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Roger Bodamer


Archipelago Analytics, Inc.

Roger Bodamer is a Co-Founder, CTO, and COO of Archipelago Analytics, Inc. Roger is a serial entrepreneur focusing on artificial intelligence, machine learning, and database architectures. Currently he is using machine learning for digesting legal documentation and assessing large-scale risks. Roger got his start early on at Oracle as an engineer in core database technologies, and has been on the leadership team at companies like Apple, OuterBay (acquired by Hewlett Packard) and Efficient Frontier (acquired by Adobe) and MongoDB (which IPO’d in 2017). He is the founder of three companies, including UpThere, which was recently acquired by Western Digital. Roger boasts over 10 patents and serves as an expert witness in IP disputes. Roger holds a bachelor’s degree in computer science from Saxion University of Applied Sciences (Netherlands) and is currently Founder/CTO/COO of Archipelago Analytics.

Recent Articles by Roger Bodamer

Machine Learning Models and the Legal Need for Editability: Surveying the Pitfalls (Part II)

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,…

Machine Learning Models: The Legal Need for Editability (Part I)

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.