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Digital Philosophy

Ethics for the Age of AI

Mahmoud Khatami asks, can machines make good moral decisions?

Imagine a self-driving car speeding down a narrow road when suddenly a child runs into its path. The car must decide: swerve and risk the passenger’s life, or stay the course and endanger the child? This real-world dilemma echoes the classic ‘trolley problem’ in ethics, and highlights the ethical challenges of AI. Similarly, AI systems in healthcare diagnose diseases and recommend treatments, sometimes making life-or-death decisions. But can machines truly understand right from wrong? What happens when they make mistakes, or reflect their creators' biases? As AI integrates into our lives, it will both transform how we work and challenge our understanding of morality. From facial recognition to loan approvals, AI makes decisions once reserved for humans; yet machines lack empathy, context, or moral reasoning. This raises profound questions: Can AI make ethical decisions? Should it? And how do we ensure it serves humanity’s values, not undermines them?

This article explores AI’s ethical challenges, including bias, privacy, and accountability. These issues, from fairness to trust, impact everyday life. So as we navigate this new era, we must ask: How can we ensure AI aligns with our ethical principles?

The future of AI is not just technological: it’s deeply moral.

The Ethics of AI Decision-Making

At the heart of the debate over artificial intelligence ethics lies a fundamental question: what does it mean for a decision to be ethical?

For humans, ethical decision-making involves weighing values, considering consequences, and often navigating complex dilemmas. It requires empathy, intuition, and an understanding of context – qualities that are deeply rooted in our experiences and emotions. But can a machine, no matter how advanced, replicate this process? And even if it can, should it?

Ethical decisions are rarely black and white. They often involve balancing competing principles, such as fairness, justice, and the greater good. For example, should a doctor prioritize saving the life of a young patient over an elderly one? Should a judge impose a harsher sentence to deter future crimes, even if it seems unfair to the individual? These questions highlight the complexity of morality, which is shaped by cultural norms, personal beliefs, and situational factors. Translating this complexity into algorithms is no small feat, especially as machines lack the ability to feel empathy or to understand the nuances of human experience.

Despite these challenges, AI is being increasingly used in high-stakes scenarios where ethical decisions are unavoidable. For example, self-driving cars must make split-second choices in emergencies – such as between swerving to avoid a pedestrian or risking harm to passengers. Should the car prioritize passenger safety, or minimize overall harm? And who decides? Similarly, in healthcare AI diagnoses diseases and recommends treatments; but what happens if it makes a mistake? Can (or should) we trust machines with life-or-death decisions, especially when their reasoning is often inscrutable? In criminal justice, predictive policing tools identify potential criminals and sometimes determine sentencing. However, these systems often reinforce biases, such as racial or socioeconomic discrimination, by being trained on or otherwise relying on historical data that reflect past injustices. This raises further critical questions about fairness: Can algorithms ever be truly impartial, or will they always mirror their creators’ biases? Or an AI system might prioritize efficiency over fairness, or fail to recognize individual circumstances. This underscores the need for human oversight and careful ethical consideration, as machines lack compassion or cultural awareness.

To better understand the ethical challenges of AI, we can turn to the insights of Immanuel Kant and John Stuart Mill. Kant’s deontological ethics emphasizes the following of moral rules or duties, regardless of the consequences. From a deontological perspective, an AI system might be programmed to always prioritize human life, even if it leads to what in some senses are less efficient outcomes. In contrast, Mill’s utilitarianism focuses on maximizing overall happiness and minimizing harm. A utilitarian approach might program an AI system to make decisions that result in the greatest good for the greatest number, even if it means sacrificing individual rights.

These contrasting philosophical frameworks highlight the complexity of ethical decision-making, and indicate the difficulty of translating human morality into algorithms. While AI can assist in making decisions, then, it cannot replace the human capacity for moral reasoning.

Robot © Marcin Wichary 2009

Algorithmic Bias & the Problem of Fairness

As artificial intelligence becomes more integrated into our lives, one troubling issue that has emerged is algorithmic bias. This occurs when often unintentionally AI systems perpetuate or even amplify existing biases in the data on which they’re trained. While AI has the potential to make decisions more efficiently and objectively than humans, it’s not immune to the flaws of the society that creates it. In fact, without careful oversight, AI can reinforce discrimination and deepen inequalities.

