What are you optimizing for? Aligning Recommender Systems with Human Values
We describe cases where real recommender systems were modified in the service of various human values such as diversity, fairness, well-being, time well spent, and factual accuracy. From this we identify the current practice of values engineering: the creation of classifiers from human-created data with value-based labels. This has worked in practice for a variety of issues, but problems are addressed one at a time, and users and other stakeholders have seldom been involved. Instead, we look to AI alignment work for approaches that could learn complex values directly from stakeholders, and identify four major directions: useful measures of alignment, participatory design and operation, interactive value learning, and informed deliberative judgments.
Code (0)
등록된 구현이 없습니다.
Tasks
DiversityFairnessRecommendation SystemsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Exploring User Opinions of Fairness in Recommender Systems
Algorithmic fairness for artificial intelligence has become increasingly relevant as these systems become more pervasive in society. One realm of AI, recommender systems, presents unique challenges for fairness due to tr…
FairnessRecommendation SystemsPrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagement
Current advances in recommender systems have been remarkably successful in optimizing immediate engagement. However, long-term user engagement, a more desirable performance metric, remains difficult to improve. Meanwhile…
Recommendation SystemsReinforcement Learning (RL)Multi-Gradient Descent for Multi-Objective Recommender Systems
Recommender systems need to mirror the complexity of the environment they are applied in. The more we know about what might benefit the user, the more objectives the recommender system has. In addition there may be multi…
Recommendation SystemsRecToM: A Benchmark for Evaluating Machine Theory of Mind in LLM-based Conversational Recommender Systems
Large Language models are revolutionizing the conversational recommender systems through their impressive capabilities in instruction comprehension, reasoning, and human interaction. A core factor underlying effective re…
Conversational Recommendation: A Grand AI Challenge
Animated avatars, which look and talk like humans, are iconic visions of the future of AI-powered systems. Through many sci-fi movies we are acquainted with the idea of speaking to such virtual personalities as if they w…
Conversational RecommendationRecommendation Systems