Three resources to help you understand today’s data and AI regulatory landscape
IBM leads in mature data privacy solutions to get you ready for emerging regulations
The data privacy and AI protection regulatory landscape seems to evolve on a constant basis. Almost three years ago, organizations were developing roadmaps for strong information and data governance programs to comply with the EU General Data Protection Regulation (GDPR). Shortly prior to this, businesses were facing the U.S. California Consumer Privacy Act (CCPA) and other Momentum Data Protection Acts.
Today the conversation has shifted to AI. As use cases grow, so do concerns about protecting sensitive data and supporting responsible AI. Regulations are taking shape to ensure organizations strike the right balance between innovation and respecting consumer privacy.
This past month we saw strong stances and proposals for enacting regulations on AI from the European Union and the Federal Trade Commission. Across the board, there have been proposals to establish a set of best practices for AI uses that might be perpetuating unfair practices. Areas more likely to see regulatory intervention include facial recognition, social scoring, and algorithm practices that do more harm than good. While self-regulation is encouraged, some of the proposals today include potential fines of up to 4% annual revenue for noncompliance.
These proposed regulations will be part of a narrative that continues for the foreseeable future, and organizations that begin taking actions today will be the ones who are able to adapt quickly once proposals develop into real regulations.
Below are some resources to help kick-start AI governance and protection mapping within your organization, leveraging the foundation you may have already built for data privacy in the process.
1. Research report: Build your trust advantage: Leadership in the era of data and AI everywhere
This global C-Suite study digs deep into leaders that stand out in their ability to unite trust and data to surge ahead in their digital transformation. In each chapter, there are recommendations, based on analyses of comparative data and in-depth interviews, for how others can advance their own journeys.
Providing an introduction to the concept of AI governance and the tooling needed to support it, this Smartpaper provides a concise overview of what organizations need to understand as they craft their enterprise AI strategies. Helpful webinars are also included to expand your learning.
After IBM announced the availability of AI FactSheets earlier this year, analysts said that this methodology should become the industry standard. A FactSheet is a collection of relevant information (facts) about the creation and deployment of an AI model or service, optimized to be shared via a data catalog. Increased transparency provides information for AI consumers to better understand how an AI model or service was built and to determine if it is appropriate for their objective.
IBM has been a leader in advancing AI-driven technologies for enterprises and has pioneered the future of machine learning systems for multiple industries. Based on decades of AI research, years of experience working with organizations of all sizes, and learnings from more than 30,000 IBM Watson engagements, IBM has developed critical technologies to support high standards of data privacy and AI protection.
Read this blog to learn about the latest IBM Watson product announcements designed to build trust in data, models and processes. Then dig a little deeper with this eBook on how a modern data catalog provides a strong foundation for your data and AI journey.
Join session 1374 – Merging trusted AI with data privacy during THINK 2021. Register for free.