Facebook一直以來都是一家專注於增長的公司。更多的使用者和更多的參與意味著更多的收入。這種一心一意的代價在麻省理工學院技術評論（mittechnologyreview）的這篇精彩故事中得到了清晰的闡述。它詳細說明了該公司的人工智慧團隊使用機器學習解決錯誤資訊的嘗試，顯然是由於Facebook不願限制使用者參與度而受到阻礙的。

“如果一個模型降低了太多的參與度，它就會被丟棄。否則，它會被部署並持續監控，”Facebook機器學習模型的作者karenhao寫道。但這種做法很快就引起了一些問題。最大化參與度的模式也有利於爭議、錯誤資訊和極端主義：簡單地說，人們喜歡**的東西。”

在Twitter上，郝女士指出，這篇文章不是關於“**分子（做）**的事情”，相反，她說，“這是關於好人真正努力做正確的事情。但他們被困在一個腐朽的體系中，盡最大努力推動不會改變的現狀。”

這篇報道也為指責Facebook在唐納德·特朗普（Donald Trump）擔任總統期間安撫保守派的願望導致其對右翼錯誤資訊視而不見提供了更多證據。這似乎至少部分是由於喬爾·卡普蘭（Joel Kaplan）的影響，喬爾·卡普蘭曾是喬治·W·布什（George W.Bush）**的一名成員，現在是Facebook全球公共政策副總裁和“最高級別的共和黨人”

All Facebook users have some 200 “traits” attached to their profile. These include various dimensi*** submitted by users or estimated by machine-learning models, such as race, political and religious leanings, socioeconomic class, and level of education. Kaplan’s team began using the traits to assemble custom user segments that reflected largely c***ervative interests: users who engaged with c***ervative content, groups, and pages, for example. Then they’d run special ****yses to see how content-moderation decisi*** would affect posts from those segments, according to a former researcher whose work was subject to those reviews.

The Fairness Flow documentation, which the Resp***ible AI team wrote later, includes a case study on how to use the tool in such a situation. When deciding whether a misinformation model is fair with respect to political ideology, the team wrote, “fairness” does not mean the model should affect c***ervative and liberal users equally. If c***ervatives are posting a greater fraction of misinformation, as judged by public c***ensus, then the model should flag a greater fraction of c***ervative content. If liberals are posting more misinformation, it should flag their content more often too.

But members of Kaplan’s team followed exactly the opposite approach: they took “fairness” to mean that these models should not affect c***ervatives more than liberals. When a model did so, they would stop its deployment and demand a change. Once, they blocked a medical-misinformation detector that had noticeably reduced the reach of anti-vaccine campaigns, the former researcher told me. They told the researchers that the model could not be deployed until the team fixed this discrepancy. But that effectively made the model meaningless. “There’s no point, then,” the researcher says. A model modified in that way “would have literally no impact on the actual problem” of misinformation.

報道還說，Facebook的人工智慧研究人員在演算法偏差問題上的工作已經展開，在這個問題上，機器學習模型無意中歧視了某些使用者群體，至少在一定程度上是為了搶先這些反保守情緒的指責，並阻止美國**的潛在監管。但是，把更多的資源投入到偏見中，就意味著忽視了涉及錯誤資訊和仇恨言論的問題。儘管該公司口口聲聲說要AI公平，但指導原則，郝說，仍然一如既往：增長，增長，增長。

[T]esting algorithms for fairness is still largely optional at Facebook. None of the teams that work directly on Facebook’s news feed, ad service, or other products are required to do it. Pay incentives are still tied to engagement and growth metrics. And while there are guidelines about which fairness definition to use in any given situation, they aren’t enforced.

你可以在麻省理工學院技術評論上閱讀郝的完整故事。