算法安全区的代价:AI 诱发的人类平庸化危机
人们对人工智能的担忧林林总总:两极分化、假信息泛滥、抢夺人类饭碗,这些确实令人焦虑。但真正让我彻夜难眠的,是一件关乎人类体验本身、更为私人且深沉的隐忧:我担心 AI 会让我们变得索然无味。当我们将越来越多的决策外包给 AI 时,我们实际上交出的是深层的人性内核——那种探索、冒险、跌跌撞撞闯入未知、乃至出乎自己意料的能力。失去偶遇与发现的契机,我们不仅作为个体,乃至整个人类群体,都面临退化为浅薄、单一维度的风险。
为了理解这种机制,不妨看一个生活中的具象场景:走进 31冰淇淋(Baskin-Robbins: 拥有31种经典风味的连锁冰淇淋品牌),眼前陈列着巧克力、开心果、扁桃仁、柠檬雪葩等31种风味。此时你面临抉择:是选择熟悉稳妥的巧克力风味,还是冒险尝试未知的“狂野鲁莽雪葩”(Wild and Reckless Sherbet)?这个抉择正是认知科学中经典的利用与探索权衡(Exploitation-Exploration Trade-off: 在已知最佳选项的即时收益与未知选项潜在更高回报之间寻求平衡的决策难题)。
英文原文
People worry about AI for all kinds of reasons. It's polarizing, it spreads misinformation, it's coming for our jobs. And those are all good reasons to be nervous. But what really keeps me up at night is something else. Something that feels more personal and perhaps even more concerning for the human experience itself. I worry that AI will make us boring. You, me, all of us. That's because the more we outsource our decisions to AI, the more we surrender something deeply human. Our capacity to explore, to take risks, to stumble into the unknown, and sometimes surprise even ourselves. I worry that without the potential for discovery, for serendipity, we risk becoming more shallow and unidimensional versions of who we are and who we could be, not just as humans, as individuals, but as humanity.
Now let me make this idea a bit more concrete and take you somewhere perhaps unexpected, but also totally relatable. Imagine walking into a Baskin-Robbins. What you'll see is an illustrious assortment of 31 different ice cream flavors. There's chocolate chip, pistachio, almond, lemon sorbet, and many more. You're now facing a difficult choice. Are you going to go with an old favorite, chocolate maybe, or are you going to take a risk on something funky and new, like wild and reckless sherbet? And yes, it's a real flavor. (Laughter) The choice you're facing is a classic human dilemma scientists call the exploitation-exploration trade-off. Are you going to play it safe and capitalize on what you know you like, or are you going to take a risk with the hope of finding something even better?
演化本能与算法异化:短视满意度对探索的挤压
生活充斥着这类权衡:去常去的餐馆还是街角的新店;留着常规发型还是剪个前卫造型;守着稳定的工作还是去当匹克球冠军。我们在“利用”与“探索”之间的风险偏好各不相同,但演化本能塑造了我们在这两者之间的平衡机制。对生活在非洲热带草原的原始祖先而言,这种平衡关乎生死存亡:过分守成依赖已知林地,水源枯竭时便会饿死;而若毫无顾忌地闯入未知领地,则可能误食毒物或丧身狮口。现代人的日常选择虽不再关乎生死,但这一本能依然驱动我们在审慎与好奇之间寻找平衡——只守成永不失望但也无从成长,只探索虽体验丰富却无法将经验变现沉淀。
然而,现代人工智能的介入打破了这种微妙的均衡,因为 AI 本质上规避风险。这并非源于其算法底层有何先天缺陷,而是因为我们赖以导航日常生活的系统——从 Spotify、Netflix 到 ChatGPT——被普遍训练为追求极端“利用”,即最大化短期的用户参与度和满意度。你是否点击了链接、看完了视频、收藏了歌曲?若是,AI 获得奖励;若否,AI 受到惩罚。
风险承担与偶发探索根本不在当下的算法目标函数中。回到冰淇淋的隐喻:如果有 60% 的人喜欢核桃奶油风味,AI 就会直接推荐这一款。这并不是 AI 缺乏想象力,而是商业机制缺乏探索激励——安全牌能预防用户失望,从而降低客户流失率。过去15年,我作为一名计算社会科学家,跨界研究心理学、计算机科学与商业运作,深知将算法优化聚焦于“利用”具备极高价值:面对 Netflix 超过5500部电影或 Spotify 上亿首曲目,如果没有偏好“利用”的 AI 协助筛选,现代人早已在决策过载中瘫痪。但这种便利正带来深远的生存代价。
英文原文
And our lives are full of these trade offs. Stick with your favorite restaurant or try the fancy new spot on the corner. Keep your usual haircut or ask for something new and edgy. Stay in your stable job or finally try to become a pickleball champion? (Laughter) We all differ in our appetite for exploitation versus exploration. But we're also all hardwired by evolution to strike a certain balance between the two, because for our ancestors on the African savanna, the trade-off was actually pretty simple. Stick too closely to what's safe and you risk starvation when the grove runs dry. Stray too far into the unknown and you might get poisoned or eaten by lions. Luckily for you and me, the trade offs we face today are far less about survival. But our choices are still governed by this instinct to balance caution with curiosity. If all you ever did in life was play it safe and exploit, you'd never get disappointed, but you'd also never get a chance to advance and grow. If all you did instead was discover and explore, you'd collect endless experiences, but you'd also never get a chance to capitalize on your learnings. So it's really this balance between exploitation and exploration that fuels our growth, not just as a species, but also as individuals.
