Last week,新当选的美国Rep.Alexandria Ocasio-Cortez made headlines when she said,作为第四届MLK Now年度活动的一部分，thatfacial-recognition 澳门金沙网络娱乐场technologies and algorithms“总是有这些被翻译成种族不平等，because algorithms are still made by human beings,这些算法仍然与人类的基本假设联系在一起。They're just automated.自动假设-如果你不纠正偏见，then you're just automating the bias."
It turns out that the output from algorithms can indeed produce biased results.数据科学家说计算机程序，神经网络，machine learning algorithms and artificial intelligence (AI) work because they learn how to behave from data they are given.Software is written by humans,谁有偏见，and training data is also generated by humans who have bias.
The two stages ofmachine learningshow how this bias can creep into a seemingly automated process.In the first stage,the training stage,an algorithm learns based on a set of data or on certain rules or restrictions.第二阶段是推理阶段，in which an algorithm applies what it has learned in practice.This second stage reveals an algorithm's biases.For example,如果一个算法只训练有长头发的女人的照片，then it will think anyone with short hair is a man.
谷歌臭名昭著came under firein 2015 when Google Photos labeled black people as gorillas,likely because those were the only dark-skinned beings in the training set.
And bias can creep in through many avenues."A common mistake is training an algorithm to make predictions based on past decisions from biased humans," Sophie Searcy,数据科学训练营Metis的高级数据科学家，告诉现场科澳门金沙网上游戏学。"If I make an algorithm to automate decisions previously made by a group of loan officers,I might take the easy road and train the algorithm on past decisions from those loan officers.But then,of course,如果贷款人员有偏见，then the algorithm I build will continue those biases."
Searcy cited the example of COMPAS,a predictive tool used across the U.S.criminal justicesystem for sentencing,which tries to predict where crime will occur.ProPublica进行了澳门金莎网上游戏分析on COMPAS and found that,after controlling for other statistical explanations,该工具高估了黑人被告再犯的风险，并一贯低估了白人被告的风险。
To help combat algorithmic biases,西尔西告诉《现场科学》，澳门金沙网上游戏engineers and data scientists should be building more-diverse data sets for new problems,as well as trying to understand and mitigate the bias built in to existing data sets.
首先，Ira Cohen说，预测分析公司Anodoot的数据科学家，如果工程师正在训练一种识别种族或性别属性的算法，他们应该有一套对所有人口类型具有相对统一表示的训练集。澳门金莎网上游戏"It is important to represent enough examples from each population group,even if they are a minority in the overall population being examined," Cohen told 澳门金沙网上游戏Live Science.Finally,Cohen recommends checking for biases on a test set that includes people from all these groups."If,对于某个种族，the accuracy is statistically significantly lower than the other categories,the algorithm may have a bias,and I would evaluate the training data that was used for it," Cohen told LiveScience.For example,if the algorithm can correctly identify 900 out of 1,000 white faces,but correctly detects only 600 out of 1,000 asian faces,那么这个算法可能对亚洲人有偏见，Cohen added.
Removing bias can be incredibly challenging for AI.
即使是谷歌，被认为是商业人工智能的先驱，apparently couldn't come up with a comprehensive solution to its gorilla problem from 2015.有线建立它没有找到区分有色人种和大猩猩的算法，Google simply blocked its image-recognition algorithms from identifying gorillas at all.
Google's example is a good reminder that training AI software can be a difficult exercise,particularly when software isn't being tested or trained by a representative and diverse group of people.
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