We seem to subscribe to the popular oversimplification that machines are less biased than humans; however, if you are familiar with the ways in which machines are trained to read and focus on different aspects of data, you will know: It's just not that simple.
Machines are not free of bias if they are trained by humans.
Masterclass excerpt and links from recent webinar with CAVE Academy, via Visual Effects Society.
Inner brow raiser is one of the most difficult facial actions to find clean references for. Many sources fail to find actors who can separate their inner brow raiser from other facial actions such as outer brow raiser (from frontalis, pars lateralis) and brow lowerer (from corrugator).
Many tech leads live under the assumption that – if they acquire enough data to train their model, problems with quality will simply work themselves out. Wow! Magic. This assumption often operates under an additional (but false) assumption: There is only a negligible percentage of impure data.
As machine learning ambitions grow, data needs grow as well, transforming engineer-centric problems into cross-disciplinary matters.