This article discusses a talk by Houda Elmimouni on designing technology with user values in mind, using privacy and robotic telepresence research as examples. The first study examines privacy practices, while the second focuses on values in robotic telepresence in classrooms. Elmimouni highlights the importance of aligning technology design with social values like privacy. Why it matters: The emphasis on user-centered design and social values provides insights applicable to AI development in the Middle East, where cultural context and ethical considerations are paramount.
A computer science vision involves computing devices becoming proactive assistants, enhancing various aspects of life through user digitization. Current devices provide coarse digital representations of users, but there's significant potential for improvement. Karan, a Ph.D. candidate at CMU, develops technologies for consumer devices to capture richer user representations without sacrificing practicality. Why it matters: Advancements in user digitization can lead to improved extended reality experiences, health tracking, and more productive work environments, enhancing the utility of consumer devices.
This article discusses conscious investing and its potential in the Middle East, particularly in light of unprecedented market conditions. It argues that investments should align with values and aim for positive global impact, moving beyond solely maximizing shareholder value. Conscious investing can be as profitable as traditional investing while addressing social and environmental challenges. Why it matters: The piece advocates for integrating ethical considerations into investment strategies within the region, which could lead to more sustainable and socially responsible economic development.
This paper presents six experiments evaluating personalization and user tracking in web search engine results. The experiments involve comparing search results based on VPN location (including UAE vs others), logged-in status, network type, search engine, browser, and trained Google accounts. The study measures total hits, first hit, and correlation between hits to identify patterns of personalization. Why it matters: The findings shed light on the extent of filter bubble effects and potential biases in search results for users in the UAE and globally.
This paper introduces a mutually-regularized dual collaborative variational auto-encoder (MD-CVAE) for recommendation systems, addressing the limitations of user-oriented auto-encoders (UAEs) in handling sparse ratings and new items. MD-CVAE integrates item content and user ratings within a variational framework, regularizing UAE weights with item content to avoid non-optimal convergence. A symmetric inference strategy eliminates the need for retraining when introducing new items, enhancing efficiency in dynamic recommendation scenarios. Why it matters: The MD-CVAE approach offers a practical solution for improving recommendation accuracy and efficiency, especially in scenarios with data sparsity and frequent item updates, relevant to e-commerce and content platforms in the Middle East.
This paper presents a reinforcement learning framework for optimizing energy pricing in peer-to-peer (P2P) energy systems. The framework aims to maximize the profit of all components in a microgrid, including consumers, prosumers, the service provider, and a community battery. Experimental results on the Pymgrid dataset demonstrate the approach's effectiveness in price optimization, considering the interests of different components and the impact of community battery capacity.