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Toxic recommender algorithms: immunities, liabilities and the regulated self-regulation of the Digital Services Act and the Online Safety Act

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Journal of Media Law 2025年第2期16卷

作者： Kohl, Uta University of Southampton Southampton United Kingdom Visiting Professor of Law and Technology CUHK Hong Kong

With focus on toxic recommender algorithms, the article argues that the platform immunities granted in the early 2000s are incompatible with the regulated self-regulation of the EU Digital Services Act 2022 [DSA] and the UK Online Safety Act 2023 [OSA]. Whilst the DSA/OSA’s regimes recognise platforms as active shapers of the online content sphere, the immunities remain stuck in the early view of the same platforms as passive providers of neutral infrastructure and so as innocent messengers. Thus the immunities continue to shelter platforms from liability for harm caused by their toxic-but-profitable algorithms that the DSA/OSA seek to restrain. The tension between the two regimes may be resolved by restricting the immunities to truly neutral platforms, that is those not substantially invested in the content they are meant to regulate. For all other platforms the DSA/OSA self-regulatory regimes and standard liabilities could and should run in parallel. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Digital Services Act harmful content immunities Online Safety Act recommender algorithms

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Truth as social practice in a digital era: iteration as persuasion

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AI & SOCIETY 2023年第5期38卷 2009-2023页

作者： Foster, Clare L. E. Univ Cambridge Ctr Res Arts Social Sci & Humanities CRASSH Cambridge England

This article reflects on the problem of false belief produced by the integrated psychological and algorithmic landscape humans now inhabit. Following the work of scholars such as Lee McIntyre (Post-Truth, MIT Press, 2018) or Cailin O'Connor and James Weatherall (The Misinformation Age: How False Beliefs Spread, Yale University Press, 2019) it combines recent discussions of fake news, post-truth, and science denialism across the disciplines of political science, computer science, sociology, psychology, and the history and philosophy of science that variously address the ineffectiveness, in a digital era, of countering individual falsehoods with facts. Truth and falsehood, it argues, rather than being seen as properties or conditions attached to individual instances of content, should now be seen as collective, performative, and above all persuasive phenomena. They should be practically evaluated as networked systems and mechanisms of sharing in which individually targeted actions are combining with structural tendencies (both human and mechanical) in unprecedented ways. For example, the persuasive agency of apparent consensus (clicks, likes, bots, trolls) is newly important in a fractured environment that only appears to be, but is no longer `public';the control of narratives, labels, and associations is a live, time-sensitive issue, a continuous contest, or ongoing cusp. Taking a social approach to truth yields observations of new relevance;from how current strategies of negative cohesion, blame, and enemy-creation depend crucially on binary ways of constructing the world, to how the offer of identity/community powerfully cooperates with the structural tendencies of algorithm-driven advertiser platforms towards polarisation. Remedies for these machine-learned and psychological tendencies lie in end-user education. So the Arts and Humanities, whether via comparisons with previous historical periods, or via principles of critical thinking and active reading, offer cru

关键词： Misinformation Post-truth Fake news Repetition Persuasive technology Digital public sphere Social media recommender algorithms Polarisation Binary oppositions

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8-10% of algorithmic recommendations are 'bad', but... an exploratory risk-utility meta-analysis and its regulatory implications

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INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2024年 75卷

作者： Hilbert, Martin Thakur, Arti Flores, Pablo M. Zhang, Xiaoya Bhan, Jee Young Bernhard, Patrick Ji, Feng Univ Calif Davis Dept Commun Davis CA 95616 USA Univ Florida Dept Family Youth & Community Sci Gainesville FL USA Univ Calif Davis Dept Sociol Davis CA USA Univ Calif Davis Dept Polit Sci Davis CA USA Univ Toronto Dept Appl Psychol & Human Dev Toronto ON Canada

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.

关键词： recommender algorithms Algorithmic auditing Machine behavior Meta-analysis Digital harms

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Better Edges not Bigger Graphs: An Interaction-Driven Friendship recommender Algorithm for Social Networks 5

Better Edges not Bigger Graphs: An Interaction-Driven Friend...

