We're celebrating AI systems for acing human exams while ignoring what truly matters—their ability to navigate ethical complexity, understand nuance, and grapple with the moral weight of real-world decisions. It's time to rethink how we measure artificial intelligence.
Every few months, the headlines trumpet the same story: “AI Aces Medical Boards!” “ChatGPT Scores a Gold in International Maths Olympiad” “New Model Conquers Graduate School Tests!” We applaud these achievements as if they represent meaningful milestones in GenAI, but we’re fundamentally missing the point.
The ability to regurgitate correct answers from training data is not intelligence—it’s sophisticated pattern matching or querying the data at best, which has been masqueraded as understanding.
When an AI system “passes” the medical licensing exam, it hasn’t learned to heal. When it “conquers” the legal bar, it hasn’t grasped justice. When it scores perfectly on standardized tests, it hasn’t developed wisdom. We’re measuring the wrong things entirely.
True intelligence—the kind that matters for systems we might integrate into healthcare, criminal justice, education, and other critical domains—isn’t about memorizing facts or recognizing patterns. It’s about navigating moral complexity, understanding context, and grappling with the weight of decisions that affect real human lives.
Our fixation on standardized testing reveals a deeper misunderstanding of what makes intelligence valuable. Even for humans, these assessments are imperfect proxies that measure pattern recognition and memorization rather than wisdom, creativity, or moral reasoning.
When we celebrate an AI system for passing the LSAT or medical boards, we’re essentially applauding a sophisticated autocomplete function for successfully predicting what humans have written about legal or medical reasoning. The system isn’t understanding the material—it’s identifying statistical patterns in text that correlates with correct answers.
This creates a dangerous illusion. A system that can perfectly answer multiple-choice questions about medical ethics might still make catastrophic decisions when faced with real patients, real families, and real moral dilemmas that don’t appear in textbooks.
Most standardized tests operate in artificial environments with clear constraints and predetermined answers. But real intelligence operates in open-ended contexts where problems are ill-defined, stakeholders have competing interests, and the “correct” answer depends on values, priorities, and contextual factors that change from situation to situation.
An AI system trained to optimize test performance learns to navigate artificial constraints, not the messy complexity of actual decision-making environments where moral considerations, cultural contexts, and individual circumstances matter more than technical knowledge.
The most important aspects of intelligence—the ones that determine whether AI systems become beneficial or harmful—are precisely the ones that standardized tests ignore.
Real-world decisions often involve competing values with no clear resolution. How does an AI system weigh individual autonomy against public health? How does it balance efficiency against fairness? How does it handle situations where following rules would cause more harm than breaking them?
These aren’t technical problems with algorithmic solutions—they’re moral dilemmas that require understanding the weight of different ethical considerations and the ability to make principled decisions under uncertainty.
Standardized tests typically embed the cultural assumptions and values of their creators. But AI systems deployed globally must navigate vastly different cultural contexts, moral frameworks, and social norms.
Recent research in persona development shows that AI systems often reflect the biases and limitations of their training data, potentially excluding or misrepresenting diverse populations. A system that excels at tests created within one cultural context might make catastrophic errors when deployed in different environments.
Perhaps most critically, tests don’t measure humility—the recognition of one’s own limitations. The most dangerous AI system might not be one that fails tests, but one that projects false confidence in situations where uncertainty and human judgment are warranted.
Wise intelligence involves knowing when not to decide, when to seek additional input, and when to defer to human judgment. These capabilities can’t be measured through multiple-choice questions.
The most sophisticated challenges facing AI systems involve what we might call “ethical delicacy”—the subtle, nuanced reasoning required to navigate moral complexity with appropriate sensitivity.
Beyond following programmed rules about avoiding harm, can an AI system grasp why human dignity matters? Can it understand the difference between treating someone as a means versus an end? Can it recognize when efficiency optimizations undermine human agency in ways that matter morally?
These questions go beyond technical capabilities to fundamental issues of understanding value, meaning, and the nature of ethical consideration.
Real-world AI deployment involves multiple stakeholders with different needs, priorities, and power dynamics. An AI system making recommendations about resource allocation must understand not just technical efficiency, but issues of equity, representation, and justice.
This requires more than pattern matching—it demands genuine understanding of how decisions affect different groups and the ability to reason about fairness in contexts where mathematical optimization might perpetuate or amplify existing inequalities.
Perhaps most importantly, AI systems need to understand the gravity of their decisions. A recommendation algorithm that influences medical treatment, legal sentencing, or educational opportunities isn’t just processing data—it’s affecting human lives in profound ways.
True intelligence involves recognizing this weight and responding with appropriate care, transparency, and accountability. It means understanding when a decision is too important to automate and when human oversight is ethically required.
Moving beyond standardized tests requires developing evaluation frameworks that probe the capabilities that actually matter for beneficial AI deployment.
