A Artificial intelligence (AI) can already predict the future. Police forces are using it to map when and where crime is likely to occur. Doctors can use it to predict when a patient is most likely to have a heart attack or stroke. Researchers are even trying to give AI imagination so it can plan for unexpected consequences.
A
人工智能（AI）已然能够预测未来。警方正利用它预判犯罪发生的时间与地点；医生可以借助它预估患者突发心脏病或中风的概率；研究人员甚至尝试赋予人工智能想象力，使其能够应对各类意外后果。
Many decisions in our lives require a good forecast, and AI is almost always better at forecasting than we are. Yet for all these technological advances, we still seem to deeply lack confidence in AI predictions. Recent cases show that people don't like relying on AI and prefer to trust human experts, even if these experts are wrong.
生活中诸多决策都离不开精准预判，而人工智能的预测能力几乎总是优于人类。尽管科技取得了诸多进步，我们却似乎始终极度不信任人工智能的预测结果。近期诸多案例表明，即便人类专家判断有误，人们依旧不愿依靠人工智能，反而更信赖人类专家。
If we want AI to really benefit people,we need to find a way to get people to trust it. To do that, we need to understand why people are so reluctant to trust AI in the first place.
若想让人工智能真正造福人类，我们就必须找到方法建立人们对它的信任。要实现这一点，首先要弄明白人们为何迟迟不愿信任人工智能。
B Take the case of Watson for Oncology, one of technology giant IBM's supercomputer programs. Their attempt to promote this program to cancer doctors was a PR disaster. The AI promised t deliver top-quality recommendations on the treatment of 12 cancers that accounted for 80% of the world's cases. But when doctors first interacted with Watson, they found themselves in a rather difficult situation. On the one hand, if Watson provided guidance about a treatment that coincided with their own opinions, physicians did not see much point in Watson's recommendations. The supercomputer was simply telling them what they already knew, and these recommendations did not change the actual treatment.
B
以科技巨头 IBM 的超级计算机程序 “沃森肿瘤系统” 为例。该公司向肿瘤科医生推广这款程序的尝试，最终沦为一场公关灾难。这款人工智能系统承诺，针对占全球 80% 病例的 12 类癌症，提供高质量的治疗方案建议。但当医生首次使用沃森系统时，却陷入了十分尴尬的处境。
On the other hand,if Watson generated a recommendation that contradicted the experts' opinion, doctors would typically conclude that Watson wasn't competent. And the machine wouldn't be able to explain why its treatment was plausible because its machine-learning algorithms were simply too complex to be fully understood by humans. Consequently, this has caused even more suspicion and disbelief, leading many doctors to ignore the seemingly outlandish AI recommendations and stick to their own expertise.
一方面，倘若沃森给出的治疗方案与医生自身判断一致，医生便认为这套建议毫无意义。这台超级计算机不过是复述他们已知的内容，无法改变实际治疗方案。
另一方面，一旦沃森给出的建议与专家观点相悖，医生通常会判定该系统并不靠谱。同时，机器无法解释其治疗方案的合理性，因为它的机器学习算法过于复杂，人类难以完全理解。这进一步加深了人们的猜忌与质疑，致使许多医生无视人工智能看似离奇的建议，坚持沿用自身的专业判断。
C This is just one example of people's lack of confidence in AI and their reluctance to accept what AI has to offer. Trust in other people is often based on our understanding of how others think and having experience of their reliability. This helps create a psychological feeling of safety. AI, on the other hand, is still fairly new and unfamiliar to most people. Even if it can be technically explained (and that's not always the case),AI's decision-making process is usually too difficult for most people to comprehend. And interacting with something we don't understand can cause anxiety and give us a sense that we're losing control.
C
这只是人们不信任人工智能、不愿接受其建议的一个缩影。人与人之间的信任，往往建立在我们理解他人的思维方式、且熟知其可靠性的基础之上，这能带来心理上的安全感。而对大多数人而言，人工智能仍十分新颖、陌生。即便其原理能从技术层面解释（多数情况下并不能），人工智能的决策过程也大多晦涩难懂。与无法理解的事物接触，会让人产生焦虑，进而产生失控感。
Many people are also simply not familiar with many instances of AI actually working, because it often happens in the background. Instead, they are acutely aware of instances where AI goes wrong. Embarrassing AI failures receive a disproportionate amount of media attention, emphasising the message that we cannot rely on technology. Machine learning is not foolproof, in part because the humans who design it aren't.
