AI-driven highly personalized advertisement
Advanced GPAI systems can create advertisements tailored to individual recip- ients, exploiting the biases and irrational beliefs of each recipient. Such adver- tisements can cause consumers to make decisions they regret in retrospect, or would regret upon more reflection. Current versions of personalized video advertisements already show better re- sults compared to regular advertisements [110]. However, the widespread use of highly personalized advertisements raises concerns about undermining consumer autonomy and exacerbating social inequality.
ENTITY
2 - AI
INTENT
3 - Other
TIMING
3 - Other
Risk ID
mit1176
Domain lineage
4. Malicious Actors & Misuse
4.3 > Fraud, scams, and targeted manipulation
Mitigation strategy
1. Establish a mandatory framework for Algorithmic Transparency and Explainability to ensure consumers are fully informed. This necessitates clear, real-time disclosure of AI involvement in content creation and targeting, including the specific data categories leveraged, thereby preserving the consumer's capacity for informed choice and mitigating the risks associated with 'black box' systems. 2. Implement robust mechanisms for Consumer Agency and Preference Management, granting individuals easily accessible, fine-grained controls to manage or fully opt out of personalized targeting and data processing. This reinforces the core components of autonomy: liberty (freedom from undue influence) and agency (capacity for intentional decision-making). 3. Conduct independent, continuous Audits for Algorithmic Bias and Fairness throughout the AI lifecycle, from data acquisition to deployment. These technical evaluations must proactively detect and mitigate systemic biases within the models that could perpetuate social inequalities through unfair targeting, exclusion, or differential pricing strategies.