•  168
    Zemblanity and Big Data: the ugly truths the algorithms remind us of
    Acta Scientiarum. Human and Social Sciences 44 (1): 1-7. 2022.
    In this paper, we will argue that, while Big Data enthusiasts imply that the analysis of massive data sets can produce serendipitous (that is, unexpected and fortunate) discoveries, the way those models are currently designed not only does not create serendipity so easily but also frequently generates zemblanitous (that is, expected and unfortunate) findings.
  •  163
    Big Data: truth, quasi-truth or post-truth?
    Acta Scientiarum. Human and Social Sciences 42 (3): 1-7. 2020.
    In this paper we investigate if sentences presented as the result of the application of statistical models and artificial intelligence to large volumes of data – the so-called ‘Big Data’ – can be characterized as semantically true, or as quasi-true, or even if such sentences can only be characterized as probably quasi-false and, in a certain way, post-true; that is, if, in the context of Big Data, the representation of a data domain can be configured as a total structure, or as a partial structu…Read more
  •  139
    In this paper, we argue that both zemblanity and self-fulfilling prophecy may emerge from the application of Big Data models in society due to the presence of feedback loops.
  •  123
    Inspired by the early Wittgenstein’s concept of nonsense (meaning that which lies beyond the limits of language), we define two different, yet complementary, types of nonsense: formal nonsense and pragmatic nonsense. The simpler notion of formal nonsense is initially defined within Tarski’s semantic theory of truth; the notion of pragmatic nonsense, by its turn, is formulated within the context of the theory of pragmatic truth, also known as quasi-truth, as formalized by da Costa and his collabo…Read more