Developing robust methods for the measurement of online social media populations

2016年6月，令人担忧的证据浮出水面，显示谷歌的搜索引擎是“种族主义的”。争论的焦点是搜索词“三个白人青少年”和“三个黑人青少年”返回的截然不同的图片。之前的搜索返回了青少年运动和闲逛的健康照片。后者退回了面部照片。谷歌为搜索结果进行了辩护，指出他们的搜索算法是由网络内容以及访问和链接的频率决定的。谷歌是一家运行软件的公司，每天有数百万人使用该软件在互联网上查找信息。该公司声称，他们搜索结果中的任何此类偏见都“从根本上是一个社会问题”。*从本质上讲，这就是一个难题。我们期待谷歌搜索以及从Yelp到OkCupid再到Facebook的无数其他在线服务来反映世界。在这样做的过程中，我们赋予了它加强其中社会结构的力量。当机器学习算法成为传播不受欢迎的刻板印象和边缘化少数人观点的工具时，我们就遇到了问题。*解决算法偏见带来了两个挑战。首先，我们必须检测到偏见何时存在。其次，在存在偏见并被认为有必要纠正的情况下，我们需要调整算法性能的方法。这些是我们在这项建议中考虑的主要任务。*我们将分三个阶段解决这些问题。首先，我们将开发方法来帮助检测算法偏见。第二，与领域专家合作，我们将使用我们的检测方法来详细描述用于检测仇恨言论和识别政治倾向的算法中存在的偏见。利用这些发现，我们将探讨这项提议的最后一个主题，即通过修改算法本身或调整算法所依据的数据来纠正算法偏见的方法。*随着越来越多的服务迁移到网上，我们必须确保这些系统反映社会的愿望，而不是它最糟糕和最具掠夺性的因素。我们希望这项工作将为公司提供可行的解决方案，以确保无偏见的系统，并希望我们的发现刺激在这一重要主题上的进一步工作。

In June 2016, troubling evidence surfaced showing Google's search engine to be “racist.” At issue were the radically different images returned by the search terms “three white teenagers” and “three black teenagers.” The former search returned wholesome pictures of teenagers playing sports and hanging out. The latter returned mugshots. Google defended the search results, indicating that their search algorithms were informed by web content and the frequency with which it was accessed and linked. Google - the company that runs software used daily by millions of people to find information on the internet - asserted that any such bias in their search results were “fundamentally a societal problem.”***And there, in essence, is the conundrum. We look to Google search along with myriad other online services from Yelp to OkCupid to Facebook to reflect the world. And in doing so, we give it the power to reinforce the social structures in it. When machine learning algorithms become vehicles for propagating undesirable stereotypes and marginalizing minority views which we call “algorithmic prejudice” - we have a problem.***Addressing algorithmic prejudice presents two challenges. First, we must detect when a prejudice exists. Second, where a prejudice exists and is deemed necessary to correct, we require methods for adjusting the algorithm's performance. These are the central tasks we consider in this proposal.***We will tackle these problems in three phases. First, we will develop methods to aid in the detection of algorithmic prejudice. Second, in collaboration with domain experts, we will use our detection methods to do a detailed characterization of the prejudice present in algorithms for detecting hate speech and identifying political orientation. Using these findings, we will approach the final topic of this proposal, which is methods for correcting algorithmic prejudice either by modifying the algorithms themselves or by adjusting the data that the algorithms are trained on.***As increasing numbers of services migrate online, we must ensure that these systems reflect the aspirations of society, not its worst and most predatory elements. Our hope is that this work will provide viable solutions for companies to ensure prejudice-free systems and that our findings stimulate further work on this important topics.