The Challenge
As generative AI technologies—such as large language models and synthetic media generators—are rapidly deployed across critical sectors like healthcare, governance, journalism, and cybersecurity, they introduce systemic risks across the big data lifecycle. Organizations face critical challenges concerning algorithmic bias, data provenance loss, hallucinated outputs, deepfakes, and regulatory non-compliance. The underlying theoretical and empirical problem centers on how data-intensive ecosystems can effectively identify, measure, and mitigate these unintended harms without impeding technological innovation. Establishing robust socio-technical governance, transparency frameworks, and auditability mechanisms remains an urgent priority for maintaining institutional trust and operational security in AI-driven environments.
Core Findings
As a Call for Papers for the 2nd Workshop on Risks and Unintended Harms of Generative AI Systems (RisksGenAI 2026), this initiative solicits interdisciplinary research addressing GenAI vulnerabilities within big data ecosystems. Co-located with the 5th Italian Conference on Big Data and Data Science (ITADATA 2026) in Bari, Italy, the workshop invites submissions across three formats: Regular Papers (5–12 pages) for mature empirical research, Short Papers (5–8 pages) for preliminary work, and Non-Archival Papers for position pieces or discussion. Accepted archival contributions undergo peer review by at least two independent experts and will be published in open-access CEUR Workshop Proceedings. Key thematic tracks encompass transparency, red-teaming, malicious synthetic media detection, data assurance, regulatory policy responses, and ethical deployment frameworks. By bridging data science, machine learning, and technology policy, the workshop aims to construct a forward-looking research agenda for safe, interpretable, and accountable generative AI integration.
Strategic Takeaway
For digital leaders and organizational strategists, the RisksGenAI 2026 call highlights the necessity of treating AI risk management as a core strategic capability rather than a reactive IT control. As synthetic media and automated decision-making blur enterprise data boundaries, executives must implement end-to-end socio-technical governance frameworks. This includes establishing cross-functional red-teaming protocols, embedding automated data provenance auditing, and aligning internal AI practices with evolving regulatory benchmarks. Engaging with academic developments at this intersection equips leaders to foster organizational resilience, protect enterprise reputation, and responsibly scale generative AI implementations within complex, data-driven operational environments.