Prediction Accuracy and Autonomy
The tech industry has been criticised for designing applications that undermine individuals' autonomy. Recommender systems, in particular, have been identified as a suspected culprit that might exercise unwanted control over peoples' lives. In this article we try to assess the objectives of recommender system research and offer a nuanced discussion of how these objectives can align with users' goals. This discussion employs a qualitative literature survey connecting the dots between relevant research within the fields of psychology, design ethics, interaction design and recommender systems. Finally, we focus on the specific use-case of YouTube's recommender system and propose design changes that will better align with individuals' autonomy. Based on our analysis we offer directions for future research that will help secure rights to digital autonomy in the attention economy.
Code (0)
등록된 구현이 없습니다.
Tasks
EthicsPredictionRecommendation SystemsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Deep Multi-Task Learning for Joint Localization, Perception, and Prediction
Over the last few years, we have witnessed tremendous progress on many subtasks of autonomous driving, including perception, motion forecasting, and motion planning. However, these systems often assume that the car is ac…
Autonomous DrivingMotion ForecastingMotion PlanningMulti-Task LearningPredicting Preschoolers' Externalizing Problems with Mother-Child Interaction Dynamics and Deep Learning
Objective: Predicting children's future levels of externalizing problems helps to identify children at risk and guide targeted prevention. Existing studies have shown that mothers providing support in response to childre…
CogDrive: Cognition-Driven Multimodal Prediction-Planning Fusion for Safe Autonomy
Safe autonomous driving in mixed traffic requires a unified understanding of multimodal interactions and dynamic planning under uncertainty. Existing learning based approaches struggle to capture rare but safety critical…
Trajectory PredictionAutonomous DrivingTowards Zero Domain Gap: A Comprehensive Study of Realistic LiDAR Simulation for Autonomy Testing
Testing the full autonomy system in simulation is the safest and most scalable way to evaluate autonomous vehicle performance before deployment. This requires simulating sensor inputs such as LiDAR. To be effective, …
Motion PlanningAdv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulation
Self-driving vehicles (SDVs) must be rigorously tested on a wide range of scenarios to ensure safe deployment. The industry typically relies on closed-loop simulation to evaluate how the SDV interacts on a corpus of synt…
Motion Planning