Targeting Fairness in AdTech

Explore the vital role of fairness in responsible AI. In this session, we'll dive into what fairness means in AI and the technical challenges it presents. Using real-world examples from the AdTech industry, we'll discuss how a multi-disciplinary approach can help overcome these hurdles. Discover how collaboration across fields can foster more equitable AI systems and gain practical insights into integrating fairness into your AI practices. Join us for an insightful journey into the essence of responsible AI.

The Why:

• Ethical Responsibility: As AI becomes more integrated into our daily lives, it is crucial to ensure that these technologies are developed and deployed ethically. Understanding and addressing biases in AI is a fundamental part of this ethical responsibility, as it helps prevent the perpetuation of existing inequalities. By promoting fairness in AI, we can work towards more inclusive and equitable technologies that benefit everyone.
• Business Success: Fair AI models can enhance user trust and satisfaction, leading to bePer business outcomes. Companies that prioritize fairness in their AI development are more likely to build strong, lasting relationships with their customers. Biased AI systems can lead to reputational damage, financial losses, and other risks. By proactively addressing fairness, organizations can mi:gate these risks and build more resilient AI systems. Additionally, demonstrating a commitment to fairness in AI can help attract and retain top talent, particularly in the tech industry.

Key Takeaways:

• Bias in AI: Historical biases in data collection lead to unfair outcomes in AI, emphasizing the need for inclusivity and equity in technology.
• Challenges in Mitigation: Complex data environments and restricted access to sensitive information hinder traditional bias mitigation methods, posing unique challenges for industries like Criteo.
• Innovative Solutions: Despite limitations, tailored bias mitigation techniques offer a path to inclusivity and ethical integrity in AI, benefiting users and companies alike.

This session is for...

• Tech Industry Professionals: Those working in tech, especially in AI and data science roles, who are keen on understanding and addressing biases in AI models.
• Ethics and Compliance Specialists: Professionals concerned with the ethical implications of AI technologies and compliance with regulations such as the GDPR and the AI Act.
• Diversity and Inclusion Advocates: Individuals passionate about promoting diversity and inclusion in technology and interested in learning how bias mitigation in AI can contribute to these efforts.
• Business Leaders: Executives and decision-makers in companies utilizing AI technologies, seeking insights into mitigating bias while maintaining utility in their data-driven strategies.