Algorithmic bias arises when an AI system produces results that are systematically unfair to certain groups of people, usually because the data used to train the AI reflects historical or societal biases. For example, if a hiring algorithm is trained on resumes from a company that has historically favored male candidates, it will learn to prioritize male applicants over equally qualified female ones. Similarly, if a facial recognition system is trained primarily on images of lighter-skinned individuals, it may struggle to identify people with darker skin tones. These biases are not intentional, but they can have serious consequences, particularly for marginalized communities.

The impact of algorithmic bias can be seen in a variety of real-world applications. I’ve already mentioned these:

• Facial Recognition Systems: Studies have shown that many facial recognition technologies are significantly less accurate for people of color, particularly women. For example, a 2018 study by MIT researchers found that commercial facial recognition systems had error rates of up to 34% for darker-skinned women, compared to less than 1% for lighter-skinned men (‘Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification’, Proceedings of Machine Learning Research 81, Buolamwini & Gebru, 2018). This disparity can lead to serious consequences, such as wrongful arrests or a denial of services.

• Hiring Algorithms: AI-powered hiring tools are increasingly used to screen job applicants, but they have been found to favor certain demographics. For instance, Amazon once developed a recruiting algorithm that penalized resumes containing terms like ‘women’s’, or graduates of all-women’s colleges, reflecting biases in the historical hiring data on which it had been trained. Such biases can perpetuate inequality in the workplace and limit opportunities for underrepresented groups.

• Predictive Policing: Predictive policing tools use algorithms to identify areas where crimes are more likely to occur, or individuals who are more likely to commit them. However, these systems are often trained on historical crime data, which may reflect biased policing practices. As a result, they tend to target marginalized communities, reinforcing cycles of discrimination and mistrust.

The ethical implications of algorithmic bias are profound. When AI systems discriminate against certain groups, they exacerbate existing inequalities and undermine efforts to create a fairer society. Biased hiring algorithms can limit economic opportunities for women and minorities, while flawed facial recognition systems can lead to wrongful arrests, or the surveillance of innocent individuals. Beyond these immediate harms, algorithmic bias also erodes trust in technology and the institutions that use it. If people believe that AI systems are unfair or discriminatory, they may be less willing to adopt those technologies or accept their decisions.

Addressing algorithmic bias requires a concerted effort from developers, companies, and policy-makers. Programmers must be aware of the potential for bias in their algorithms and take steps to mitigate it, such as using diverse and representative datasets, testing for fairness, and incorporating other ethical considerations into the design process. Companies, meanwhile, have a responsibility to ensure that their AI systems are transparent and accountable, allowing users to understand how decisions are made and to challenge them if necessary. Policy-makers can also play a role, by establishing regulations and standards to prevent discrimination and promote fairness in AI use.

The problem of algorithmic bias also raises deeper philosophical questions about fairness and accountability. Can an algorithm ever be truly fair, or will it always reflect the biases of the society that created it? And who is accountable when an AI system makes a biased decision – the developer, the company, or the user? Such questions challenge us to think critically about the role of technology in our lives and the values we want it to uphold.

Privacy, Autonomy, & the Human Factor

As artificial intelligence becomes more pervasive, it is not only transforming how decisions are made, but also raising critical concerns about privacy, autonomy, and the role of human judgment. How do we balance the benefits of AI with the need to protect privacy and preserve human autonomy? And what happens when machines make the decisions that were once the domain of human expertise? While AI offers remarkable efficiency and precision, its use often comes at the cost of the nuanced understanding that only humans can provide.

One of the most pressing issues surrounding AI is its impact on privacy. Many AI systems – particularly those using facial recognition or data mining – rely on vast amounts of personal information to function effectively. For example, facial recognition technologies scan and store images of thousands, even millions, of individuals, often without their consent – raising concerns about the erosion of privacy. Similarly, data mining algorithms analyze mass online behavior, purchase histories, and even social media activity, to make predictions of likes or recommendations. While these technologies can be useful, they also create opportunities for misuse, such as unauthorized tracking, profiling, or even manipulation. The ethical question here is clear: How much privacy are we willing to sacrifice for the convenience and efficiency that AI promises?

Another significant concern is the loss of human autonomy in decision-making. As AI systems take over making decisions that were once performed by humans, such as diagnosing medical conditions, recommending treatments, or even determining prison sentences, this raises questions about accountability and the value of human judgment. For instance, if an AI system misdiagnoses a patient or recommends an inappropriate treatment, who is responsible – the developer, the healthcare provider, or the machine itself? Moreover, when decisions are made by algorithms, the individuals concerned may feel disempowered, as though their fate is being determined by an impersonal and inscrutable system (which it is).

