What is disinformation?

Prototypical instances of disinformation include deceptive advertising (in business and in politics), government propaganda, doctored photographs, forged documents, fake maps, internet frauds, fake websites, and manipulated Wikipedia entries. Disinformation can cause significant harm if people are misled by it. In order to address this critical threat to information quality, we first need to understand exactly what disinformation is. This paper surveys the various analyses of this concept that have been proposed by information scientists and philosophers (most notably, Luciano Floridi). It argues that these analyses are either too broad (that is, that they include things that are not disinformation), or too narrow (they exclude things that are disinformation), or both. Indeed, several of these analyses exclude important forms of disinformation, such as true disinformation, visual disinformation, side-effect disinformation, and adaptive disinformation. After considering the shortcomings of these analyses, the paper argues that disinformation is misleading information that has the function of misleading. Finally, in addition to responding to Floridi’s claim that such a precise analysis of disinformation is not necessary, it briefly discusses how this analysis can help us develop techniques for detecting disinformation and policies for deterring its spread

Toward a Formal Analysis of Deceptive Signaling

Deception has long been an important topic in philosophy (see Augustine 1952; Kant 1996; Chisholm & Feehan 1977; Mahon 2007; Carson 2010). However, the traditional analysis of the concept, which requires that a deceiver intentionally cause her victim to have a false belief, rules out the possibility of much deception in the animal kingdom. Cognitively unsophisticated species, such as fireflies and butterflies, have simply evolved to mislead potential predators and/or prey. To capture such cases of “functional deception,” several researchers (e.g., Sober 1994; Hauser 1997; Searcy & Nowicki 2005, Skyrms 2010) have endorsed the broader view that deception only requires that a deceiver benefit from sending a misleading signal. Moreover, in order to facilitate game-theoretic study of deception in the context of Lewisian sender-receiver games, Brian Skyrms has proposed an influential formal analysis of this view. Such formal analyses have the potential to enhance our philosophical understanding of deception in humans as well as animals. However, as we argue in this paper, Skyrms's analysis, as well as two recently proposed alternative analyses (viz., Godfrey-Smith 2011; McWhirter 2016), are seriously flawed and can lead us to draw unwarranted conclusions about deception

Accuracy, conditionalization, and probabilism

Accuracy-based arguments for conditionalization and probabilism appear to have a significant advantage over their Dutch Book rivals. They rely only on the plausible epistemic norm that one should try to decrease the inaccuracy of one's beliefs. Furthermore, conditionalization and probabilism apparently follow from a wide range of measures of inaccuracy. However, we argue that there is an under-appreciated diachronic constraint on measures of inaccuracy which limits the measures from which one can prove conditionalization, and none of the remaining measures allow one to prove probabilism. That is, among the measures in the literature, there are some from which one can prove conditionalization, others from which one can prove probabilism, but none from which one can prove both. Hence at present, the accuracy-based approach cannot underwrite both conditionalization and probabilism

Accuracy, conditionalization, and probabilism

Accuracy-based arguments for conditionalization and probabilism appear to have a significant advantage over their Dutch Book rivals. They rely only on the plausible epistemic norm that one should try to decrease the inaccuracy of one's beliefs. Furthermore, it seems that conditionalization and probabilism follow from a wide range of measures of inaccuracy. However, we argue that among the measures in the literature, there are some from which one can prove conditionalization, others from which one can prove probabilism, and none from which one can prove both. Hence at present, the accuracy-based approach cannot underwrite both conditionalization and probabilism

Toward a Formal Analysis of Deceptive Signaling

Deception has long been an important topic in philosophy (see Augustine 1952; Kant 1996; Chisholm & Feehan 1977; Mahon 2007; Carson 2010). However, the traditional analysis of the concept, which requires that a deceiver intentionally cause her victim to have a false belief, rules out the possibility of much deception in the animal kingdom. Cognitively unsophisticated species, such as fireflies and butterflies, have simply evolved to mislead potential predators and/or prey. To capture such cases of “functional deception,” several researchers (e.g., Sober 1994; Hauser 1997; Searcy & Nowicki 2005, Skyrms 2010) have endorsed the broader view that deception only requires that a deceiver benefit from sending a misleading signal. Moreover, in order to facilitate game-theoretic study of deception in the context of Lewisian sender-receiver games, Brian Skyrms has proposed an influential formal analysis of this view. Such formal analyses have the potential to enhance our philosophical understanding of deception in humans as well as animals. However, as we argue in this paper, Skyrms's analysis, as well as two recently proposed alternative analyses (viz., Godfrey-Smith 2011; McWhirter 2016), are seriously flawed and can lead us to draw unwarranted conclusions about deception

Accuracy, conditionalization, and probabilism

Accuracy-based arguments for conditionalization and probabilism appear to have a significant advantage over their Dutch Book rivals. They rely only on the plausible epistemic norm that one should try to decrease the inaccuracy of one's beliefs. Furthermore, conditionalization and probabilism apparently follow from a wide range of measures of inaccuracy. However, we argue that there is an under-appreciated diachronic constraint on measures of inaccuracy which limits the measures from which one can prove conditionalization, and none of the remaining measures allow one to prove probabilism. That is, among the measures in the literature, there are some from which one can prove conditionalization, others from which one can prove probabilism, but none from which one can prove both. Hence at present, the accuracy-based approach cannot underwrite both conditionalization and probabilism

Accuracy, conditionalization, and probabilism

Accuracy-based arguments for conditionalization and probabilism appear to have a significant advantage over their Dutch Book rivals. They rely only on the plausible epistemic norm that one should try to decrease the inaccuracy of one's beliefs. Furthermore, it seems that conditionalization and probabilism follow from a wide range of measures of inaccuracy. However, we argue that among the measures in the literature, there are some from which one can prove conditionalization, others from which one can prove probabilism, and none from which one can prove both. Hence at present, the accuracy-based approach cannot underwrite both conditionalization and probabilism

Lies, damned lies, and statistics: An empirical investigation of the concept of lying

Abstract There are many philosophical questions surrounding the notion of lying. Is it ever morally acceptable to lie? Can we acquire knowledge from people who might be lying to us? In order to answer these questions, however, we must first answer the question of what, exactly, constitutes the concept of lying. This paper examines three predominate definitions, as well as some cases-bald-faced lies and lies told with warrant-defeating provisions tacked on-that, arguably, pose problems for some of these definitions. Importantly, theorists working on this topic fundamentally disagree about whether these cases are genuine instances of lying and, thus, serve as counter-examples to the definitions on offer. To settle these disputes, we elicited judgments about the proposed counter-examples from ordinary language users unfettered by theoretical bias. We discuss the results of these experiments and the relevance of the data on the philosophical debate about the definition of lying, as well as some implications for further research on the topic. We suggest that the definition offered by Don Fallis (2009) most closely captures the notion of lying utilized by everyday speakers of English. Finally, we offer some further considerations on the moral implications of our investigation into the concept of lying