AI & Marketing: New Methods and New Risks Conference
Location
Stanford Graduate School of Business
AI promises to revolutionize marketing. Product placement has already evolved into the science of recommendation. Large-scale machine learning models now rely on vast amounts of consumer data to predict preferences, personalize content, and optimize engagement in real time—profoundly transforming the way brands connect with their audiences. For example, recommender systems have become essential to platform success, particularly in e-commerce and content streaming; on Amazon, they drive over 50% of product sales, while on Netflix, they account for 80% of total watch time, highlighting their powerful influence on consumer behavior and engagement.
However, these advanced AI models are “black-box” in nature, lacking transparency regarding the underlying data-generating processes and understanding the “why” behind consumer choices. At the same time, decades of marketing research offer valuable insights into consumer motivations and behavior. This creates a compelling opportunity: by integrating these marketing insights into AI development, we can design more robust, interpretable, and human-centric solutions that optimize the long-term outcomes of these platforms.
At the same time, there are risks that come with these black-box AI models. Privacy is a primary concern—AI’s reliance on vast amounts of consumer data raises critical questions about data collection, storage, and use. The increasing complexity of data sharing among companies and platforms often leaves consumers in the dark about how their information is used, complicating efforts to comply with evolving privacy regulations like GDPR and CCPA. In addition, marketers might be concerned that algorithmic bias could lead to skewed marketing strategies or discrimination. This is of particular concern for customer segmentation, product recommendations, and targeted ads, where subtle biases can have significant impacts on both businesses and consumers.
The conference is organized by Stanford Graduate School of Business, in collaboration with the Business, Government and Society Initiative. It will span two days, bringing together experts from academia, industry, and policymaking. It focuses on the following themes:
● New Methods: New developments for more robust, generalizable, and human-centric AI solutions by moving away from black-box models;
● New Data: Harnessing today’s diverse unstructured sources (text, images, user-generated content, etc.) to fuel more insightful, actionable marketing strategies.
● New Risks: How AI can be harnessed ethically and effectively in marketing, and strategies for balancing innovation with responsibility.
Together, we will forge a path forward that leverages AI’s transformative potential while upholding our commitment to transparency, ethical innovation, and long-term optimization.
