector organizations seek to leverage AI's benefits while minimizing its adverse impacts. An analysis of future implications is then followed by a series of recommendations concerning the responsible use of these technologies going forward.

Historical Context of Artificial Intelligence

The concept of artificial intelligence has roots dating back to classical philosophers who explored the idea of mechanical reasoning. Modern AI, however, began to take shape during the mid-20th century when Alan Turing proposed the Turing Test in 1950 as a measure of machine intelligence (Saha et al., 2024). The "official birth" of AI as a field of scientific study is traced to the 1956 Dartmouth Conference (Moor, 2016). At this conference, Professor John McCarthy brainstormed proposals with colleagues for the future use of these technologies (Artificial Intelligence Coined at Dartmouth, 2024).

While the Dartmouth Conference helped generate increased interest and funding for AI research during the 1960s, that initial enthusiasm diminished in subsequent years, resulting in periods known as "AI winters" (Chun & Elkins, 2022). As computer processing speeds continued to follow the dictates of Moore's Law, a resurgence of interest emerged in newly developed expert systems during the 1980s, along with significant breakthroughs in machine learning during the 1990s (Chun & Elkins, 2022).

Given these sustained improvements in computing capacity, it is little wonder that the 21st century has witnessed exponential growth in AI capabilities, driven by increases in computing power, the expanding availability of big data, and significant improvements in algorithmic design (Asker et al., 2022). Besides routinely passing the Turing Test, other notable advancements include IBM's supercomputer Deep Blue, which was already capable of processing 200 million different chess moves per second in 1997 — a capability that allowed it to defeat then-world chess champion Garry Kasparov that same year (Deep Blue, 2024). More recently, Google's AlphaGo likewise succeeded in defeating the world's top Go player in 2016 (Ribeiro, 2016). In sum, the past 75 years of AI history have mirrored the remarkable trajectory of aviation, moving from modest beginnings to near-infinite possibilities, and the current situation holds even greater promise for the future.

Current Situation

The cumulative benefits of AI have already produced multiple impressive advances for humankind, most notably in the automation of complex tasks and in healthcare, where AI has demonstrated its ability to help diagnose illnesses and assist in delicate surgical procedures (Lutenco et al., 2024). Other innovations include autonomously driven vehicles such as safer and more fuel-efficient commercial trucks. In this regard, Katreddi et al. (2022) emphasize that "artificial intelligence shows promising results in the trucking industry for increasing productivity, sustainability, reliability, and safety" (p. 7457).

Recent advancements in AI have become increasingly integrated into the systems people use to live, work, recreate, and learn. These advancements have, however, also introduced important ethical questions concerning the adverse effects of AI on individual privacy, a "neo-Luddite" resistance to technologies that replace human jobs, and the safety of using AI-enabled systems to make the kinds of high-stakes decisions that critics argue should remain reserved for humans (Ghoshal, 2023). Indeed, the use of realistic AI-generated images has already influenced political and social discourse worldwide. These concerns will likely continue to intensify as AI infiltrates even more aspects of daily life.

Conclusion

The research showed that the AI genie is out of the bottle and there is no turning back. The rapid advancement of artificial intelligence has ushered in a new era of technological innovation, bringing both unprecedented opportunities and complex ethical challenges. Using a deontological perspective, this essay examined the historical context, current situation, and future implications of AI, with an emphasis on the importance of conforming to ethical principles that respect human autonomy.

The research consistently demonstrated that as AI continues to permeate various aspects of life — from healthcare to transportation — it is crucial to address the ethical concerns it raises. The recommendations offered here provide a useful, though not exhaustive, framework for responsible AI development, deployment, and use. These recommendations emphasize respect for human autonomy, the establishment of universal ethical standards, non-discrimination in algorithmic design, and the importance of informed consent in order to maximize the benefits of AI both now and in the future.

References

Artificial Intelligence Coined at Dartmouth. (2024). Dartmouth University. Retrieved from

Asker, J., Fershtman, C., & Pakes, A. (2022). Artificial intelligence, algorithm design, and pricing. AEA Papers & Proceedings, 112, 452–456.

Chun, J., & Elkins, K. (2022). What the rise of AI means for narrative studies: A response to "Why computers will never read (or write) literature" by Angus Fletcher. Narrative, 30(1), 104–113.

Deep Blue. (2024). IBM. Retrieved from

Fest, I. C. (2023). Jérôme Duberry (2022) Artificial intelligence and democracy: Risks and promises of AI-mediated citizen-government relations. Edward Elgar: Cheltenham. Information Polity: The International Journal of Government & Democracy in the Information Age, 28(3), 435–438.

Ghoshal, A. (2023). Microsoft seeks US agency for AI governance, lays out strategy. Computerworld (Online Only), 1.

Katreddi, S., Kasani, S., & Thiruvengadam, A. (2022). A review of applications of artificial intelligence in heavy duty trucks. Energies, 15(20), 7457.

Lutenco, V., Cocu, G., Guliciuc, M., Moraru, M., Candussi, I. L., Danila, M., Lutenco, V., Dimofte, F., Mihailov, O. M., & Mihailov, R. (2024). New horizons of artificial intelligence in medicine and surgery. Journal of Clinical Medicine, 13(9), 2532.

Mansell, S. (2013). Shareholder theory and Kant's "duty of beneficence." Journal of Business Ethics, 117(3), 583–599.

Moor, J. (2016). The Dartmouth College Artificial Intelligence Conference: The next fifty years. AI Magazine, 27(4), 87–91.

Podichetty, J. T., Sardar, S., Henscheid, N., Lee, G. V., Abrams, J. R., Anderson, W., Ma, S. C., & Romero, K. (2024). Accelerating healthcare innovation: The role of artificial intelligence and digital health technologies in Critical Path Institute's public-private partnerships. Clinical and Translational Science, 17(6), e13851.

Ribeiro, J. (2016). Google DeepMind's AlphaGo AI system wins first round against top human Go player. Good Gear Guide, 7.

Saha, D. F., Brooker, P., Mair, M., & Reeves, S. (2024). Thinking like a machine: Alan Turing, computation and the praxeological foundations of AI. Science & Technology Studies, 37(2), 66–88.