Dr. Michael Meehan

In Part I of this series, we discussed the Federal Trade Commission’s (FTC’s) case against Everalbum as just one example where companies may be required to remove data from their machine learning models (or shut down if unable to do so). Following are some additional pitfalls to note. A. Evolving privacy and data usage restrictions Legislators at the international, federal,…

A widespread concern with many machine learning models is the inability to remove the traces of training data that are legally tainted. That is, after training a machine learning model, it may be determined that some of the underlying data that was used to develop the model may have been wrongfully obtained or processed. The ingested data may include files that an employee took from a former company, thus tainted with misappropriated trade secrets. Or the data may have been lawfully obtained, but without the adequate permissions to process the data. With the constantly and rapidly evolving landscape of data usage restrictions at the international, federal, state, and even municipal levels, companies having troves of lawfully-obtained data may find that the usage of that data in their machine learning models becomes illegal.