Passage:
Imagine a world in which artificial intelligence is entrusted with the highest moral responsibilities: sentencing criminals, allocating medical resources, and even mediating conflicts between nations. This might seem like the pinnacle of human progress: an entity
unburdened by emotion, prejudice or inconsistency, making ethical decisions with impeccable precision. . . .
Yet beneath this vision of an idealised moral arbiter lies a fundamental question: can a machine understand morality as humans do, or is it confined to a simulacrum of ethical
reasoning? AI might replicate human decisions without improving on them, carrying forward the same biases, blind spots and cultural distortions from human moral judgment. In trying to emulate us, it might only reproduce our limitations, not transcend them. But there is a deeper concern. Moral judgment draws on intuition, historical awareness and context qualities that resist formalisation. Ethics may be so embedded in lived experience that any attempt to encode it into formal structures risks flattening its most essential features. If so, AI would merely reflect human shortcomings; it would strip morality of the very depth that makes ethical reflection possible in the first place.
Still, many have tried to formalise ethics, by treating certain moral claims not as conclusions, but as starting points. A classic example comes from utilitarianism, which often takes as a foundational axiom the principle that one should act to maximise overall wellbeing. From
this, more specific principles can be derived, for example, that it is right to benefit the greatest number, or that actions should be judged by their consequences for total happiness. As computational resources increase, AI becomes increasingly well-suited to the task of starting from fixed ethical assumptions and reasoning through their implications in complex situations.
But, what exactly, does it mean to formalise something like ethics? The question is easier to grasp by looking at fields in which formal systems have long played a central role. Physics,for instance, has relied on formalisation for centuries. There is no single physical theory that
explains everything. Instead, we have many physical theories, each designed to describe specific aspects of the Universe: from the behaviour of quarks and electrons to the motion of galaxies. These theories often diverge. Aristotelian physics, for instance, explained falling objects in terms of natural motion toward Earth’s centre; Newtonian mechanics replaced this with a universal force of gravity. These explanations are not just different; they are incompatible. Yet both share a common structure: they begin with basic postulates assumptions about motion, force or mass– and derive increasingly complex consequences. . . .
Ethical theories have a similar structure. Like physical theories, they attempt to describe a domain– in this case, the moral landscape. They aim to answer questions about which actions are right or wrong, and why. These theories also diverge, and even when they recommend similar actions, such as giving to charity, they justify them in different ways. Ethical theories also often begin with a small set of foundational principles or claims, from which they reason about more complex moral problems.
All of the following can reasonably be inferred from the passage EXCEPT:
The appeal of an AI judge rests on immunity to bribery, partiality, and fatigue; yet the text questions whether procedural cleanliness amounts to moral understanding without lived context and interpretive depth.
By analogy with physics, compact postulates can yield broad predictions across incompatible theories and ethics can likewise share structure while continuing to diverge rather than close on a single comprehensive framework.
Encoding ethics into fixed structures risks stripping away intuition, history, and context and, if that occurs, the depth that enables reflective judgment disappears. So, machines would mirror our limits rather than exceed them.
With fixed moral starting points and expanding computational resources, the argument forecasts convergence on one ethical system and treats contextual judgment as unnecessary once formal reasoning scales across domains and cultures.
With fixed moral starting points and expanding computational resources, the argument forecasts convergence on one ethical system and treats contextual judgment as unnecessary once formal reasoning scales across domains and cultures.
Step 1: Understanding the Passage.
The passage discusses AI and ethics, particularly how it might replicate human moral judgment. It talks about AI potentially reducing complexity by following set structures, which could strip away essential qualities of human judgment such as intuition and context.
Step 2: Analysis of Options.
(1) is inferred as it talks about AI’s impartiality and concerns about its procedural correctness.-
(2) presents a comparison to physics, which aligns with the discussion of formalization in ethics.-
(3) matches the passage’s message about the risk of stripping away essential judgment qualities if ethics are formalized.-
(4) contradicts the passage as it suggests a reduction to a single ethical framework, which the passage argues is impractical.