生活中的观察者偏见例子_人工智能中的系统偏见

生活中的观察者偏见例子

Recently, Dr. Jennifer Lincoln made a TikTok highlighting the multitude of ways that African Americans face discrimination in healthcare such as receiving less pain medication and waiting longer in emergency rooms. The video, based on this study published in the Proceedings of the National Academy of Sciences (PNAS), has gone viral with 400k+ views on TikTok and nearly 8 million views on Twitter. If an AI model was trained on healthcare records to predict painkiller dosages for patients, it may recommend lower dosages for African American patients because it was trained on a dataset where African American patients received lower dosages. Clearly, this would become very problematic since this hypothetical use case of AI would further institutionalize racism.

最近，詹妮弗·林肯(Jennifer Lincoln)博士做了一次TikTok，重点介绍了非洲裔美国人在医疗保健方面面临歧视的多种方式，例如减少服用止痛药和在急诊室等待的时间更长。 该视频基于发表在《美国国家科学院院刊》(PNAS)上的这项研究成果，在TikTok上的观看次数超过40万，在Twitter上的观看次数达到了近800万，从而引起了轰动。 如果在医疗记录上训练了AI模型以预测患者的止痛药剂量，则可能会建议非裔美国人使用较低的剂量，因为它是在非裔美国人患者接受较低剂量的数据集上进行训练的。 显然，这将变得非常成问题，因为AI的这种假设用例将进一步使种族主义制度化。

偏见的后果 (Consequences of Bias)

As AI becomes increasingly integrated into current systems, systematic bias is an important risk that cannot be overlooked. When models are fed data where a bias towards a certain ethnicity or gender exists, they aren’t able to serve their intended purpose effectively. A model evaluated on a metric such as accuracy, profit etc would attempt to maximize said metric without any regard for biases it forms. If steps aren’t taken to combat this issue, humans, or regulators, more importantly, might lose faith in AI, preventing us from unlocking the potential of the technology. To comprehend the severity of this problem, here are two more frightening examples of bias in AI.

随着AI越来越多地集成到当前系统中，系统性偏差已成为不可忽视的重要风险。 当向模型提供数据时，其中存在对特定种族或性别的偏见，则它们将无法有效地实现其预期目的。 在诸如准确性，利润等度量标准上评估的模型将尝试在不考虑其形成的偏差的情况下最大化所述度量标准。 如果不采取措施解决这个问题，更重要的是，人类或监管者可能会失去对AI的信心，从而阻止我们释放技术的潜力。 为了理解这个问题的严重性，这里有两个更令人恐惧的AI偏见示例。

• As outlined in the paper “The Risk of Racial Bias in Hate Speech Detection”, Researchers at the University of Washington tested Google’s AI hate speech detector on over 5 million tweets and discovered that tweets from African Americans were twice as liable to be classified as toxic speech compared to people of other races.

  如论文“ 仇恨语音检测中的种族偏见风险 ”所述，华盛顿大学的研究人员在超过500万条推文上测试了Google的AI仇恨语音检测器，发现来自非洲裔美国人的推文被归类为有毒的两倍。演讲相比其他种族的人。

• COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) is an algorithm used by New York, California and other states to predict the risk of released prisoners committing another crime. In the research article “The accuracy, fairness, and limits of predicting recidivism”, researchers at Dartmouth concluded that “Black defendants who did not recidivate were incorrectly predicted to reoffend at a rate of 44.9%, nearly twice as high as their white counterparts at 23.5%; and white defendants who did recidivate were incorrectly predicted to not reoffend at a rate of 47.7%, nearly twice as high as their black counterparts at 28.0%.” This is extremely troubling given that COMPAS scores can influence the length of a defendant’s sentence.

