Ah, that's the great puzzle: On the Quest of a Holistic Understanding of the Harms of Recommender Systems on Children
Children come across various media items online, many of which are selected by recommender systems (RS) primarily designed for adults. The specific nature of the content selected by RS to display on online platforms used by children - although not necessarily targeting them as a user base - remains largely unknown. This raises questions about whether such content is appropriate given children's vulnerable stages of development and the potential risks to their well-being. In this position paper, we reflect on the relationship between RS and children, emphasizing the possible adverse effects of the content this user group might be exposed to online. As a step towards fostering safer interactions for children in online environments, we advocate for researchers, practitioners, and policymakers to undertake a more comprehensive examination of the impact of RS on children - one focused on harms. This would result in a more holistic understanding that could inform the design and deployment of strategies that would better suit children's needs and preferences while actively mitigating the potential harm posed by RS; acknowledging that identifying and addressing these harms is complex and multifaceted.
Code (0)
등록된 구현이 없습니다.
Tasks
Recommendation SystemsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
On Measures of Biases and Harms in NLP
Recent studies show that Natural Language Processing (NLP) technologies propagate societal biases about demographic groups associated with attributes such as gender, race, and nationality. To create interventions and mit…
MoviePuzzle: Visual Narrative Reasoning through Multimodal Order Learning
We introduce MoviePuzzle, a novel challenge that targets visual narrative reasoning and holistic movie understanding. Despite the notable progress that has been witnessed in the realm of video understanding, most prior w…
BenchmarkingContrastive LearningVideo UnderstandingAmazUtah_NLP at SemEval-2024 Task 9: A MultiChoice Question Answering System for Commonsense Defying Reasoning
The SemEval 2024 BRAINTEASER task represents a pioneering venture in Natural Language Processing (NLP) by focusing on lateral thinking, a dimension of cognitive reasoning that is often overlooked in traditional linguisti…
Multiple-choiceQuestion AnsweringSentenceA Puzzle-Based Dataset for Natural Language Inference
We provide here a dataset for tasks related to natural language understanding and natural language inference. The dataset contains logical puzzles in natural language from three domains: comparing puzzles, knighs and kna…
Natural Language InferenceNatural Language UnderstandingReading ComprehensionAre Language Models Puzzle Prodigies? Algorithmic Puzzles Unveil Serious Challenges in Multimodal Reasoning
This paper introduces the novel task of multimodal puzzle solving, framed within the context of visual question-answering. We present a new dataset, AlgoPuzzleVQA designed to challenge and evaluate the capabilities of mu…
Multimodal ReasoningQuestion AnsweringVisual Question Answering