no code implementations • 24 Mar 2022 • David Solans, Andrea Beretta, Manuel Portela, Carlos Castillo, Anna Monreale
We observe that this setting elicits mostly rational behavior from participants, who place a moderate amount of trust in the DSS and show neither algorithmic aversion (under-reliance) nor automation bias (over-reliance). However, their stated willingness to accept the DSS in the exit survey seems less sensitive to the accuracy of the DSS than their behavior, suggesting that users are only partially aware of the (lack of) accuracy of the DSS.
1 code implementation • 15 Apr 2020 • David Solans, Battista Biggio, Carlos Castillo
Research in adversarial machine learning has shown how the performance of machine learning models can be seriously compromised by injecting even a small fraction of poisoning points into the training data.