  COMPAS(替代性制裁的更正罪犯管理分析)是纽约，加利福尼亚和其他州使用的算法，用于预测释放的囚犯犯下另一种罪行的风险。 在研究文章“ 预测累犯的准确性，公平性和局限性 ”中，达特茅斯大学的研究人员得出以下结论：“未犯罪的黑人被告被错误地预测会以44.9％的比率再次犯罪，几乎是白人的两倍。 23.5％； 被误判的白人和被告被错误地预测不会以47.7％的比率再次犯罪，几乎是黑人被告的28.0％的两倍。 考虑到COMPAS分数会影响被告的刑期，这是非常令人不安的。

对抗偏差 (Fighting Bias)

A concern that many critics of AI are vocal about is the “black box” nature of artificial neural networks: a machine learning model can provide an answer to the question we ask, but we can’t understand how the model arrived at that answer due to the complexity of the calculations involved. This opaqueness allows bias to creep in unnoticed. Even beyond bias, consumers/businesses are interested in understanding how AI arrives at its conclusions.

许多AI批评家都在抱怨一个问题，就是人工神经网络的“黑匣子”性质：机器学习模型可以为我们提出的问题提供答案，但是我们无法理解该模型是如何得出该答案的涉及的计算复杂性。 这种不透明性会导致偏差逐渐消失。 甚至超越偏见，消费者/企业也有兴趣了解AI如何得出其结论。

One potential solution for elucidating how AI makes high stakes decisions is interpretable machine learning. As the name suggests, interpretable machine learning involves creating models whose decisions making process is more understandable than black box models. To become interpretable, these models are designed with factors such as additional constraints and the input of domain experts. For example, an additional constraint to prevent bias in loan applications would be compliance: the model must adhere to fair lending laws by not discriminating against consumers of a certain race.

阐明AI如何做出重大决策的一种潜在解决方案是可解释的机器学习。 顾名思义，可解释的机器学习涉及创建模型，其决策过程比黑匣子模型更容易理解。 为了便于解释，这些模型的设计考虑了诸如附加约束和领域专家的意见等因素。 例如，防止贷款申请出现偏差的另一个约束条件是合规性：该模型必须遵守公平贷款法，不得歧视特定种族的消费者。

While interpretable machine learning models are more time consuming and expensive to develop due to their increased complexity, the layer of interpretability is definitely worth it for applications like autonomous vehicles, healthcare, or criminal justice where errors have serious repercussions. Human nature makes society resistant to change but more transparent models can begin to make the public/government more receptive to widespread adoption of AI.

尽管可解释性机器学习模型由于复杂性的增加而花费更多时间和开发成本，但可解释性层绝对值得在错误引起严重后果的应用中使用，例如自动驾驶汽车，医疗保健或刑事司法。 人性使社会抵制变革，但更加透明的模式可以开始使公共/政府更容易接受人工智能的广泛采用。

Other potential solutions focus on the data the model uses instead of how the model uses data. One proposed method involves taking large datasets that would typically remain confidential (ex. medical data) and release them to the public after removing personally identifiable information. The idea is that bias will be filtered out in these anonymous datasets. Yet, this tactic comes with its own risks as hackers can cross reference data to make it past the layer of anonymity. At the same time, consciously including underrepresented populations would bolster datasets which lack diversity. Finally, fostering diversity among the engineers designing these algorithms should help fight bias.

其他可能的解决方案着重于模型使用的数据，而不是模型如何使用数据。 一种建议的方法涉及获取通常将保持机密的大型数据集(例如医疗数据)，并在删除个人身份信息后将其发布给公众。 这个想法是，偏见将在这些匿名数据集中被滤除。 但是，这种策略有其自身的风险，因为黑客可以交叉引用数据以使其经过匿名层。 同时，有意识地包括代表性不足的人群将支持缺乏多样性的数据集。 最后，在设计这些算法的工程师中培养多样性应有助于消除偏见。

As AI rapidly advances, proactively combating bias has to remain a priority.

随着AI的快速发展，积极应对偏见仍然是当务之急。

Thanks for reading!

谢谢阅读！

I’m Roshan, a 16 year old passionate about the applications of AI. If you’re further interested in AI, check out my article on the applications of AI in healthcare.

我是Roshan，他16岁就对AI的应用充满热情。 如果您对AI进一步感兴趣，请查看我有关AI在医疗保健中的应用的文章 。

Reach out to me on Linkedin: https://www.linkedin.com/in/roshan-adusumilli/

在Linkedin上与我联系： https ： //www.linkedin.com/in/roshan-adusumilli/

翻译自: https://towardsdatascience.com/systematic-bias-in-artificial-intelligence-8698ffa20d57

生活中的观察者偏见例子