{
  "id": 18750036,
  "title": "AI can learn a lesson from Hume and Kant",
  "url": "https://urgent.news/2026/11/07/ai-can-learn-a-lesson-from-hume-and-kant",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-07T16:00:00.000Z",
  "source": {
    "name": "Philippine Star Business",
    "slug": "philippine-star-business",
    "url": "https://www.philstar.com/business/2026/11/08/2568557/ai-can-learn-lesson-hume-and-kant"
  },
  "original_language": "en",
  "account": "AI systems are increasingly adept at optimizing processes, becoming more efficient through experience. However, this optimization requires a target, which is set by humans. While AI can determine the quickest route or the most efficient means to achieve an end, it cannot consider whether the end itself is worthy or aligns with human values. This raises the question: can AI truly be intelligent if it does not possess the ability to judge the purpose behind its actions?\n\nDavid Hume, a Scottish philosopher from the 18th century, famously argued that reason is \"the slave of the passions,\" meaning that desires choose the destination while reason only calculates the route. Immanuel Kant took this a step further, suggesting that reason not only finds efficient means but also assesses whether an end justifies those means. In modern terms, Hume's perspective resembles the Humean model of optimization-driven companies, while Kant's aligns with the idea that leadership must question the purpose of optimization, ensuring that metrics serve as servants rather than masters.\n\nCompanies often excel in optimization, setting up cascading targets, monitoring metrics, and rewarding those who meet them. Yet, they rarely ponder whether these targets are worth pursuing. This is akin to rewarding call center agents for making short calls, leading them to cut customers off, or asking sales teams to focus only on volume, resulting in selling clients unnecessary products. In both cases, the original purpose is undermined, demonstrating the potential pitfalls of metric-driven decision-making.\n\nThe danger lies in the ease with which organizations can surrender not only the work of analysis but also the responsibility of judgment. As AI becomes more capable, it can sift through vast amounts of data, detect patterns, identify risks, and surface options—all in seconds. This is undoubtedly valuable. However, the more advanced our tools become, the greater the risk of surrendering judgment and responsibility to AI. The distinction between asking AI, \"What is happening?\" and asking it, \"What should matter?\" is crucial. While AI can provide insights and recommendations, it lacks the capacity for leadership judgments about what matters most, what should be valued, or what decisions reflect our values. Leadership, with its capacity for empathy, courage, and wisdom, must ultimately determine what is right, what matters, and what is worth pursuing. AI should be used to enhance efficiency, but leadership must retain accountability for the consequences of its decisions.",
  "summary": "A pilot speaks into the intercom during the flight, saying, “I have some good news and some bad news.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}