A "horse" is a system that is not actually addressing the problem it appears to be solving. The inspiration for the metaphor is the real-life example of Clever Hans, a horse that appeared to have great skill in mathematics but had actually learned to respond to a prosaic cue confounded with the correct answer. Similarly, a model created through the statistical treatment of a large dataset and wielded by an engineer can also appear successful for solving a complex problem, but actually not be. In this talk, I take a critical look at past applications of machine learning - exemplifying contemporary practices - and identify where issues arise that affect the validity of conclusions. I argue that the onus is on the engineer to not stop at describing how well a model performs on a given dataset (no matter how big it may be), but to go further and explain what they with their models are actually doing.
Time: Thursday, 27th of February 2020, 6:30 p.m. sharp
Location: Oesterreichisches Forschungsinstitut fuer Artificial Intelligence (OFAI), Freyung 6, Stiege 6, 1010 Wien
