A common intuition is that the more difficult it is for someone to do the right thing, the more praiseworthy she is for succeeding and the less blameworthy she is for failing. Here, I call this the ‘Difficulty Thesis’ and argue that the Difficulty Thesis is false. In Section 2, I briefly describe what I mean by ‘difficulty’ and the Difficulty Thesis. The Difficulty Thesis has strong prima facie appeal, however, why exactly difficulty is morally relevant remains an open and (...) interesting philosophical question. I discuss this briefly in Section 3. Then, in Section 4, I argue that the Difficulty Thesis is in fact false. That is, difficulty is not relevant to degrees of praise- and blameworthiness. In Section 5, I offer several explanations for why many are fooled by the illusion of relevance of Difficulty. I argue that difficulty is, at best, an imperfect heuristic with regard to evaluations of praise- and blameworthiness. I also argue that while difficulty is not relevant to praiseworthiness and blameworthiness, difficulty is relevant to practices of praise and blame. I conclude, then, that the Difficulty Thesis ought to be rejected. (shrink)

John Danaher and Sven Nyholm have argued that automation, especially of the sort powered by artificial intelligence, poses a threat to meaningful work by diminishing the chances for meaning-conferring workplace achievement, what they call “achievement gaps”. In this paper, I argue that Danaher and Nyholm’s achievement gap thesis suffers from an ambiguity. The weak version of the thesis holds that automation may result in the appearance of achievement gaps, whereas the strong version holds that automation may result on balance loss (...) in possibilities for workplace achievements, i.e., in the appearance of an overall gappier work landscape. Against the strong version of the achievement gap thesis, I characterize situations where automation may result in boons to meaning-conferring workplace achievements: the appearance of what I call achievement spread and achievement swaps. Nevertheless, Danaher and Nyholm are right to worry about some uses of automation whereby human workers become subservient to AI. But these situations are better framed, I argue, as autonomy gaps rather than achievement gaps. (shrink)