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Testing Artificial Intelligence 127 considered as a cause-effect analysis of unwanted outcome. This could give clues foroptimising thesystem.Forexample:under-fittedsystemsmost likelyneedmore diverse trainingdata,over-fittedsystemsstreamliningof labels. 3.1 Bias The main risks with AI are types of “bias”. In human intelligence we would call this prejudice, reductionism or indecisiveness. Because of limits in training data and concepts, we see things too simple (reductionism) or only from one point of view (prejudice). A high granularity in concepts could mean that the system can’t generaliseenough,making theoutcomeisuseless (indecisiveness). Significant typesofpossiblebias in AIarediscussednext. 3.1.1 SelectionBias If the training data selection misses important elements from the real world, this could lead to selection bias. Compared to the real results, the polls for the last European elections predicted much higher wins for the Eurosceptic parties in the Netherlands than they did in the real election. The polls did not filter on whether people were really going to vote. Eurosceptics provedmore likely not to vote than othervoters. 3.1.2 ConfirmationBias Eagerness to verify an hypothesis heavily believed or invested in can lead to selecting or over-weighing data confirming the thesis over possible falsifications. Scientists, politicians and product developers could be susceptible to this kind of bias, even with the best of intentions. A medical aid organisation exaggerated a possible food crises by showing rising death numbers but not numbers of death unrelated to famineand theoverallpopulationnumber inorder to raisemorefunds. 3.1.3 Under-fitting Training data lacking diversity causes under-fitting. The learning process will not becapable todeterminecriticaldiscriminatingcriteria.Software thatwas trained to recognisewolvesfromdogs, identifieda huskyasa wolfbecause it hadnot learned thatdogscanalsobeseen insnow.Whatwouldhappenifweonlygetdrugs-related newsmessages in theNetherlands?