Daniele Quercia’s research while affiliated with Polytechnic University of Turin and other places

... Potential agent failures significantly shape user perception, trust, and adoption [38]. Unpredictable agent behavior often elicits skepticism and deep concerns about losing control, heightened by societal anxieties about AI safety [32,52]. Consequently, users often prefer an "AI as augmentation" model: agents assisting under human oversight rather than replacing human judgment [16,76,77]. ...

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Characterizing Unintended Consequences in Human-GUI Agent Collaboration for Web Browsing

... They are then presented with a set of human-written principles that capture culture-specific harms so that they can engage in a multi-turn process of critiquing and revising originally harmful generations to harmless generations (or vice versa), to create culturespecific preference pairs for alignment training. Enabling constitutional AI for multilingual and multicultural alignment data generation requires close collaboration among linguists, cultural anthropologists and AI researchers to co-create three key components: (1) culturally-informed constitutional principles that reflect diverse value systems and ethical frameworks across different societies [Kirk et al., 2024;Pistilli et al., 2025]; (2) sufficiently capable multilingual LLMs that can both understand these principles and generate high-quality content in target languages [Qin et al., 2024;; and (3) evaluation protocols involving native speakers and cultural experts to validate both the constitutional principles and the resulting synthetic data [Kyrychenko et al., 2025]. This direction offers a pathway toward scalable, culturally grounded alignment practices that make LLM safety more inclusive and globally relevant. ...

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The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating It

... For example, an analysis over 70,000 patent citations to HCI publications revealed that only a small fraction of academic work directly influences patents [11]. Similarly, an analysis of Responsible AI research found that while approximately 18.7% of AI research papers transitioned into code repositories, only 7.2% ended up in patents [34]. These findings demonstrate that HCI research often struggles to transition from theory to practice. ...

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The Impact of AI on Jobs: HCI is Missing

... The need for reporting AI risks both at the level of models and specific uses is partly driven by standards like the NIST AI Risk Management Framework [51] and regulations like the EU AI Act [14], which mandate risk documentation based on the particular use and context [26,31]. Consequently, various AI impact assessment reports [1,6,65] and cards [25] have been proposed to help AI developers prepare the required documentation, particularly for high-risk systems. ...

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RiskRAG: A Data-Driven Solution for Improved AI Model Risk Reporting

... Credo AI [13] provides a set of Policy Packs with associated online questionnaires to identify areas of non-compliance with one or more AI regulations. Herdel et al. [21] discuss the use of large language models to assist in envisioning the uses and risks of AI. Wang et al. [58] describe and test a system that helps users identify potential AI risks during prompt-based prototyping. ...

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Developing a Risk Identification Framework for Foundation Model Uses

... A growing body of work emphasizes the importance of task-specific assessment and documentation of potential harms related to particular AI applications (and several frameworks have been developed for this purpose, e.g., RiskCards [23], ethics sheets [77], and RAI Guidelines [18]). Our findings complement this call for action, demonstrating that bias can and does manifest in unique ways in product description generation-problems that are of critical importance but would have been difficult or impossible to identify with a more general analysis. ...

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Understanding Gender Bias in AI-Generated Product Descriptions

... It has also been shown that anticipating the risks of an AI system or a model is a hard task even for practitioners and researchers with knowledge of AI [8,19]. The increasing frequency 1 https://huggingface.co/ of real-world AI incidents and harms [45,68] is likely partly due to the lack of transparency regarding risks associated with models deployed [4,13]. Risks can be reported as "model-specific", i.e., risks arising from the model's unique capabilities or limitations (e.g., perpetuating harmful biases from training data), or "use-specific", i.e., contextualized risks tied to specific applications (e.g., biases affecting attendees when transcribing virtual meeting). ...

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RiskRAG: A Data-Driven Solution for Improved AI Model Risk Reporting

... Wang et al. [69] introduced FarSight, another LLM-based tool designed specifically to support prompt developers working with LLMs. Bogucka et al. [7] compiled risks of various AI uses that have led to real-world harms, presenting them in a visualization appealing to the broader public. All of these tools leveraged LLMs to identify potential uses or risks for a given AI system. ...

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RiskRAG: A Data-Driven Solution for Improved AI Model Risk Reporting

... The thorny area of data use, privacy, and sovereignty, as discussed, is one that mobile AI providers are seeking to get ahead of. So while mobile AI brings potential risks and problems with a new, ramped-up phase of data gathering and use (Constantinides et al., 2024) that we don't yet fully understand, there is the promise of mobile AI devices to provide better options for data privacy and security with on-device housing of data at the "edge" of Internet and mobile networks, rather than relying on cloud storage and services . Another area, also evident from the case studies above, arises from the fascinating ways in which mobile AI is a focal point for the next wave of digitalization and personalization in products and services. ...

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Mobile AI: Communication and Mobility After the Smartphone

... To measure the impact of AI on occupational tasks, Septiandri et al [24] defined the AI Impact (AII) as a measure of how much AI could impact a job's tasks by looking at how closely these tasks are associated with patents. For each task, their method finds the patent most similar to it using a cosine similarity score. ...

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AI and the economic divide: How Artificial Intelligence could widen the divide in the U.S.