Ethical aspects of multi-stakeholder recommendation systems

被引:15
|
作者
Milano, Silvia [1 ]
Taddeo, Mariarosaria [1 ,2 ]
Floridi, Luciano [1 ,2 ]
机构
[1] Univ Oxford, Oxford Internet Inst, 1 St Giles, Oxford OX1 2JD, England
[2] Alan Turing Inst, London, England
来源
INFORMATION SOCIETY | 2021年 / 37卷 / 01期
关键词
Artificial intelligence; digital ethics; multistakeholder recommendation; recommender systems; recommender systems ontology; recommender systems and welfare; social aspects of recommendation;
D O I
10.1080/01972243.2020.1832636
中图分类号
G2 [信息与知识传播];
学科分类号
05 ; 0503 ;
摘要
In this article we analyze the ethical aspects of multistakeholder recommendation systems (RSs). Following the most common approach in the literature, we assume a consequentialist framework to introduce the main concepts of multistakeholder recommendation. We then consider three research questions: Who are the stakeholders in a RS? How are their interests taken into account when formulating a recommendation? And, what is the scientific paradigm underlying RSs? Our main finding is that multistakeholder RSs (MRSs) are designed and theorized, methodologically, according to neoclassical welfare economics. We consider and reply to some methodological objections to MRSs on this basis, concluding that the multistakeholder approach offers the resources to understand the normative social dimension of RSs.
引用
收藏
页码:35 / 45
页数:11
相关论文
共 50 条
  • [1] Explanation in Multi-Stakeholder Recommendation for Enterprise Decision Support Systems
    Cornacchia, Giandomenico
    Donini, Francesco M.
    Narducci, Fedelucio
    Pomo, Claudio
    Ragone, Azzurra
    [J]. ADVANCED INFORMATION SYSTEMS ENGINEERING WORKSHOPS, 2021, 423 : 39 - 47
  • [2] Multi-stakeholder News Recommendation Using Hypergraph Learning
    Gharahighehi, Alireza
    Vens, Celine
    Pliakos, Konstantinos
    [J]. ECML PKDD 2020 WORKSHOPS, 2020, 1323 : 531 - 535
  • [3] A multi-stakeholder ethical framework for AI-augmented HRM
    Prikshat, Verma
    Patel, Parth
    Varma, Arup
    Ishizaka, Alessio
    [J]. INTERNATIONAL JOURNAL OF MANPOWER, 2022, 43 (01) : 226 - 250
  • [4] Modelling Multi-Stakeholder Systems: A Case Study
    Oey, Michel
    Genc, Zulkuf
    Ghorbani, Amineh
    Aldewereld, Huib
    Brazier, Frances
    Aydogan, Reyhan
    Jonker, Catholijn M.
    Timmer, Reinier
    Wijngaards, Niek
    [J]. 2014 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 3, 2014, : 404 - 411
  • [5] Utility optimization-based multi-stakeholder personalized recommendation system
    Shrivastava, Rahul
    Sisodia, Dilip Singh
    Nagwani, Naresh Kumar
    [J]. DATA TECHNOLOGIES AND APPLICATIONS, 2022, 56 (05) : 782 - 805
  • [6] A Multi-Objective Optimization Framework for Multi-Stakeholder Fairness-Aware Recommendation
    Wu, Haolun
    Ma, Chen
    Mitra, Bhaskar
    Diaz, Fernando
    Liu, Xue
    [J]. ACM TRANSACTIONS ON INFORMATION SYSTEMS, 2023, 41 (02)
  • [7] Multi-stakeholder engagement
    Blenkus, M. Gabrijelcic
    Kronegger, L.
    Pushkarev, N.
    Robnik, M.
    Sotlar, I.
    [J]. EUROPEAN JOURNAL OF PUBLIC HEALTH, 2020, 30
  • [8] An Ethical Multi-Stakeholder Recommender System Based on Evolutionary Multi-Objective Optimization
    Kermany, Naime Ranjbar
    Zhao, Weiliang
    Yang, Jian
    Wu, Jia
    Pizzato, Luiz
    [J]. 2020 IEEE 13TH INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC 2020), 2020, : 478 - 480
  • [9] Multi-stakeholder Interactive Simulation for Federated Satellite Systems
    Grogan, Paul T.
    Golkar, Alessandro
    Shirasaka, Seiko
    de Weck, Olivier L.
    [J]. 2014 IEEE AEROSPACE CONFERENCE, 2014,
  • [10] The Role of Blockchains in Multi-Stakeholder Transactive Energy Systems
    Eisele, Scott
    Laszka, Aron
    Schmidt, Douglas C.
    Dubey, Abhishek
    [J]. FRONTIERS IN BLOCKCHAIN, 2020, 3