Good Opposition vs. Bad Opposition One day, I came across an essay by George Orwell about Rudyard Kipling, the Anglo-Indian conservative poet. Orwell, after a thorough analysis of Kipling's work, concluded that despite his precarious intellectual stance as a defender of the ruling power, Kipling possessed a distinct advantage over his liberal contemporaries: "a certain grip on reality." Orwell elaborated, stating that those in power are consistently confronted with the practical question, "In such and such circumstances, what would you do?" In contrast, the opposition is not held accountable for making real decisions or taking responsibility. This lack of practical accountability, especially in a context where opposition is a permanent and well-funded role, as seen in many Western democracies, often leads to a decline in the quality of their thought. Fast forward to today, and we find ourselves in a similar dynamic within the realm of technology, particularly with artificial intelligence (AI). The most significant opposition group in this field is undoubtedly the AI skeptics. I emphasize their importance not to offer a backhanded compliment, but because AI is the most transformative technology of our time. Any group associated with it, whether supportive or critical, holds considerable influence. This includes AI enthusiasts, creators, and dedicated users like myself. AI has been advancing steadily for over 80 years, moving from gradual improvements to rapid breakthroughs. The technology's impact is profound and far-reaching, touching virtually every aspect of modern life. From healthcare to education, finance to transportation, AI systems are increasingly shaping our decisions and actions. Given this significance, the role of AI skeptics is crucial. They challenge the status quo, raise ethical concerns, and ensure that the development of AI remains grounded in practical reality. However, not all skepticism is beneficial. Constructive criticism and responsible questioning are vital for progress, but knee-jerk opposition or dismissiveness can impede innovation and lead to misinformed policies. A well-informed and engaged skepticism, on the other hand, can drive better design, implementation, and regulation. It forces developers and policymakers to consider long-term implications and address potential risks proactively. One example of effective AI skepticism is the debate surrounding facial recognition technology. Critics have raised valid concerns about privacy, bias, and misuse. These objections have prompted companies and governments to reconsider how and when to deploy such technologies, leading to better safeguards and more ethical guidelines. In contrast, blanket condemnations that ignore the benefits and real-world applications can be counterproductive. Similarly, the discussion around AI in healthcare has been enriched by thoughtful skeptics. They highlight the need for rigorous testing and transparent data practices, ensuring that AI tools do not exacerbate existing health disparities. This dialogue has led to more robust research and ethical standards, ultimately making AI more reliable and equitable. On the other hand, there are instances where skepticism crosses the line into unhelpful negativity. Some critics dismiss AI's potential entirely, claiming it will never live up to its hype or is inherently dangerous. This attitude can stifle investment and research, depriving society of valuable innovations. For instance, early skepticism about the internet delayed many organizations' adoption and integration of this revolutionary technology. Moreover, hyperbolic predictions of catastrophic outcomes due to AI can create unnecessary panic and hinder balanced policy-making. While it is essential to address genuine risks, such as job displacement and algorithmic bias, exaggerating these issues can lead to overregulation or outright bans, which may do more harm than good. To be effective, AI skepticism must be informed, constructive, and evidence-based. It should focus on identifying and mitigating specific risks rather than making sweeping generalizations. Engaging with the technology itself and understanding its capabilities and limitations is crucial. For example, the work of renowned skeptics like Joy Buolamwini and Timnit Gebru, who conduct empirical studies on AI bias, offers actionable insights and concrete solutions. Ultimately, the role of AI skeptics is to ensure that the technology evolves responsibly and ethically. By asking tough questions and pushing for accountability, they help create a more resilient and beneficial AI ecosystem. Just like Orwell's observation about the ruling power and the opposition, AI developers and proponents are constantly asked to justify their actions and decisions. The AI skeptics, however, must also rise to the occasion, providing thoughtful and well-reasoned critiques that contribute to the ongoing dialogue and advancement of AI. In conclusion, the balance between support and skepticism is essential in the development of any powerful technology. AI skeptics play a vital role in keeping the conversation grounded and ensuring that ethical considerations remain at the forefront. Their contributions, when thoughtful and informed, are invaluable to the progress of AI and, by extension, to the betterment of society.