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5th International Conference on Cloud Computing and Artificial Intelligence - Technologies and Applications (CloudTech)

作者： Alshammari, Aadil Rezgui, Abdelmounaam New Mexico Inst Min & Technol Dept Comp Sci & Engn Socorro NM 87801 USA Illinois State Univ Sch Informat Technol Normal IL 61761 USA

ISBN: (纸本)9781728161754

Online social networks have been increasingly growing over the past few years. One of the critical factors that drive these social networks' success and growth is the friendship recommender algorithms, which are used to suggest relationships between users. Current friending algorithms are designed to recommend friendship connections that are easily accepted. Yet, most of these accepted relationships do not lead to any interactions. We refer to these relationships as weak connections. Facebook's Friends-of-Friends (FoF) algorithm is an example of a friending algorithm that generates friendship recommendations with a high acceptance rate. However, a considerably high percentage of Facebook algorithm's recommendations are of weak connections. The metric of measuring the accuracy of friendship recommender algorithms by acceptance rate does not correlate with the level of interactions, i.e., how much connected friends interact with one another. Consequently, new metrics and friendship recommenders are needed to form the next generation of social networks by generating better edges instead of merely growing the social graph with weak edges. This paper is a step towards this vision. We first introduce a new metric to measure the accuracy of friending recommendations by the probability that they lead to interactions. We then briefly investigate existing recommender systems and their limitations. We also highlight the consequences of recommending weak relationships within online social networks. To overcome the limitations of current friending algorithms, we present and evaluate a novel approach that generates friendship recommendations that have a higher probability of leading to interactions between users than existing friending algorithms.

关键词： recommender algorithms Social Networks Friending Friendship Recommendation

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A Trust-aware recommender Algorithm based on Users Overlapping Community Structure 16

A Trust-aware Recommender Algorithm based on Users Overlappi...

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16th International Conference on Advances in ICT for Emerging Regions (ICTer)

作者： Moradi, Parham Rezaimehr, Fatemeh Ahmadian, Sajad Jalili, Mahdi Univ Kurdistan Dept Comp Engn Sanandaj Iran RMIT Univ Sch Engn Melbourne Vic Australia

ISBN: (纸本)9781509060788

Retrieving items in online e-commerce systems with abundance of products is time consuming for users. To deal with this issue, recommender systems (RS) aims to help users by suggesting their interested items in the presence of thousands of products. Generally, RS algorithms are constructed based on similarity between users and/or items (e.g., a user is likely to purchase the same items as his/her most similar users). In this paper, we introduce a novel time-aware recommendation algorithm that is based on overlapping community structure between users. Users' interests might change over time, and thus accurate modelling of dynamic users' tastes is a challenging issue in designing efficient recommendation systems. The users-items interaction network is often highly sparse in real systems, for which many recommenders fail to provide accurate predictions. We apply the proposed algorithm on a benchmark dataset. Our proposed recommendation algorithm overcomes these challenges and show better precision as compared to the state-of- the-art recommenders.

关键词： recommender algorithms social networks network science trust overlapping community structure reliability

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OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2016年第2期23卷 339-348页

作者： Chen, Jonathan H. Podchiyska, Tanya Altman, Russ B. Vet Affairs Palo Alto Hlth Care Syst Ctr Innovat Implementat Ci2i Palo Alto CA USA Stanford Univ Ctr Primary Care & Outcomes Res PCOR Stanford CA 94305 USA Stanford Univ Biomed Informat Training Program Stanford CA 94305 USA Stanford Univ Dept Bioengn Stanford CA 94305 USA Stanford Univ Dept Genet Stanford CA 94305 USA Stanford Univ Dept Med Stanford CA 94305 USA

Objective: To answer a "grand challenge" in clinical decision support, the authors produced a recommender system that automatically data-mines inpatient decision support from electronic medical records (EMR), analogous to Netflix or ***'s product recommender. Materials and Methods: EMR data were extracted from 1 year of hospitalizations (> 18K patients with > 5.4M structured items including clinical orders, lab results, and diagnosis codes). Association statistics were counted for the similar to 1.5K most common items to drive an order recommender. The authors assessed the recommender's ability to predict hospital admission orders and outcomes based on initial encounter data from separate validation patients. Results: Compared to a reference benchmark of using the overall most common orders, the recommender using temporal relationships improves precision at 10 recommendations from 33% to 38% (P< 10(-10)) for hospital admission orders. Relative risk-based association methods improve inverse frequency weighted recall from 4% to 16% (P< 10(-16)). The framework yields a prediction receiver operating characteristic area under curve (c-statistic) of 0.84 for 30 day mortality, 0.84 for 1 week need for ICU life support, 0.80 for 1 week hospital discharge, and 0.68 for 30-day readmission. Discussion: recommender results quantitatively improve on reference benchmarks and qualitatively appear clinically reasonable. The method assumes that aggregate decision making converges appropriately, but ongoing evaluation is necessary to discern common behaviors from "correct" ones. Conclusions: Collaborative filtering recommender algorithms generate clinical decision support that is predictive of real practice patterns and clinical outcomes. Incorporating temporal relationships improves accuracy. Different evaluation metrics satisfy different goals (predicting likely events vs. "interesting" suggestions).

关键词： clinical decision support systems electronic health records data-mining recommender algorithms collaborative filtering practice guidelines practice variability order sets

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Joint Workshop on Interfaces and Human Decision Making for recommender Systems (#IntRS) 15

Joint Workshop on Interfaces and Human Decision Making for R...