But that's exactly where AI throws a wrench into the works, because AI hates risk. And that's not because of its algorithmic DNA. It's because the systems we rely on to navigate our world, from Spotify to Netflix to ChatGPT, are overwhelmingly trained to optimize for exploitation. Or more specifically, for short-term engagement and satisfaction. Did you click the link, watch the video, like the song? If yes, AI gets a clap on the shoulder. If no, it gets a slap on the hand. Risk, discovery and exploration are simply not part of their programming. Think back to Baskin-Robbins. If 60 percent of people preferred the flavor pralines and cream, then that's what the AI is going to recommend. It's not that AI lacks imagination. It lacks incentive. Safe bets protect against disappointment, which in turn reduces customer churn. So instead of taking this risky gamble that helps you explore, companies tend to err on the side of exploitation when training their AI systems. And to be clear, there's nothing inherently wrong with that. I've spent the last 15 years as a computational social scientist working at the intersection of psychology, computer science, and business. And I know that rewarding AI in that way can be extremely valuable. Algorithms, in a way, only become so good at figuring out what you might enjoy or currently be looking for because they focus on exploitation. And that's not just good for business. It's also good and sometimes even necessary for us as consumers. For me, the mere thought of having to choose between 31 different ice cream flavors makes me dizzy. And yet, most of the choices that we face day-to-day are far more complex than that. Netflix, for example, has over 5,500 movies to choose from. Spotify, over 100 million songs. So the truth is that without a little help from our exploitation-loving AI friends, we simply don't stand a chance at navigating modern life. But I've also become increasingly concerned that this upside comes at a somewhat existential cost.
统计安全的同质陷阱:个体复杂性的隐性剥蚀
为了验证这种算法机制的实际效应,我做了一组实验:我向 ChatGPT 索取 31冰淇淋 的推荐风味,模拟全新客户连续提问100次。结果其中96次,它只在最受欢迎的两种口味——核桃奶油(Pralines and Cream)与薄荷巧克力脆片(Mint Chocolate Chip)之间循环。如果所有消费者都只被引导选择这两种,剩下29种风味最终将从货架上彻底消失。
这种对人类体验的压平并非局限于食品选择。在我和学生及合作者的多项研究中,我们发现当人们借助 AI 提供指导时,群体偏好会变得愈发趋向规范化且缺乏异质性,创意产出的独特性显著下降,连人们心目中最重要的科学家、运动员或历史人物名单也趋于雷同。AI 正在将人类观点、信念与偏好的无限多样性,收敛为统计学上的安全同质。
更严峻的是,哪怕算法捕捉到了你的个性偏好,它依然会在你的偏好闭环内追求绝对安全。在后续实验中,我告诉 ChatGPT 我过去100次吃冰淇淋的记录:70% 选择坚果椰子味,其余30% 平分给另外三种喜爱口味。当我让它替我做出接下来的100次选择时,它在每一次无一例外地都选了坚果椰子味。偶尔尝试巧克力软糖、曲奇面团或芒果口味的探索乐趣荡然无存——在系统的刻画下,我被简化成了一个纯粹的“坚果椰子”。
这种影响是极其隐蔽的。它不是剧烈的骤变,而是一场由成千上万次算法推荐构成的慢性消亡(Death by a thousand algorithmic recommendations):一次次更保守的观影选择、更通俗的书籍、更主流的度假目的地。正如《纽约时报》记者 Kashmir Hill 在连续一周将所有生活决定交由 AI 接管后所感叹的那样,算法在暗中将她塑造成了一个平庸附庸者(Basic bitch: 毫无原创性、面目模糊、缺乏个性与复杂维度的平庸个体)。
英文原文