At the heart of these concerns is the recognition that ethical decision-making requires more than just data and algorithms – it requires empathy, intuition, and a deep understanding of context. Humans are very capable of considering the unique circumstances of a situation, weighing competing values, and making judgments that reflect moral principles. Machines, on the other hand, operate on predefined rules and patterns. Naturally they can’t feel compassion, but neither can they understand the subtleties of human experience. For example, a human doctor might consider a patient’s emotional state, cultural background, and personal preferences when recommending a treatment, while an AI system might focus solely on statistical outcomes. This limitation once again underscores the importance of preserving the human factor in critical decision-making processes.

A compelling example of the tension between algorithmic efficiency and human judgment can be seen in the use of AI for hiring. Many companies now use algorithms to screen resumes, assess candidates’ suitability for a role and even conduct interviews. While these systems can save time and reduce bias, they raise concerns about fairness and transparency. For instance, an AI hiring tool might prioritize candidates solely on factors such as education or previous job titles, and overlook qualities like creativity, resilience, or interpersonal skills that are harder to quantify. This raises the question: are we sacrificing the richness of human judgment for the sake of efficiency? And what does this mean for the future of work or for the individuals whose lives are shaped by these decisions?

Accountability & Responsibility

As artificial intelligence systems take on increasingly critical roles in society, the question of accountability becomes more pressing. When an AI system makes a decision – whether in diagnosing a disease, approving a loan, or even in causing harm if it’s a combat AI – who is responsible? Is it the developer who designed the algorithm, the company that deployed it, or the user who relied on its output? This question lies at the heart of the ethical and legal challenges surrounding AI, and it highlights the need for clear legal frameworks to assign responsibility and ensure accountability.

However, determining accountability for AI decisions is far from straightforward. Developers create the algorithms, but often lack control over how their creations are used. Companies deploy AI systems, but they may not understand the intricacies of the technology, or its potential consequences. Users, meanwhile, may rely on AI for decision-making, but may not have the expertise to question its outputs.

This diffusion of responsibility creates a moral and legal gray area. Without clear guidelines, holding anyone accountable for AI decisions becomes a daunting task.

One way to address this challenge would be by ensuring that AI systems are transparent and their decisions explainable. If an AI system recommends a medical treatment or denies a loan application, both users and affected individuals should be able to understand how that decision was made. This is not just a technical issue, but an ethical one. Transparency fosters trust, and allows for meaningful oversight, helping to ensure that AI systems are used responsibly. However, many AI algorithms, particularly those based on deep learning, operate as ‘black boxes’, making it difficult even for experts to explain their reasoning. This lack of explainability complicates efforts to assign accountability, and undermines public trust in AI.

To address these challenges, there is a growing call for ethical guidelines and legal policies to govern AI development and use. Governments, industry leaders, and civil society must work together to establish AI standards that ensure fairness, accountability, and transparency. Regulations could require companies to conduct regular audits of their AI systems, disclose potential biases, and provide mechanisms for redress when things go wrong. Such measures would not only protect individuals, but also encourage responsible innovation.

The question of accountability in AI also resonates with broader philosophical concerns about moral responsibility. Hannah Arendt developed her concept of the ‘banality of evil’ in a very different context, but it highlights how ordinary individuals can contribute to harmful (or evil) systems without fully understanding or acknowledging their role. Similarly, reliance on AI systems might diffuse moral responsibility, allowing individuals to defer difficult decisions to the machines, and thus avoid confronting the ethical implications of their actions. This raises a troubling possibility: As AI becomes more pervasive, will we lose sight of our own moral agency?

Conclusion: The Future of AI & Ethics

It’s clear that AI holds immense potential to improve decision-making, enhance efficiency, and address global challenges. From healthcare to criminal justice, it is transforming how we live and work. Yet, these advances come with ethical risks, including algorithmic bias, privacy threats, and accountability gaps. While AI can assist in making decisions, it cannot replace human judgment, empathy, or moral reasoning. It lacks the ability to understand context or feel compassion, underscoring the need for human oversight, transparency, and fairness. So the central question – Can machines make moral decisions? – remains unresolved.

As we integrate AI into more aspects of life, we must ensure it serves humanity’s values, not undermines them. This requires sound ethical principles in research and development, robust governance, and effective public engagement. Policymakers, developers, and users all have a role in shaping a future where AI enhances, rather than diminishes, our collective well-being.

As always, the true measure of progress is not the sophistication of our machines, but the wisdom with which we use them. This means that the future of AI is not just a technological challenge – it is also a moral one.

Mahmoud Khatami is Professor of Philosophy at the University of Tehran.