Informational and Communicational AI Risks
Informational and communicational AI risks refer particularly to informational manipulation through AI systems that influence the provision of information (Rahwan, 2018; Wirtz & Müller, 2019), AIbased disinformation and computational propaganda, as well as targeted censorship through AI systems that use respectively modified algorithms, and thus restrict freedom of speech.
ENTITY
3 - Other
INTENT
1 - Intentional
TIMING
2 - Post-deployment
Risk ID
mit292
Domain lineage
4. Malicious Actors & Misuse
4.1 > Disinformation, surveillance, and influence at scale
Mitigation strategy
1. Establish Comprehensive AI Governance and Accountability Frameworks: Mandate the adoption of established AI risk management frameworks (e.g., NIST AI RMF, ISO/IEC 42001) to embed principles of fairness, transparency, and accountability into the design and deployment of informational AI systems, ensuring clear oversight and defined responsibility for system outcomes. 2. Ensure Algorithmic Integrity and Data Provenance: Employ rigorous data processing techniques, including bias audits and the use of high-quality, verified training datasets, alongside adversarial testing and continuous security monitoring to maintain model robustness against tampering and to actively detect AI-generated disinformation (e.g., deepfakes, hallucinations). 3. Implement Explainable AI (XAI) and Human-in-the-Loop Oversight: Integrate XAI techniques to provide technical and stakeholder clarity on algorithmic decision-making, particularly in content moderation. This must be complemented by robust human oversight mechanisms and accessible appeal processes to safeguard against targeted censorship and to validate the accuracy of critical AI outputs.