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3. Misinformation2 - Post-deployment

Radicalisation

Radicalisation - Adoption of extreme political, social, or religious ideals and aspirations due to the nature or misuse of an algorithmic system, potentially resulting in abuse, violence, or terrorism.

Source: MIT AI Risk Repositorymit951

ENTITY

3 - Other

INTENT

3 - Other

TIMING

2 - Post-deployment

Risk ID

mit951

Domain lineage

3. Misinformation

74 mapped risks

3.2 > Pollution of information ecosystem and loss of consensus reality

Mitigation strategy

1. Implement and Enforce Algorithmic Constraints for Diversity Mandate the design and deployment of **fairness-aware algorithms** for all content recommendation and curation systems. This includes applying constraints such as **reranking techniques** and **exploration strategies** to actively diversify the content presented to users, mitigating the reinforcement learning loops that create filter bubbles and accelerate radicalization and polarization. 2. Establish Continuous Auditing and Systemic Risk Assessment Require independent, **regular audits** of high-risk algorithmic systems to proactively detect and prevent the amplification or unintended exposure of extremist content, as stipulated in regulatory frameworks. This necessitates establishing clear metrics for measuring **algorithmic bias drift** and defining explicit **feedback loops** and governance mechanisms for human intervention when systemic risks related to information pollution are identified. 3. Deploy Comprehensive Counter-Narrative and Digital Literacy Interventions Invest in and widely disseminate **evidence-based counter-narrative strategies** to address and delegitimize extremist ideologies within the digital domain. This should be complemented by large-scale **digital and media literacy programs** to equip users with the critical evaluation skills necessary to recognize and resist persuasive, radicalizing content and understand the behavioral impact of algorithmic curation.