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Steven C. Carlson is an intellectual property litigator with Robins Kaplan, who draws upon national and international experience to assist clients with a wide range of legal disputes, including patent litigation, trade secret matters, and copyright disputes. Armed with a keen understanding of the technology that powers his clients’ businesses, Mr. Carlson works closely with technology companies to protect and promote their unique interests.
Mr. Carlson is a first-chair courtroom litigator whose professional experience covers a wide range of technologies, including medical devices, semiconductors, chemistry, machine learning, genomics, telecommunications, and robotics. He practices in state and federal courts around the country and has secured a trial verdict before the Patent Trial and Appeal Board (PTAB) that invalidated multiple database technology patents used in the travel industry. In addition to his intellectual property litigation work, Mr. Carlson also practices in the areas of product liability, privacy, and accessibility of disabled persons in digital environments.
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.