And to give you a first flavor of what I mean by that, let's go back to Baskin-Robbins. Before coming here, I ran a simple experiment. I asked ChatGPT to recommend one of their ice cream flavors. I did that 100 times, each round pretending to be a new customer. Ninety-six times it recommended one of their two most popular flavors -- Pralines and Cream and Mint Chocolate Chip -- which I realize, and I’m sure you’d agree, is not exactly a very diverse set of choices. (Laughter) And it could mean that we soon find ourselves in a world where Baskin-Robbins offers only those two flavors. If no one ever picks the other 29, why bother offering them in the first place? Now this might sound trivial and not really existential at all, but that's only until you realize that this flattening of the human experience happens across every aspect of life. In studies with my collaborators and students, we've shown that when people use AI for guidance, their preferences become more normative and less diverse, their creative output becomes less unique, and their choice of the most important scientists, athletes and historical figures becomes the same as everyone else's. So in a nutshell, AI turns this infinite diversity of human opinions, beliefs and preferences into statistically safe sameness.
And that's only part of the problem, because even when AI learns your quirks -- say, it figures out that you like nutty coconut better than pralines and cream -- it will still play it safe within your preferences. Meaning it is going to optimize for what you are most likely to like. I went back to ChatGPT for a second experiment and this time told it about my personal ice cream preferences. I said that during my last 100 visits, I picked nutty coconut 70 percent of the time. The remaining 30 percent, I split evenly among three of my other favorites. I then asked it to make the next 100 choices on my behalf. And guess what it did? It picked nutty coconut every single time. Gone are my adventures into occasional chocolate fudge, cookie dough or mango. All I am at that point is literally a nutty coconut. (Laughter) But forget about ice cream. My students and I have repeatedly shown the same patterns when studying people's actual preferences. AI narrows your taste, it flattens your personality, and it scrubs away the edges that make you interesting and keep you dynamic. And the insidious part of all of this is that the impact of AI on human complexity is subtle. You won't notice it happening overnight. Rather, it's a death by a thousand algorithmic recommendations. It's one slightly safer movie, one slightly more popular book, one slightly more mainstream vacation at a time. With every decision you outsource to AI, you become more narrow. Then AI learns from that shallower version of you and narrows its recommendations even more. The New York Times reporter Kashmir Hill captured this dilemma perfectly in one of her articles. After outsourcing her decisions to AI for a week, she complained about its hidden agenda to turn her into a "basic bitch." (Laughter) And the funny part is, it's the word basic here that's the biggest insult. (Laughter) Because being basic means being unoriginal, unexceptional and uninteresting. It's offensive, not because it suggests failure, but because it implies mediocrity, an absence of complexity. And who really wants it for themselves? Nobody. We all like the feeling of being unique, of being special, of being someone who doesn't easily fit into a box. I, for one, don't want to become exactly like everybody else. And I also don't want to become a singular version of myself.