Instead of multiple-choice questions, we need complex, open-ended scenarios that require balancing competing interests, considering long-term consequences, and reasoning about values under uncertainty.
These evaluations should assess not just final decisions, but the reasoning process itself. How does the system identify relevant stakeholders? How does it weigh different ethical considerations? How does it handle conflicting values or uncertain outcomes?
Evaluation frameworks should test how well AI systems adapt their reasoning to different cultural contexts, recognizing that moral frameworks vary across societies while still maintaining core commitments to human dignity and wellbeing.
This might involve presenting the same ethical dilemma in different cultural contexts and assessing whether the system’s reasoning appropriately reflects different values and norms without abandoning fundamental ethical principles.
We need to evaluate not just what AI systems decide, but how they explain their reasoning and respond to challenges. Can they articulate the values they’re optimizing for? Can they explain why they weighted different considerations as they did? Can they identify the limitations of their reasoning?
Perhaps most importantly, can they recognize when they lack sufficient information or expertise to make a particular decision and appropriately defer to human judgment?
The most critical aspect of AI evaluation involves understanding how systems interact with human agency, decision-making, and dignity.
A truly intelligent AI system might sometimes recommend that humans make their own decisions, recognizing that some choices—about family, values, creative expression, life direction—are fundamentally human prerogatives that shouldn’t be optimized away.
This requires understanding not just efficiency or even outcomes, but the intrinsic value of human choice, self-determination, and personal growth through decision-making.
The most beneficial AI systems might be those that enhance human decision-making rather than replacing it. This requires understanding when to provide information, when to offer recommendations, when to challenge assumptions, and when to step back and let humans choose.
Evaluation frameworks should assess how well AI systems serve as thinking partners rather than decision-making authorities, supporting human agency while providing valuable capabilities.
Some aspects of human experience might be fundamentally inappropriate for algorithmic optimization. Love, grief, creative expression, spiritual experience, and moral development might require human engagement in ways that AI systems should respect and protect rather than try to improve.
True intelligence might involve recognizing these boundaries and operating with appropriate humility about the limits of computational optimization.
The fundamental distinction we need to make is between intelligence as information processing capability and wisdom as the appropriate application of intelligence in service of human flourishing.
Intelligence without wisdom is merely clever. It can solve puzzles, recognize patterns, and optimize outcomes according to specified metrics. But wisdom involves understanding which metrics matter, when optimization is appropriate, and how to balance competing values in service of deeper purposes.
We need AI systems that are not just smart but wise—that understand the difference between what can be measured and what matters, between what can be optimized and what should be preserved.
Perhaps the greatest challenge is creating AI systems that can integrate technical capability with moral sensitivity, efficiency with equity, and optimization with human dignity. This requires evaluation frameworks that assess not just individual capabilities but their integration in service of human values.
Wisdom involves considering consequences that extend far beyond immediate optimization targets. How will this decision affect future generations? How might it impact marginalized communities? What precedent does it set for future AI development and deployment?
These considerations can’t be captured in standardized tests but they’re essential for AI systems that we want to integrate into society in beneficial ways.
The transition from test-focused to wisdom-focused AI evaluation requires fundamental changes in how we think about artificial intelligence and its role in society.
Success in AI development should be measured not by performance on human-designed tests but by the system’s ability to contribute to human flourishing while respecting human dignity, agency, and diversity.
This might mean developing AI systems that are less impressive in narrow technical domains but more beneficial in the complex, ambiguous, morally-laden contexts where they’ll actually be deployed.
Rather than seeking simple metrics and clear benchmarks, we need evaluation frameworks that embrace the complexity of moral reasoning, cultural sensitivity, and contextual judgment that characterize truly beneficial intelligence.
This requires longer, more expensive, more nuanced evaluation processes—but the stakes are too high for shortcuts.
Ultimately, AI evaluation should be grounded in understanding human needs, values, and flourishing rather than technical capabilities for their own sake. The question isn’t whether AI can pass human tests, but whether it can serve human purposes in ways that respect human dignity and promote human welfare.
The future of AI evaluation lies not in celebrating systems that memorize human knowledge, but in developing systems that can think alongside humans with the moral sophistication, cultural sensitivity, and ethical wisdom that complex decisions require.
The measure of truly intelligent systems won’t be their test scores—it will be whether they make the world more just, more compassionate, and more human.
Intelligence without wisdom is merely computation. The future belongs to AI systems that understand not just how to solve problems, but which problems are worth solving and how to solve them in ways that honor human dignity and promote human flourishing.
What matters isn’t whether AI can beat humans at human-designed tests, but whether it can think with humans about the moral complexities that define our shared future.
AI Attribution: This article was written with the assistance of Claude, an AI assistant created by Anthropic—which I believe cares about the big questions as much as the little questions.