Artificial intelligence (AI) and privacy: 3 key security practices
If you are involved in next-generation digital product engineering, experimenting with artificial intelligence (AI) will help you imagine new business models, revenue streams, and experiences.
But you should be wary of wild headlines about cutting-edge AI breakthroughs. For every AlphaFold that solves a 50-year-old problem about protein folding, there are dozens of less glitzy but perhaps more impactful business AI advances that are helping to make it more responsible and privacy-conscious.
As algorithms imbibe increasingly huge data sets both in training and deployment, data privacy as it relates to AI/machiene learning (ML) will only grow in importance, especially with new regulations expanding upon GDPR, CCPA, HIPAA, etc. In fact, the FDA recently issued a new action plan for regulating AI in medical devices. Expanding regulatory frameworks are partially why data privacy is one of the most important issue of this decade.
As your organization plans for AI investments in the future, the following three AI techniques will ensure you stay compliant and secure well into the future.
Federated learning is an increasingly important ML training technique that solves one of ML’s biggest data privacy issues, especially in fields with sensitive user data, such as healthcare. The traditional wisdom of the last decade was to unsilo data wherever possible. However, the resulting data aggregation necessary to train and deploy ML algorithms has created serious privacy and security problems, especially when data is being shared between organizations.
Federated learning gives you the insights of aggregated data sets while keeping the data safe and secure in non-aggregated environments. The basic premise is that local ML models are trained on private data sets, and model updates flow between the data sets to be aggregated centrally. Crucially, the data never has to leave its local environment.
In this way, the data remains secure while still giving organizations the “wisdom of the crowd.” Federated learning reduces the risk of a single attack or leak compromising the privacy of all the data because instead of sitting in a single repository, the data is spread out among many.
Many AI/ML models, particularly neural networks, are black boxes. Having been trained on huge amounts of data, these models are often unaccountable due to the difficulties in determining how and why they make certain decisions. To make them more accountable and transparent, we need to make them more interpretable.
An up-and-coming area of research called explainability uses sophisticated techniques to help bring transparency to simple systems such as decision trees as well as to complex systems such as neural networks.
