Crowdsourcing, Human-AI Interaction, and the Future of Digital Platforms

Dear Colleagues,

The continuing development of artificial intelligence (AI) has profoundly reshaped the landscape of digital work, social communication, and human-computer interaction, and, as AI systems move beyond laboratories into daily life, particularly across large-scale platforms for data creation (crowdsourcing) and information consumption (digital media), a critical need to understand the complex socio-technical dynamics, ethical implications, and practical design principles that govern these interactions has become apparent. The potential for the misalignment of AI systems and the emergence of destructive algorithmic behaviors, such as algorithmic sociopathy, further underscore the urgency of this interdisciplinary inquiry. In response to this need, this Special Issue aims to serve as a vital forum for cutting-edge interdisciplinary research. In alignment with global principles of equity and inclusive design, this Special Issue also invites contributions that critically examine AI approaches that are accessibility- and disability-inclusive. As AI systems increasingly mediate communication, work, and social participation, it is essential to understand how these technologies either support or hinder access for people with disabilities, including sensory, cognitive, physical, and neurodiverse communities. We encourage research that investigates barriers, co-design practices, adaptive interfaces, multimodal access, and the broader socio-technical implications of deploying AI in contexts where accessibility, safety, and autonomy are paramount. We especially welcome interdisciplinary work that integrates insights from disability studies, accessible computing, universal design, and inclusive HCI, ensuring that the next generation of AI-driven digital platforms fosters equitable participation for all.

This Special Issue will focus on the convergence and mutual influence of AI, crowdsourcing, human–AI interaction (H-AI), digital media, and accessible solutions to the aforementioned emerging challenges. We aim to investigate how AI-driven technologies are shaped by human collaboration and digital environments, examining key issues such as alignment, fairness, transparency, governance, accessibility, and user experience at scale.

We invite high-quality submissions that address the theoretical foundations, empirical studies, and innovative systems spanning these interconnected domains. We particularly welcome interdisciplinary research that bridges technical development with societal, legal, or ethical considerations, offering novel insights into the future of human-AI collaboration in the digital age.

Areas of interest include, but are not limited to, the following topcis:

• New models and architectures of AI-augmented crowdsourcing (human-in-the-loop, AI-in-the-loop, and AI-sourcing);
• Ethical and labor implications of using AI in crowdwork platforms (eg, the "gig economy");
• The role of crowdsourcing in the evaluation and validation of AI models (LLMs and generative AI);
• Trust, transparency, and explainability (XAI) in AI systems interacting with humans;
• Design of interfaces and interaction models for conversational systems and generative AI;
• Studies on the cognitive, emotional, and psychological impacts of human–AI collaboration;
• Design and evaluation of ethical AI nudges to promote positive behaviors and mitigate biases in digital platforms;
• Detection, mitigation, and dissemination of misinformation, misaligned AI, and malicious algorithmic behaviors (for example, algorithmic sociopathy) in digital platforms;
• The use of AI for sentiment analysis and social behavior analysis in large-scale social networks;
• AI in content creation and curation (algorithmic journalism, generative art, and media personalization);
• Implications of privacy, surveillance, and algorithmic bias in AI-controlled digital media platforms;
• Frameworks of AI governance and regulatory structures addressing the distributed nature of crowdsourcing and the rapid evolution of digital media;
• Development of metrics and methodologies for assessing the effectiveness and fairness of AI systems in complex socio-technical contexts;
• Study and prevention of misaligned AI (AI alignment) and safety failures in crowdsourcing and digital media systems that may lead to sociopathic or psychopathic misaligned outcomes;
• Accessibility and inclusive design principles in AI-enabled systems and digital media platforms;
• AI-driven adaptive interfaces supporting diverse sensory, cognitive, and physical access needs;
• Co-design and participatory methods with disabled users for developing human–AI interaction models;
• Algorithmic accessibility—assessing barriers, affordances, and inequities created by AI systems;
• Risks of algorithmic exclusion and amplification of ableist bias in crowdsourcing, content moderation, and media personalization;
• AI for assistive technologies and augmented communication in digital and crowdwork environments;
• Safety, privacy, and autonomy considerations for disabled individuals interacting with AI systems;
• Accessibility metrics and evaluation methodologies for AI-driven platforms, tools, and digital ecosystems.

We welcome original research articles, surveys, case studies, and critical reviews that contribute to advancing the field of interdisciplinary AI systems, particularly at the intersection of human collaboration, digital platforms, and algorithmic governance.

We look forward to receiving your contributions.

Prof. Dr. Daniel Schneider
Dr. Glaucia Melo
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• artificial intelligence (AI)
• crowdsourcing
• human–AI interaction (H-AI)
• digital media
• algorithmic governance
• AI alignment (misaligned AI)
• algorithmic bias
• explainable AI (XAI)
• AI nudges
• gig economy
• misinformation/disinformation
• generative AI (GenAI)
• human-in-the-loop (HITL)
• accessibility
• inclusive design/universal design
• disability studies
• assistive technologies
• algorithmic ableism
• accessible HCI