AI’s ethical challenges
Perhaps we must ensure that we, as humans, retain the authority to direct AI technologies towards ethical commitments that reflect our deepest values.
Published: 08:01 PM,Jan 15,2025 | EDITED : 12:01 AM,Jan 16,2025
Dr Muamar bin Ali al Tobi.The writer is an academic and researcher
Today’s AI landscape calls for a profound philosophical and ethical review, prompting us to revisit the frameworks that have guided human moral behavior throughout history. Let us consider three such influential approaches: Immanuel Kant’s ethics, John Stuart Mill’s utilitarianism and Aristotle’s virtue ethics. We will attempt to integrate these philosophical standpoints into our understanding of AI, examining how they interact with algorithms capable of learning and making critical decisions.
Kant’s ethics revolve around a key concept known as the ‘Categorical Imperative’ which essentially dictates that an individual should act only in accordance with a rule that can be willed to be a universal law. For Kant, morality does not hinge on outcomes but on the intention of the agent and on whether that intention aligns with moral duty. Hence, goodwill takes precedence as the highest ethical standard: an action is moral if it is performed out of respect for moral law rather than in pursuit of personal gain. Applied to AI, this approach imposes strict conditions on digital algorithms; if we build Kant’s Categorical Imperative into an AI system, it must be guided by ethical principles that treat humanity as an end, never merely as a means. Yet a pivotal question arises: can a machine truly embody goodwill as Kant demands, given that will and intention are properties of conscious moral agents? Since Kant’s focus is on moral intent rather than results, we must ask whether deep-learning algorithms — based on massive datasets and mathematical logic — can genuinely carry “moral intention” or if they remain bound by pre-set pathways, no matter how adaptive they seem.
By contrast, Mill’s utilitarianism posits that moral acts are those that maximise overall happiness, caring only about outcomes rather than intentions. In practice, behavior is judged based on its net utility, even if it sometimes requires sacrificing certain individual interests for the benefit of the larger community. Unlike Kant’s framework, utilitarianism can feel more compatible with how AI works, since algorithms frequently rely on optimisation principles to achieve specific results. However, the crux of the problem lies in conceptualising happiness or utility within diverse societies with differing values.
Who defines happiness? Who decides if one group’s benefit outweighs another’s? Moreover, the data powering AI may contain historical or cultural biases, possibly leading to unequal treatment of various segments of society. Thus, when we shift from the theoretical ideal of ‘maximising happiness’ to real-world application, grave ethical and practical obstacles appear. Programming algorithms purely for utilitarian ends can result in injustices that strip certain groups of their rights. This prompts a deeper ethical dilemma: do we permit these algorithmic systems to prioritise economic gains or political advantages even if that means harming individuals or entire communities?
Aristotle’s approach, on the other hand, places virtue at the heart of ethical philosophy. He maintains that moral persons cultivate good character traits and internal motivations, enabling them to behave ethically and with moderation. Unlike Kant, Aristotle does not emphasise unbreakable duties or rigid outcomes; rather, he focuses on shaping a morally virtuous individual who uses practical wisdom, balancing reason, personal experience and cultural values.
When we turn this lens on AI, we confront the question of whether a digital algorithm can “develop virtues” comparable to those cultivated by humans.
Although modern algorithms use deep learning and reinforcement learning — methods that can resemble educational processes — Aristotle’s virtue requires real-life experience and a refined cognitive ability to act ethically in varied, context-driven scenarios. Deep learning might mimic certain desirable behaviors, but it lacks the fully realised moral discernment that Aristotle insists upon, an understanding that integrates justice, kindness and compassion in real-world contexts. After all, an algorithm merely processes structured data and cannot draw on the rich human experiences and empathetic contexts that underpin moral life.
In reviewing these three philosophies, none seems fully equipped to resolve AI’s ethical challenges. Kant highlights dignity and the primacy of respecting human rights. Mill’s utilitarianism provides a potential framework for evaluating actions by their outcomes, which can be especially relevant in time-sensitive situations, such as self-driving vehicles or drones.