此外，人工智能的诸多成功应用大多隐藏在幕后，人们对此知之甚少；相反，人们对人工智能出错的案例却印象深刻。人工智能的各类尴尬失误被媒体过度报道，不断传递出 “我们不能依赖科技” 的信号。机器学习并非万无一失，部分原因在于设计算法的人类本身也并非完美。
D Feelings about AI run deep. In a recent experiment, people from a range of backgrounds were given various sci-fi films about AI to watch and then asked questions about automation in everyday life. It was found that,regardless of whether the film they watched depicted AI in a positive or negative light, simply watching a cinematic vision of our technological future polarised the participants' attitudes. Optimists became more extreme in their enthusiasm for AI and sceptics became even more guarded.
D
人们对人工智能的看法根深蒂固。近期一项实验中，研究人员让不同背景的受试者观看各类有关人工智能的科幻影片，随后询问他们对日常生活中自动化技术的看法。结果发现，无论影片将人工智能塑造为正面还是负面形象，仅仅观看这类科技未来的影视画面，就会让受试者的态度走向极端：乐观者对人工智能愈发热衷，质疑者则愈发警惕。
This suggests people use relevant evidence about AI in a biased manner to support their existing attitudes, a deep-rooted human tendency known as “confirmation bias”. As AI is represented more and more in media and entertainment,it could lead to a society split between those who benefit from AI and those who reject it. More pertinently, refusing to accept the advantages offered by AI could place a large group of people at a serious disadvantage.
这表明人们会带着偏见筛选人工智能的相关信息，以此印证自身原有观点，这是人类一种根深蒂固的倾向，即 “证实性偏见”。随着人工智能在媒体与娱乐作品中不断出现，社会或将分裂为受益于人工智能的群体与抵制人工智能的群体。更关键的是，拒绝接受人工智能带来的红利，会让大批人陷入严重的劣势。
E Fortunately, we already have some ideas about how to improve trust in AI. Simply having previous experience with AI can significantly improve people's opinions about the technology, as was found in the study mentioned above. Evidence also suggests the more you use other technologies such as the internet, the more you trust them.
Another solution may be to reveal more about the algorithms which AI uses and the purposes they serve. Several high-profile social media companies and online marketplaces already release transparency reports about government requests and surveillance disclosures. A similar practice for AI could help people have a better understanding of the way algorithmic decisions are made.
E
所幸，我们已经掌握了一些提升人们对人工智能信任度的方法。上述研究发现，只要接触、使用过人工智能，人们对这项技术的评价就会显著改善。也有证据表明，人们使用互联网等其他技术的频率越高，对其信任度就越高。
另一种解决办法是，更多地公开人工智能所使用的算法及其用途。多家知名社交媒体与电商平台已发布透明度报告，公示政府的数据调取要求及监控信息披露情况。人工智能领域若效仿此种做法，能够帮助人们更好地理解算法的决策逻辑。
F Research suggests that allowing people some control over AI decision-making could also improve trust and enable AI to learn from human experience. For example, one study showed that when people were allowed the freedom to slightly modify an algorithm, they felt more satisfied with its decisions, more likely to believe it was superior and more likely to use it in the future.
We don't need to understand the intricate inner workings of AI systems, but if people are given a degree of responsibility for how they are implemented, they will be more willing to accept AI into their lives.
F
研究表明，让人们对人工智能的决策拥有一定控制权，同样可以提升信任度，还能让人工智能从人类经验中学习。例如，有研究显示，当人们可以小幅调整算法时，会对其决策结果更满意，更认可其优越性，也更愿意在未来使用它。
我们无需弄懂人工智能系统复杂的内部运行机制，但只要人们能在其落地应用中拥有一定话语权，就会更愿意接纳人工智能融入日常生活。
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