Switch to: References

Add citations

You must login to add citations.
  1. The Right to be an Exception to Predictions: a Moral Defense of Diversity in Recommendation Systems.Eleonora Viganò - 2023 - Philosophy and Technology 36 (3):1-25.
    Recommendation systems (RSs) predict what the user likes and recommend it to them. While at the onset of RSs, the latter was designed to maximize the recommendation accuracy (i.e., accuracy was their only goal), nowadays many RSs models include diversity in recommendations (which thus is a further goal of RSs). In the computer science community, the introduction of diversity in RSs is justified mainly through economic reasons: diversity increases user satisfaction and, in niche markets, profits.I contend that, first, the economic (...)
    Download  
     
    Export citation  
     
    Bookmark  
  • Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective.Erik Hermann - 2022 - Journal of Business Ethics 179 (1):43-61.
    Artificial intelligence is shaping strategy, activities, interactions, and relationships in business and specifically in marketing. The drawback of the substantial opportunities AI systems and applications provide in marketing are ethical controversies. Building on the literature on AI ethics, the authors systematically scrutinize the ethical challenges of deploying AI in marketing from a multi-stakeholder perspective. By revealing interdependencies and tensions between ethical principles, the authors shed light on the applicability of a purely principled, deontological approach to AI ethics in marketing. To (...)
    Download  
     
    Export citation  
     
    Bookmark   3 citations  
  • Social influence for societal interest: a pro-ethical framework for improving human decision making through multi-stakeholder recommender systems.Matteo Fabbri - 2023 - AI and Society 38 (2):995-1002.
    In the contemporary digital age, recommender systems (RSs) play a fundamental role in managing information on online platforms: from social media to e-commerce, from travels to cultural consumptions, automated recommendations influence the everyday choices of users at an unprecedented scale. RSs are trained on users’ data to make targeted suggestions to individuals according to their expected preference, but their ultimate impact concerns all the multiple stakeholders involved in the recommendation process. Therefore, whilst RSs are useful to reduce information overload, their (...)
    Download  
     
    Export citation  
     
    Bookmark