Simon D'Alfonso

In this paper I look at Fred Dretske’s account of information and knowledge as developed in Knowledge and The Flow of Information. In particular, I translate Dretske’s probabilistic definition of information to a modal logical framework and subsequently use this to explicate the conception of information and its flow which is central to his account, including the notions of channel conditions and relevant alternatives. Some key products of this task are an analysis of the issue of information closure and an investigation into some of the logical properties of Dretske’s account of information flow

Some claim that digital phenotyping will revolutionize understanding of human psychology and experience and significantly promote human wellbeing. This paper investigates the nature of digital phenotyping in relation to its alleged promise. Unlike most of the literature to date on philosophy and digital phenotyping, which has focused on its ethical aspects, this paper focuses on its epistemic and methodological aspects. The paper advances a tetra-taxonomy involving four scenario types in which knowledge may be acquired from human “digitypes” by digital phenotyping. These scenarios comprise two causal relations and a correlative and constitutive relation that can exist between information generated by digital systems/devices on the one hand and psychological or behavioral phenomena on the other. The paper describes several modes of inference involved in deriving knowledge within these scenarios. After this epistemic mapping, the paper analyzes the possible knowledge potential and limitations of digital phenotyping. It finds that digital phenotyping holds promise of delivering insight into conditions and states as well producing potentially new psychological categories. It also argues that care must be taken that digital phenotyping does not make unwarranted conclusions and is aware of potentially distorting effects in digital sensing and measurement. If digital phenotyping is to truly revolutionize knowledge of human life, it must deliver on a range of fronts, including making accurate forecasts and diagnoses of states and behaviors, providing causal explanations of these phenomena, and revealing important constituents of human conditions, psychology, and experience.

Recommender systems assist users in receiving preferred or relevant services and information. Using such technology could be instrumental in addressing the lack of relevance digital mental health apps have to the user, a leading cause of low engagement. However, the use of recommender systems for digital mental health apps, particularly those driven by personal data and artificial intelligence, presents a range of ethical considerations. This paper focuses on considerations particular to the juncture of recommender systems and digital mental health technologies. While separate bodies of work have focused on these two areas, to our knowledge, the intersection presented in this paper has not yet been examined. This paper identifies and discusses a set of advantages and ethical concerns related to incorporating recommender systems into the digital mental health ecosystem. Advantages of incorporating recommender systems into DMH apps are identified as a reduction in choice overload, improvement to the digital therapeutic alliance, and increased access to personal data & self-management. Ethical challenges identified are lack of explainability, complexities pertaining to the privacy/personalization trade-off and recommendation quality, and the control of app usage history data. These novel considerations will provide a greater understanding of how DMH apps can effectively and ethically implement recommender systems.

The merging/fusion of belief/data collections in propositional logic form is a topic that has received due attention within the domains of database and AI research. A distinction can be made between two types of scenarios to which the process of merging can be applied. In the first type, the collections represent preferences, such as the voting choices of a group of people, that need to be aggregated so as to give a consistent result that in some way best represents the collective judgement of the group. In the second type, the collections represent factual data that is to be aggregated with an aim of obtaining a result that maximises factual correctness. After introducing a general framework for belief merging via some prominent literature on the topic, this paper then introduces and considers a method for belief merging with the second type of scenario in mind. Its suitability is corroborated by demonstrating how it can be seen as a special case of a merging procedure that combines aggregation of probabilities and maximisation of expected truthlikeness

The purpose of this paper is to look at some existing methods of semantic information quantification and suggest some alternatives. It begins with an outline of Bar-Hillel and Carnap’s theory of semantic information before going on to look at Floridi’s theory of strongly semantic information. The latter then serves to initiate an in-depth investigation into the idea of utilising the notion of truthlikeness to quantify semantic information. Firstly, a couple of approaches to measure truthlikeness are drawn from the literature and explored, with a focus on their applicability to semantic information quantification. Secondly, a similar but new approach to measure truthlikeness/information is presented and some supplementary points are made.

The KK principle is typically rejected in externalist accounts of knowledge. However, a standard general argument for this rejection is in need of a supportive explication. In a recent paper, Samir Okasha argues that the standard externalist argument in question is fallacious. In this paper I start off with some critical discussion of Okasha’s analysis before suggesting an alternative way in which an externalist might successfully present such a case. I then further explore this issue via a look at how Fred Dretske’s externalist epistemology, one of the exemplifying accounts, can explain failure of the KK principle.