Tootfinder

D-RDW: Diversity-Driven Random Walks for News Recommender Systems
Runze Li, Lucien Heitz, Oana Inel, Abraham Bernstein
https://arxiv.org/abs/2508.13035 https://

D-RDW: Diversity-Driven Random Walks for News Recommender Systems
This paper introduces Diversity-Driven RandomWalks (D-RDW), a lightweight algorithm and re-ranking technique that generates diverse news recommendations. D-RDW is a societal recommender, which combines the diversification capabilities of the traditional random walk algorithms with customizable target distributions of news article properties. In doing so, our model provides a transparent approach for editors to incorporate norms and values into the recommendation process. D-RDW shows enhanced pe…

Towards Transparent Ethical AI: A Roadmap for Trustworthy Robotic Systems
Ahmad Farooq, Kamran Iqbal
https://arxiv.org/abs/2508.05846 https://arxiv.org/pdf…

Towards Transparent Ethical AI: A Roadmap for Trustworthy Robotic Systems
As artificial intelligence (AI) and robotics increasingly permeate society, ensuring the ethical behavior of these systems has become paramount. This paper contends that transparency in AI decision-making processes is fundamental to developing trustworthy and ethically aligned robotic systems. We explore how transparency facilitates accountability, enables informed consent, and supports the debugging of ethical algorithms. The paper outlines technical, ethical, and practical challenges in imple…

Interpretable reinforcement learning for heat pump control through asymmetric differentiable decision trees
Toon Van Puyvelde, Mehran Zareh, Chris Develder
https://arxiv.org/abs/2506.01641

Interpretable reinforcement learning for heat pump control through asymmetric differentiable decision trees
In recent years, deep reinforcement learning (DRL) algorithms have gained traction in home energy management systems. However, their adoption by energy management companies remains limited due to the black-box nature of DRL, which fails to provide transparent decision-making feedback. To address this, explainable reinforcement learning (XRL) techniques have emerged, aiming to make DRL decisions more transparent. Among these, soft differential decision tree (DDT) distillation provides a promisin…