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  3. Social platforms large and small are struggling to keep their communities safe from hate speech, extremist content, harassment a

Social platforms large and small are struggling to keep their communities safe from hate speech, extremist content, harassment a

admin2022-11-01  90
问题     Social platforms large and small are struggling to keep their communities safe from hate speech, extremist content, harassment and misinformation. Most recently, far-right agitators posted openly about plans to storm the U.S. Capitol before doing just that on January 6. One solution might be AI: developing algorithms to detect and alert us to toxic and inflammatory comments and flag them for removal. But such systems face big challenges.
    The prevalence of hateful or offensive language online has been growing rapidly in recent years, and the problem is now rampant. In some cases, toxic comments online have even resulted in real life violence, from religious nationalism in Myanmar to neo-Nazi propaganda in the U.S. Social media platforms, relying on thousands of human reviewers, are struggling to moderate the ever-increasing volume of harmful content. In 2019, it was reported that Facebook moderators are at risk of suffering from PTSD as a result of repeated exposure to such distressing content. Outsourcing this work to machine learning can help manage the rising volumes of harmful content, while limiting human exposure to it. Indeed, many tech giants have been incorporating algorithms into their content moderation for years.
    One such example is Google’s Jigsaw, a company focusing on making the internet safer. In 2017, it helped create Conversation AI, a collaborative research project aiming to detect toxic comments online. However, a tool produced by that project, called Perspective, faced substantial criticism. One common complaint was that it created a general "toxicity score" that wasn’t flexible enough to serve the varying needs of different platforms. Some Web sites, for instance, might require detection of threats but not profanity, while others might have the opposite requirements. Another issue was that the algorithm learned to conflate toxic comments with nontoxic comments that contained words related to gender, sexual orientation, religion or disability.
    Following these concerns, the Conversation AI team invited developers to train their own toxicity-detection algorithms and enter them into three competitions hosted on Kaggle, a Google subsidiary known for its community of machine learning practitioners, public data sets and challenges. To help train the AI models, Conversation AI released two public data sets containing over one million toxic and non-toxic comments from Wikipedia and a service called Civil Comments. The comments were rated on toxicity by annotators, with a "Very Toxic" label indicating "a very hateful, aggressive, or disrespectful comment that is very likely to make you leave a discussion or give up on sharing your perspective," and a "Toxic" label meaning "a rude, disrespectful, or unreasonable comment that is somewhat likely to make you leave a discussion or give up on sharing your perspective."
AI is used in detecting toxic online content to__________.
选项 A、eliminate religious nationalism
B、snatch jobs from human
C、reduce human suffering from negative effect
D、crack down on neo-Nazi propaganda
答案C
解析 由题干关键词AI is used in detecting toxic online content定位到文章第二段最后两句话：“将这项工作外包给机器学习有助于管理日益增多的有害言论，同时限制人类接触这些内容。事实上，许多科技巨头多年来一直在将算法应用于内容审核。”由此可知，人工智能用于检测网上有害内容的原因有两个：不良内容日益增多；人工审查会给人带来不良影响。选项[C]“减少人类遭受的负面影响”符合原文所述内容，故为答案。
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