在线社交网络中的算法介导的社会学习
William J Brady1, Joshua Conrad Jackson1, Björn Lindström2
1Northwestern University, Kellogg School of Management, Evanston, IL, USA.
Trends in cognitive sciences
|August 5, 2023
概括
在线算法利用人类的社会学习偏见,放大声望,内组,道德和情感信息 (PRIME). 这种算法介导的社会学习可以导致误解,冲突和错误信息的传播.
科学领域:
- 社会心理学 社会心理学
- 数字媒体研究数字媒体研究
- 人与计算机的互动.
背景情况:
- 人类的社会学习正在转向像Twitter,Facebook和TikTok这样的在线平台.
- 这些平台上的算法利用社会学习偏见来最大限度地提高用户参与度.
- 现有的偏见包括对声望,内组,道德和情感信息 (PRIME) 的偏好.
研究的目的:
- 综合对算法介导的社会学习的见解.
- 提出一个框架来理解其后果,称为功能失调.
- 确定减轻负面影响的潜在解决方案.
主要方法:
- 关于社会学习和算法的新兴研究的文学综合.
- 开发一个概念框架,分析人与算法的交互.
- 讨论潜在的干预措施及其影响.
主要成果:
- 以算法为媒介的社会学习通过人与算法的互动来放大PRIME信息.
- 这种放大导致功能失调,导致社会误解和冲突.
- 这一过程有助于错误信息的广泛传播.
结论:
- 算法介导的社会学习由于利用认知偏见而带来风险.
- 功能失调可以通过算法调整来减少.
- 促进有限多样化和算法放大中的透明度是关键的解决方案.
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