Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Can transparency contribute to restoring accountability for such systems? Several objections are examined: the loss of privacy when data sets become public, the perverse effects of disclosure of the very algorithms themselves, the potential loss of competitive edge, and the limited gains in answerability to be expected since sophisticated algorithms are inherently opaque. It is concluded that transparency is certainly useful, but only up to a point: extending it to the public at large is normally not to be advised. Moreover, in order to make algorithmic decisions understandable, models of machine learning to be used should either be interpreted ex post or be interpretable by design ex ante.