Striking the balance between personalization and user privacy

Uncover the ethical challenges in hyper-personalization, navigating data privacy concerns and algorithmic biases. Understand the techniques— differential privacy, synthetic data generation, and bias-detection algorithms—employed to enhance ad relevance responsibly

- Ashok Pandey

In the world of AI-driven advertising, personalization has evolved into hyperpersonalization. This shift, driven by rapid advancements in AI tools and technology, has raised ethical concerns about data privacy. Krishnakumar Govindarajan, Global Chief Technology Officer, MiQ highlights the emergence of laws like DPDP, GDPR, and CCPA, emphasizing the need for stringent controls and ethical considerations.

▾ Techniques and Algorithms for Enhanced Ad Relevance

Personalization is not new anymore; it’s been the norm for over a decade now. With the rapid advancements in tools, technology and process in AI and the rush to differentiate and monetize all the data that organizations have and are collecting, personalization has evolved into Hyper-personalization over time. This, for the right reasons, has led to the rise in ethical concerns regarding data privacy in AI marketing. For instance—look at the recent high-profile data breaches and other privacy scandals. Scores of laws that have come to reality—DPDP, GDPR, CCPA, etc., are helping in setting the base expectation around data privacy and opening up opportunities to set up higher levels of controls that organizations see as differentiators, eg., browser third-party cookie deprecation, Apple IDFA deprecation, edge computing, browser sandboxing, etc.,

Data privacy is no longer limited to ‘invasion of user privacy’, it now encapsulates data protection concerns as well as algorithmic bias concerns.