abusesaffiliationarrow-downarrow-leftarrow-rightarrow-upattack-typeburgerchevron-downchevron-leftchevron-rightchevron-upClock iconclosedeletedevelopment-povertydiscriminationdollardownloademailenvironmentexternal-linkfacebookfiltergenderglobegroupshealthC4067174-3DD9-4B9E-AD64-284FDAAE6338@1xinformation-outlineinformationinstagraminvestment-trade-globalisationissueslabourlanguagesShapeCombined Shapeline, chart, up, arrow, graphLinkedInlocationmap-pinminusnewsorganisationotheroverviewpluspreviewArtboard 185profilerefreshIconnewssearchsecurityPathStock downStock steadyStock uptagticktooltiptwitteruniversalityweb

このページは 日本語 では利用できません。English で表示されています

記事

2021年3月11日

著者:
Karen Hao, MIT Technology Review

Facebook's AI algorithms make misinformation & hate speech hard to uproot

"How Facebook got addicted to spreading misinformation", 11 March 2021

... The models that maximize engagement also favor controversy, misinformation, and extremism: put simply, people just like outrageous stuff. Sometimes this inflames existing political tensions. The most devastating example to date is the case of Myanmar, where viral fake news and hate speech about the Rohingya Muslim minority escalated the country’s religious conflict into a full-blown genocide. Facebook admitted in 2018, after years of downplaying its role, that it had not done enough “to help prevent our platform from being used to foment division and incite offline violence.”

... To catch things before they go viral, content-moderation models must be able to identify new unwanted content with high accuracy. But machine-learning models do not work that way. An algorithm that has learned to recognize Holocaust denial can’t immediately spot, say, Rohingya genocide denial. It must be trained on thousands, often even millions, of examples of a new type of content before learning to filter it out. Even then, users can quickly learn to outwit the model by doing things like changing the wording of a post or replacing incendiary phrases with euphemisms, making their message illegible to the AI while still obvious to a human. This is why new conspiracy theories can rapidly spiral out of control, and partly why, even after such content is banned, forms of it can persist on the platform.

... When I described the Responsible AI team’s work to other experts on AI ethics and human rights, they noted the incongruity between the problems it was tackling and those, like misinformation, for which Facebook is most notorious... “It seems like the ‘responsible AI’ framing is completely subjective to what a company decides it wants to care about. It’s like, ‘We’ll make up the terms and then we’ll follow them,’” says Ellery Roberts Biddle, the editorial director of Ranking Digital Rights... “I don’t even understand what they mean when they talk about fairness. Do they think it’s fair to recommend that people join extremist groups, like the ones that stormed the Capitol? If everyone gets the recommendation, does that mean it was fair?”

タイムライン

プライバシー情報

このサイトでは、クッキーやその他のウェブストレージ技術を使用しています。お客様は、以下の方法でプライバシーに関する選択肢を設定することができます。変更は直ちに反映されます。

ウェブストレージの使用についての詳細は、当社の データ使用およびクッキーに関するポリシーをご覧ください

Strictly necessary storage

ON
OFF

Necessary storage enables core site functionality. This site cannot function without it, so it can only be disabled by changing settings in your browser.

クッキーのアナリティクス

ON
OFF

When you access our website we use Google Analytics to collect information on your visit. Accepting this cookie will allow us to understand more details about your journey, and improve how we surface information. All analytics information is anonymous and we do not use it to identify you. Google provides a Google Analytics opt-out add on for all popular browsers.

Promotional cookies

ON
OFF

We share news and updates on business and human rights through third party platforms, including social media and search engines. These cookies help us to understand the performance of these promotions.

本サイトにおけるお客様のプライバシーに関する選択

このサイトでは、必要なコア機能を超えてお客様の利便性を高めるために、クッキーやその他のウェブストレージ技術を使用しています。