What needs to be considered when balancing AI and data privacy? Join host Dan Hammaker and and Marlene Meyerson JCC Manhattan VP of Digital Products Shaifali Patel as they explore the delicate balance between innovation and privacy in today’s AI-driven landscape. In this session, you’ll gain actionable insights into delivering AI-powered tools that not only elevate the customer experience but also prioritize and protect their data. Discover best practices, real-world examples, and strategies for building trust while pushing the boundaries of technology. Don’t miss this crucial conversation for any forward-thinking product leader!
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Show Notes:
- Privacy is the right to be left alone.
- There are different categories of data with varying degrees of privacy considerations, such as usage tracking, PII, and medical records.
- Both companies and individuals must take responsibility for protecting privacy, including understanding what data is collected and how it is used.
- Modularity in data systems is important to allow users control over their data and the ability to delete it.
- Transparency and trust are critical in AI systems to ensure users feel in control of their data.
- Consider privacy from the beginning of the product design process through a privacy-by-design approach.
- The regulatory landscape is evolving, with principles like transparency, lawfulness, fairness, and accuracy becoming increasingly important.
- Understanding the source of data and ensuring proper consent is crucial, especially for AI systems.
- Accountability in AI systems is complex, with questions around the role of humans in the loop.
- Minimizing data collection and only collecting what is necessary is important for privacy.
- Maintain transparency in data usage.
- AI can potentially enhance privacy through advanced encryption and synthetic data.
- Building trust with users by being transparent about data use and protection is key.
- Partnerships with legal, compliance, and regulatory teams are important to understand and comply with evolving privacy laws.
- Understanding the specific laws and regulations that apply to different user populations is crucial.
- Product managers should shift from viewing privacy as a checkbox to prioritizing user trust and transparency.
- Collecting data progressively, asking users for only necessary information at each stage, can improve privacy.
- Understanding the context and source of data from disparate sources is important when building user profiles.
- Consider a hybrid on-premise and cloud data storage model based on data sensitivity and regulatory requirements.
- Balancing AI and data privacy important to ensure compliance with evolving privacy regulations.
About the speaker
I am passionate about leveraging technology and data to foster innovation and advance businesses. I enjoy working with teams at both strategic and tactical levels, generating bold and innovative ideas for growth. With over 16+ years of experience in product management and leadership, I have successfully built and scaled SAAS products and platforms across multiple industries, including financial services, data and analytics, healthcare, and telecommunications. I have led many successful user-centric and data-driven solutions that drive growth, efficiency, and AI/ML innovation for both B2B and B2C markets. As a connector, communicator, and catalyst, I value fairness, diversity, and inclusion, and I champion these principles in my work and leadership. I am a lifelong learner who is always curious and eager to uncover the next challenge and opportunity. I believe that clarity, integrity, and transparency are the keys to creating trust, excitement, and commitment among stakeholders, customers, and teams.
About the host
I'm a product leader focused on delivering exceptional client value by building quality software and engaged teams. I love talking to users about their problems and working with new PMs to grow their careers. I live in Brooklyn, NY with my spouse. Outside of work I love traveling, cooking, and following Philadelphia sports.