8%-10%的算法建议是 "糟糕的",但......一项探索性风险效用荟萃分析及其对监管的影响

IF 20.1 1区 管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE International Journal of Information Management Pub Date : 2023-12-29 DOI:10.1016/j.ijinfomgt.2023.102743
Martin Hilbert , Arti Thakur , Pablo M. Flores , Xiaoya Zhang , Jee Young Bhan , Patrick Bernhard , Feng Ji
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引用次数: 0

摘要

我们对推荐算法提供 "好 "和 "坏 "推荐的频率进行了粗粒度但广泛的定量评估,重点关注后者。我们从 33 项研究中找到了 151 项算法审核,这些研究报告了来自 YouTube、谷歌搜索、Twitter、Facebook、TikTok、亚马逊等的风险效用拟合统计数据。我们的研究结果表明,约有 8%-10% 的算法推荐是 "坏的",而约有四分之一的算法推荐积极保护用户免受自我伤害("好的")。这一平均值在所有审计中都非常一致,与平台和风险类型(偏见/歧视、心理健康和儿童伤害、错误信息或政治极端主义)无关。算法审计发现,负面反馈循环可能会使用户陷入 "坏 "推荐(或被 "拖入兔子洞")的漩涡,但同时也凸显了 "好推荐 "的正面漩涡的更大可能性。虽然我们的分析没有对风险的因果关系和严重程度做出任何判断,但检测到的风险水平超过了许多其他消费品。它们与美国 FDA 或 FSIS 等公共机构监测的普通食品缺陷的风险水平相当。因此,我们的研究结果为正在进行的有关推荐算法潜在风险监管的讨论提供了信息。
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8–10% of algorithmic recommendations are ‘bad’, but… an exploratory risk-utility meta-analysis and its regulatory implications

We conducted a quantitatively coarse-grained, but wide-ranging evaluation of the frequency recommender algorithms provide ‘good’ and ‘bad’ recommendations, with a focus on the latter. We found 151 algorithmic audits from 33 studies that report fitting risk-utility statistics from YouTube, Google Search, Twitter, Facebook, TikTok, Amazon, and others. Our findings indicate that roughly 8–10% of algorithmic recommendations are ‘bad’, while about a quarter actively protect users from self-induced harm (‘do good’). This average is remarkably consistent across the audits, irrespective of the platform nor on the kind of risk (bias/ discrimination, mental health and child harm, misinformation, or political extremism). Algorithmic audits find negative feedback loops that can ensnare users into spirals of ‘bad’ recommendations (or being ‘dragged down the rabbit hole’), but also highlight an even larger likelihood of positive spirals of ‘good recommendations’. While our analysis refrains from any judgment of the causal consequences and severity of risks, the detected levels surpass those associated with many other consumer products. They are comparable to the risk levels of generic food defects monitored by public authorities such as the FDA or FSIS in the United States. Consequently, our findings inform the ongoing discussion regarding regulatory oversight of the potential risks posed by recommender algorithms.

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来源期刊
International Journal of Information Management
International Journal of Information Management INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
53.10
自引率
6.20%
发文量
111
审稿时长
24 days
期刊介绍: The International Journal of Information Management (IJIM) is a distinguished, international, and peer-reviewed journal dedicated to providing its readers with top-notch analysis and discussions within the evolving field of information management. Key features of the journal include: Comprehensive Coverage: IJIM keeps readers informed with major papers, reports, and reviews. Topical Relevance: The journal remains current and relevant through Viewpoint articles and regular features like Research Notes, Case Studies, and a Reviews section, ensuring readers are updated on contemporary issues. Focus on Quality: IJIM prioritizes high-quality papers that address contemporary issues in information management.
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