Yet increasing evidence suggests that human prejudices have been baked into these tools because the machine-learning models are trained on biased data. Far from avoiding racism, they may simply be better at hiding it. Many critics now view these tools as a form of tech-washing, where an appearance of objectivity covers mechanisms that perpetuate inequities in society.

思考题：

What is the author’s attitude towards these tools?

(A) biased (B) skeptical (C) objective (D) tolerance

词汇突破：

1.increasing evidence 越来越多的证据

2.human prejudices 人类的偏见

3.the machine-learning models机器学习的模式

4.biased 歧视性的

5.Far from：远没有

6. simply干脆

7.tech-washing 技术性洗白

8. mechanisms 机制

9.perpetuate 使…持续

10.inequities 不平等

句子解析：

句：

Yet increasing evidence suggests that// human prejudices have been baked into these tools because the machine-learning models are trained on biased data.

参考译文：然而，越来越多的证据表明，这些工具中已经植入了人们的偏见，因为机器学习模型是由歧视性的数据训练的。

第二句：

Far from avoiding racism, // they may simply be better at hiding it.

参考译文：它们远未避免种族主义，可能只是更擅长隐藏种族主义。

第三句：

Many critics now view these tools as a form of tech-washing, where a veneer of objectivity covers mechanisms that perpetuate inequities in society.

参考译文：现在，许多批评家将这些工具视为一种技术洗白，用表面的客观来掩盖延续社会不平等的机制。

思考题解析：

What is the author’s attitude towards these tools?

(A) biased (B) skeptical (C) objective (D) tolerance

从第二句中可以知道答案为B!

文章来源背景和来源：

Predictive policing algorithms are racist. They need to be dismantled.

预测性警用算法是种族主义的。它们需要被解除。

Source: https://www.technologyreview.com/2020/07/17/1005396/predictive-policing-algorithms-racist-dismantled-machine-learning-bias-criminal-justice/

背景：根据历史经验和现时环境预测未来的能力历来是人类智能的桂冠。在古老的原始部落，人们就尊崇这方面能力出众的巫师们，给予他们极高的社会地位。而现在，我们期待机器学习和人工智能掀起一场新的革命，从大数据中读取解锁未来的钥匙。然而，最早借助这些预测技术的美国警察们却发现，旨在帮助他们超越个人偏见的算法，却因为训练数据的“原罪”而天然地带有系统性歧视。本这些算法不仅没有维持中立和客观，还以技术的外衣洗白了种族歧视。就像电脑普及后，我们可以大大方方地把自己的错误归结为电脑故障，歧视也得以“无辜脸”地隐身于算法之中。