As a researcher at the intersection of psychology, human-computer interaction (HCI), and AI ethics, my work has long been driven by a simple yet urgent question: How do interactive and interactional systems - not just as tools, but as entities and experience creators - shape our emotions, behaviours, interactions with others and even our sense of self?

Whistleblowing channels are a well-established element of corporate governance, helping organizations detect misconduct early and limit legal and reputational harm. This function is especially important in the AI context. This article is written by Rocío Riesco. Her interests include responsible AI, whistleblower protection, and governance challenges related to emerging technologies. Her interests include responsible AI,whistleblower protection, and governance challenges rela

The paper addresses the high computational cost of active learning, where large models must be repeatedly trained to select informative data for labeling. This limits its practicality, especially for large-scale or resource-constrained settings.

Synthetic Influencers are no longer sci-fi curiosities. They are already adopted by many brands as scalable marketing tools that help sell products by mimicking human behaviour and interacting with users online. AI-generated personas like Lil Miquela, fronting campaigns for Prada and Calvin Klein, and Lu do Magalu, partnered with Samsung and Red Bull, now command millions of followers and operate at a scale no human creator can match without the support of AI tools.

What makes this research particularly compelling is the convergence of three powerful ideas that are rarely combined in a single experimental study: continuous wavelet transform (CWT) scalogram image generation, masked autoencoder (MAE) self-supervised The paper addresses the high computational cost of active learning, where large models must be repeatedly trained to select informative data for labeling. This limits its practicality, especially for large-scale or resource-c

hat problem does this paper address, and why does it matter? The paper addresses the high computational cost of active learning, where large models must be repeatedly trained to select informative data for labeling. This limits its practicality, especially for large-scale or resource-constrained settings.

The DIVERSIFAIR project concludes with the "Fair AI in Practice" conference on 19 May in Brussels. As a project partner, Women in AI (WAI) is represented by President Dr Alessandra Sala, who delivers the keynote, and Adebola Olomo and Dr Laura Caroli, who join panels on inclusive design. The event focuses on intersectional fairness and practical tools like the Fair AI Scrum Workshop. Seats are limited!

In this article, Petruta Pirvan and Sonal Makhija explore several real-world use cases and highlight the practical challenges that organizations commonly encounter.The growing reliance on legitimate interest as the primary lawful basis for AI training has been broadly accepted by EU data protection authorities. In comparison to the impracticality of obtaining consent at scale, legitimate interest offers a more pragmatic path. However, its applicability depends on the specific

As part of the community partnership between WAI Kenya and Gitex Kenya, the WAI community benefits from 50% registration until 28 August.

This article is written by Dawn YU, a patent attorney and Shareholder at Jiaquan IP Law in China, focusing on cross-border patent strategy, patent invalidation proceedings, and trade secret protection. She works with technology companies and international partners on patent disputes and innovation protection, especially in areas such as AI, medical devices, and advanced manufacturing. She also led the development of the firm’s AI-assisted tool for monitoring CNIPA post-grant

This article is written by Dina Blikshteyn, a partner and Co-Chair of AI and Deep Learning Practice Group and Haynes Boone, LLP. Dina focuses her practice on intellectual property portfolio management, post-grant proceedings, and AI Governance. Dina also regularly speaks and publishes on issues related to AI.

Wireless was born to ship bits. The next wave will ship intent. We still celebrate lower bit error rate (BER) and higher spectral efficiency, yet many modern applications don’t need every bit, they need the right information to make the next decision. If a detector’s confidence is unchanged, why protect the last kilobyte? If a policy is invariant to pixel noise, why retransmit for perfection?