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Proceedings of the 9th ACM Conference on recommender Systems

作者： John O'Donovan Nava Tintarev Alexander Felfernig Peter Brusilovsky Giovanni Semeraro Pasquale Lops University of California Santa Barbara Santa Barbara CA USA University of Aberdeen Aberdeen Scotland Uk Technische Universität Graz Graz Austria University of Pittsburgh Pittsburgh USA University of Bari Italy Bari Italy

ISBN: (纸本)9781450336925

As an interactive intelligent system, recommender systems are developed to give recommendations that match users' preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from users' perspectives. The field has reached a point where it is ready to look beyond algorithms, into users' interactions, decision making processes, and overall experience. Following from the success of the joint IntRS 2014 workshop and previous workshops on Interfaces and Decisions in recommender Systems, this workshop will focus on the aspect of integrating different theories of human decision making into the construction of recommender systems. It will focus particularly on the impact of interfaces on decision support and overall satisfaction, and on ways to compare and evaluate novel techniques and applications in this area.

关键词： decision making recommender algorithms interaction decision biases user interfaces recommender systems decision psychology

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RecSys'14 Joint Workshop on Interfaces and Human Decision Making for recommender Systems 14

RecSys'14 Joint Workshop on Interfaces and Human Decision Ma...

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8th ACM Conference on recommender Systems (RecSys)

作者： Tintarev, Nava O'Donovan, John Brusilovsky, Peter Felfernig, Alexander Semeraro, Giovanni Lops, Pasquale Univ Aberdeen Dept Comp Sci Aberdeen Scotland Univ Calif Santa Barbara Dept Comp Sci Santa Barbara CA 93106 USA Univ Pittsburgh Sch Informat Sci Pittsburgh PA 15260 USA Graz Univ Technol Inst Software Technol Graz Austria Univ Bari Aldo Moro Dept Comp Sci Bari Italy

ISBN: (纸本)9781450326681

As an interactive intelligent system, recommender systems are developed to give predictions that match users preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from the end-user perspective. The field has reached a point where it is ready to look beyond algorithms, into users interactions, decision-making processes and overall experience. Accordingly, the goals of this workshop (IntRS@RecSys) are to explore the human aspects of recommender systems, with a particular focus on the impact of interfaces and interaction design on decision-making and user experiences with recommender systems, and to explore methodologies to evaluate these human aspects of the recommendation process that go beyond traditional automated approaches.

关键词： recommender Systems Decision Making User Interfaces Interaction Decision Psychology recommender algorithms Decision Biases

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Cross-domain Scientific Collaborations Prediction with Citation Information 38

Cross-domain Scientific Collaborations Prediction with Citat...

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38th Annual IEEE International Computer Software and Applications Conference (COMPSAC)

作者： Guo, Ying Chen, Xi Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China

ISBN: (纸本)9781479935789

Cross-domain Scientific Collaborations have promoted rapid development of science and generated many innovative breakthroughs. However, predicting cross-domain scientific collaboration problem is rarely studied and collaboration recommendation methods within single domain cannot be directly utilized for solving cross-domain problems. In this paper, we propose a Hybrid Graph Model, which combines both explicit co-author relationships and implicit co-citation relationships together to construct a hybrid graph and then Random Walks with Restarts concept is used to measure and rank relatedness. The experiments with large publication data set show that Hybrid Graph Model outperforms some baseline approaches on several recommendation metrics. Citation information has been demonstrated to be very helpful for scientific collaboration recommendations as well.

关键词： link prediction social network recommender algorithms data mining

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Boolean Matrix Factorisation for Collaborative Filtering: An FCA-Based Approach

Boolean Matrix Factorisation for Collaborative Filtering: An...

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16th International Conference on Artificial Intelligence - Methodology, Systems, and Applications

作者： Ignatov, Dmitry I. Nenova, Elena Konstantinova, Natalia Konstantinov, Andrey V. Natl Res Univ Higher Sch Econ Moscow Russia Imhonet Moscow Russia Wolverhampton Univ Wolverhampton W Midlands England

ISBN: (纸本)9783319105543;9783319105536

We propose a new approach for Collaborative filtering which is based on Boolean Matrix Factorisation (BMF) and Formal Concept Analysis. In a series of experiments on real data (MovieLens dataset) we compare the approach with an SVD-based one in terms of Mean Average Error (MAE). One of the experimental consequences is that it is enough to have a binary-scaled rating data to obtain almost the same quality in terms of MAE by BMF as for the SVD-based algorithm in case of non-scaled data.

关键词： Boolean Matrix Factorisation Formal Concept Analysis Singular Value Decomposition recommender algorithms

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