重塑探索权衡:设计主动对抗同质化的算法旋钮
面对滑向平庸的轨迹,我们无法也不应彻底抛弃 AI。我们所需要的是夺回冒险与发现的主动权,重新平衡“利用”与“探索”。讽刺的是,AI 本身正是实现这种平衡的最强工具——前提是我们学会提出正确的问题,并针对探索行为建立全新的奖励机制。我们不能只让 AI 推荐“已知喜爱之物”,而应引导它发掘“前所未见但极可能爱上之物”。
AI 的超能力在于从海量人类数据中捕捉潜在模式。它不仅知晓你的舒适区,更洞察紧邻你现有偏好边界之外的未知地带。这意味着 AI 完全有能力引导人类进行精准的受控探索(Curated risks)。试想一种全新的交互架构:在你的流媒体或搜索引擎界面中植入一个探索偏好旋钮(Exploration Dial),赋予用户自由调节偏离度的控制权:
- 精准模式(Spot on):在需要效率的常规场景下,保持最高相关度,直接获取最精准的已知答案;
- 微创新模式(Me with a twist):略微推高旋钮,探出舒适区边缘,推荐相关但带有些许陌生感的新异内容;
- 万能牌模式(Wildcard):在充满探索欲的时刻,将旋钮拉满,主动寻求完全颠覆认知的未知发现。
但核心的系统瓶颈在于激励结构。仅在 Prompt 中输入“请发挥创意”无法解决问题,模型在底层损失函数与对齐机制驱动下,依然会本能地退回低风险的安全输出。因此,我们必须改变评价体系:不再因 AI 的偶尔失误而进行负反馈惩罚,而是要对其基于对用户深刻理解而做出的聪明冒险与探索尝试给予正面奖励。
这关乎捍卫人类独特的复杂性、混乱感与丰富性——无论是对冷门纪念品的狂热收藏、对冷门寻宝游戏的突然痴迷,还是对手动挡汽车的顽固偏好。正是这些算法无法预测的矛盾与特立独行,构成了人性的魅力。当前我们正处在一个关键的转折点:AI 正在从单纯的“建议者”演进为代表我们做出选择的“代理人”。下一次当算法再次奉上安全的核桃奶油时,不妨主动拒绝,让它带你奔向未知与狂野。
英文原文
So what can we do to course-correct and avoid destination basic bitch? (Laughter) We can’t hit the reset button on AI, and we shouldn’t. As I said before, these tools are incredibly valuable for navigating modern life, and they make us better off in many ways. But we need to reclaim our ability to take risks and discover. We need to find a way to rebalance exploitation and exploration. And ironically, AI could actually help us accomplish just that, but only if we ask the right questions and reward it for the right actions. So instead of asking it to help us find something we like, we could start asking it to help us find something new, something we're likely to love, even though we've never tried it before. And by that, I don't necessarily mean you typing that question into ChatGPT yourself, although you should certainly try. What I mean is getting companies to harness AI’s superpower, its ability to detect patterns in vast amounts of human data for exploration purposes. Because AI has seen the entire universe of preferences -- it knows not only what you currently like, but also what lies just beyond the boundaries of your typical preferences -- which means that it can help you explore smartly to take curated risks when you want to.
Now here's what this could look like. Imagine a dial on your Netflix account or your Google search bar that lets you decide how far from your typical preferences you want to stray at any given point in time. On a regular day, you probably keep the dial close to the "spot on" setting because you want to get the most relevant hits right away. But then on other days, you might feel adventurous enough to push it a little closer to the "me with a twist" setting and ask for content that's a little outside of your comfort zone, but still relevant. And then on other days, for those rare moments when you feel like you're really ready to take on the world, you might push the dial all the way to the "wildcard" setting and ask it to help you discover something entirely new. I, for one, would love to have such a dial. It doesn't force me to leave my comfort zone. I can always leave it on the "spot on" setting and benefit from the convenience that comes with it. But it gives me the choice and the agency to break out of my little bubble whenever I want to, or I feel like I have to.
But here's the catch. For AI to toggle its superpower between exploitation and exploration, we need to incentivize it to do so. Merely asking it to be more creative or help you become more adventurous isn't going to do the trick. In most cases, it will still default to the tried-and-tested output because it craves that clap on the shoulder. So instead of punishing AI every time it takes a swing and misses, we need to start rewarding it for taking smart swings, for making bets that are a little bold, a little unusual, perhaps, but still grounded in what it knows about us. AI doesn't have to throw darts in the dark. It can take informed risks. It can help us optimize our exploration. And when it does, we should treat it as a success, not a failure. Now this isn’t about desserts or playlists. This is about preserving what makes us uniquely, messily and gloriously human. It’s your random passion for E.T. memorabilia, your weird detour into geocaching, or your stubborn preference for a stick-shift car. (Laughter) It’s about all the contradictions that AI can’t quite explain and the beauty of us being just a little weird once in a while. And if we believe that human complexity is worth preserving, then the time to act is now. Because we're at this inflection point where AI is no longer just recommending but starting to act on our behalf. It's choosing, not suggesting. And that's where the stakes become really existential. So next time AI offers you pralines and cream, maybe say no and ask it to help you go wild and reckless instead. Thank you. (Applause)