Aristotle’s virtue ethics stresses the importance of moral character, emotional depth and rational thinking, a reminder that ethics cannot be reduced to mere calculations of inputs versus outputs. Yet as AI advances, a deeper question surfaces: what sort of ethical act can a predefined algorithm produce? Lacking Kantian intention deprived of Aristotelian practical wisdom and ignorant of the utilitarian notion of pleasure and pain can we measure AI’s decisions solely through legal codes or social utility? Might an over reliance on machine-driven decisions erode the distinctly human qualities of empathy and moral reflection, which have shaped our collective ethical memory? Perhaps we must ensure that we, as humans, retain the authority to direct AI technologies toward ethical commitments that reflect our deepest values.
Kant’s ethics revolve around a key concept known as the ‘Categorical Imperative’ which essentially dictates that an individual should act only in accordance with a rule that can be willed to be a universal law. For Kant, morality does not hinge on outcomes but on the intention of the agent and on whether that intention aligns with moral duty. Hence, goodwill takes precedence as the highest ethical standard: an action is moral if it is performed out of respect for moral law rather than in pursuit of personal gain. Applied to AI, this approach imposes strict conditions on digital algorithms; if we build Kant’s Categorical Imperative into an AI system, it must be guided by ethical principles that treat humanity as an end, never merely as a means. Yet a pivotal question arises: can a machine truly embody goodwill as Kant demands, given that will and intention are properties of conscious moral agents? Since Kant’s focus is on moral intent rather than results, we must ask whether deep-learning algorithms — based on massive datasets and mathematical logic — can genuinely carry “moral intention” or if they remain bound by pre-set pathways, no matter how adaptive they seem.
By contrast, Mill’s utilitarianism posits that moral acts are those that maximise overall happiness, caring only about outcomes rather than intentions. In practice, behavior is judged based on its net utility, even if it sometimes requires sacrificing certain individual interests for the benefit of the larger community. Unlike Kant’s framework, utilitarianism can feel more compatible with how AI works, since algorithms frequently rely on optimisation principles to achieve specific results. However, the crux of the problem lies in conceptualising happiness or utility within diverse societies with differing values.
Who defines happiness? Who decides if one group’s benefit outweighs another’s? Moreover, the data powering AI may contain historical or cultural biases, possibly leading to unequal treatment of various segments of society. Thus, when we shift from the theoretical ideal of ‘maximising happiness’ to real-world application, grave ethical and practical obstacles appear. Programming algorithms purely for utilitarian ends can result in injustices that strip certain groups of their rights. This prompts a deeper ethical dilemma: do we permit these algorithmic systems to prioritise economic gains or political advantages even if that means harming individuals or entire communities?
Aristotle’s approach, on the other hand, places virtue at the heart of ethical philosophy. He maintains that moral persons cultivate good character traits and internal motivations, enabling them to behave ethically and with moderation. Unlike Kant, Aristotle does not emphasise unbreakable duties or rigid outcomes; rather, he focuses on shaping a morally virtuous individual who uses practical wisdom, balancing reason, personal experience and cultural values.
When we turn this lens on AI, we confront the question of whether a digital algorithm can “develop virtues” comparable to those cultivated by humans.
Although modern algorithms use deep learning and reinforcement learning — methods that can resemble educational processes — Aristotle’s virtue requires real-life experience and a refined cognitive ability to act ethically in varied, context-driven scenarios. Deep learning might mimic certain desirable behaviors, but it lacks the fully realised moral discernment that Aristotle insists upon, an understanding that integrates justice, kindness and compassion in real-world contexts. After all, an algorithm merely processes structured data and cannot draw on the rich human experiences and empathetic contexts that underpin moral life.
In reviewing these three philosophies, none seems fully equipped to resolve AI’s ethical challenges. Kant highlights dignity and the primacy of respecting human rights. Mill’s utilitarianism provides a potential framework for evaluating actions by their outcomes, which can be especially relevant in time-sensitive situations, such as self-driving vehicles or drones.
Aristotle’s virtue ethics stresses the importance of moral character, emotional depth and rational thinking, a reminder that ethics cannot be reduced to mere calculations of inputs versus outputs. Yet as AI advances, a deeper question surfaces: what sort of ethical act can a predefined algorithm produce? Lacking Kantian intention deprived of Aristotelian practical wisdom and ignorant of the utilitarian notion of pleasure and pain can we measure AI’s decisions solely through legal codes or social utility? Might an over reliance on machine-driven decisions erode the distinctly human qualities of empathy and moral reflection, which have shaped our collective ethical memory? Perhaps we must ensure that we, as humans, retain the authority to direct AI technologies toward ethical commitments that reflect our deepest values.