图片出处:《东方日报》 |
近期由前谷歌大脑(Google Brain)主管、Deep Learning AI创办人,现为Landing AI首席执行官吴恩达(Andrew Ng)所主编的《The Batch》人工智能周讯,以及6月5日《麻省理工学院科技评论 . 算法 - 揭开人工智能的神秘面纱》每周快报(MIT Technology Review - The Algorithm Weekly Newsletter),
另外,美国知名科技杂志《连线》(Wired)
这个由《连线》杂志制作的视频直截了当的表示,
事实上,早在2016年,美国调查式新闻网站《
我们应该如何杜绝或至少减低人工智能的偏差呢?
专家们建议人工智能系统开发团队成员的背景应该更加多元。
还有就是确保用来培训人工智能建模的数据具备广泛的代表性,
也许让人工智能不偏不倚、绝对公正客观的执行日常细琐任务,
这是因为,迄今为止最流行的两大人工智能技术流派,
以人工神经网络(Artificial Neural Network)为代表的联结主义(
但是,值得探讨的是,
另一方面,以专家系统(Expert System)为代表的符号主义(Symbolism),
防范和纠正资讯系统算法所产生的偏差,是人们共有的责任。 偏颇的系统算法和结论不仅仅在情感上伤害了受到歧视的人士,
客观上来说,人工智能理应减少人类对数据的主观诠释,
文章以上下篇的形式发表于2020年6月11日以及6月12日的《东方日报》
参考资料/Reference
[1] 《The Batch》6月3日人工智能周讯
[2] 《麻省理工学院科技评论 . 算法 - 揭开人工智能的神秘面纱》6月5日号每周快报
https://forms.
[3] MIT Technology Review: The activist dismantling racist police algorithms
[4] Business Insider: How Silicon Valley leaders, from Elon Musk to Tim Cook, are responding to the George Floyd protests
[5] The Verge: Big Tech companies are responding to George Floyd in a way they never did for Michael Brown
[6] Wired: How Algorithms Become Biased
https://www.facebook.com/
[7] Harvard Business Review: What Do We Do About the Biases in AI?
https://hbr.org/2019/10/what-
[8] Harvard Business Review: 4 Ways to Address Gender Bias in AI
https://hbr.org/2019/11/4-ways-to-address-gender-bias-in-ai
[9] Hackernoon: The simplest explanation of machine learning you'll ever read
https://hackernoon.com/the-
[10] McKinsey Global Institute: Tackling bias in artificial intelligence (and in humans)
[11] Wiley Online Library: Bias in data‐driven artificial intelligence systems—An introductory survey
https://onlinelibrary.wiley.
[12] Google Developers: Text Embedding Models Contain Bias. Here's Why That Matters.
https://developers.googleblog.
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