Deliberative Indispensability and Epistemic Justification

Many of us care about the existence of ethical facts because such facts appear crucial to making sense of our practical lives. On one tempting line of thought, this idea does more than raise the metaethical stakes: it can also play a central role in justifying our belief in those facts. In recent work, David Enoch has developed this tempting thought into a formidable new proposal in moral epistemology, that aims to explain how the deliberative indispensability of ethical facts gives us epistemic justification for believing in such facts. In this paper, we argue that Enoch’s proposal fails because it conflicts with a central fact about epistemic justification: that the norms of epistemic justification have the content that they do in part because of some positive connection between those norms and the truth of the beliefs that these norms govern. We then argue that the most salient alternatives to Enoch’s attempt to defend the idea that deliberative indispensability confers epistemic justification fail for parallel reasons. We conclude that the tempting line of thought should be rejected: deliberative indispensability does not provide epistemic justification

I'm sorry to say, but your understanding of image processing fundamentals is absolutely wrong

The ongoing discussion whether modern vision systems have to be viewed as visually-enabled cognitive systems or cognitively-enabled vision systems is groundless, because perceptual and cognitive faculties of vision are separate components of human (and consequently, artificial) information processing system modeling.Comment: To be published as chapter 5 in "Frontiers in Brain, Vision and AI", I-TECH Publisher, Viena, 200

In the Beginning Was the Verb: The Emergence and Evolution of Language Problem in the Light of the Big Bang Epistemological Paradigm.

The enigma of the Emergence of Natural Languages, coupled or not with the closely related problem of their Evolution is perceived today as one of the most important scientific problems. \ud The purpose of the present study is actually to outline such a solution to our problem which is epistemologically consonant with the Big Bang solution of the problem of the Emergence of the Universe}. Such an outline, however, becomes articulable, understandable, and workable only in a drastically extended epistemic and scientific oecumene, where known and habitual approaches to the problem, both theoretical and experimental, become distant, isolated, even if to some degree still hospitable conceptual and methodological islands. \ud The guiding light of our inquiry will be Eugene Paul Wigner's metaphor of ``the unreasonable effectiveness of mathematics in natural sciences'', i.e., the steadily evolving before our eyes, since at least XVIIth century, \ud ``the miracle of the appropriateness of the language of mathematics for the formulation of the laws of physics''. Kurt Goedel's incompleteness and undecidability theory will be our guardian discerner against logical fallacies of otherwise apparently plausible explanations. \ud John Bell's ``unspeakableness'' and the commonplace counterintuitive character of quantum phenomena will be our encouragers. And the radical novelty of the introduced here and adapted to our purposes Big Bang epistemological paradigm will be an appropriate, even if probably shocking response to our equally shocking discovery in the oldest among well preserved linguistic fossils of perfect mathematical structures outdoing the best artifactual Assemblers

The Creative Frontier of Generative AI: Managing the Novelty-Usefulness Tradeoff

In this paper, drawing inspiration from the human creativity literature, we explore the optimal balance between novelty and usefulness in generative Artificial Intelligence (AI) systems. We posit that overemphasizing either aspect can lead to limitations such as hallucinations and memorization. Hallucinations, characterized by AI responses containing random inaccuracies or falsehoods, emerge when models prioritize novelty over usefulness. Memorization, where AI models reproduce content from their training data, results from an excessive focus on usefulness, potentially limiting creativity. To address these challenges, we propose a framework that includes domain-specific analysis, data and transfer learning, user preferences and customization, custom evaluation metrics, and collaboration mechanisms. Our approach aims to generate content that is both novel and useful within specific domains, while considering the unique requirements of various contexts

Explicit Instruction of Scientific Explanation and Argument in an Undergraduate Introductory Biology Laboratory Course Using the Claim, Evidence and Reasoning Framework

Scientific explanation and scientific argument are important aspects of science inquiry and science education. Though students of all ages find construction of written scientific explanations and arguments difficult, explicit instruction in scientific explanation and argument can improve student ability. This quantitative research study used a non-equivalent comparison group design to investigate the affect of explicit instruction of scientific explanation and argument in an undergraduate introductory biology laboratory course, using a college level appropriate modified CER Framework (McNeill et al., 2006). This study compared students receiving explicit instruction in scientific explanation and argument with students taking traditionally-designed biology laboratory courses. Students who received explicit instruction in scientific explanation and argument had significantly higher mean score increases from their pretest to posttest written scientific explanations and arguments. Additionally, students who received explicit instruction in scientific explanation and argument had significantly higher adjusted mean Colorado Learning Attitudes about Science Survey for Use in Biology (CLASS-bio) (Semsar, Knight, Birol, & Smith, 2011) posttest overall percent favorable scores, suggesting these students have more expert aligned views of biology as a discipline of science and the nature of science. Over the course of fourteen class meetings during the Fall 2013 semester, students receiving explicit instruction in scientific explanation and argument improved in ability to construct written scientific explanations and arguments. When constructing a model to explain student variation in ability to construct written scientific explanations and arguments biological content knowledge and explicit instruction were two influencing variable that contributed significantly to the model. The results of this research study provide information on the role of explicit instruction in scientific explanation and argument in undergraduate introductory biology laboratory courses, and shows how to successfully include this valuable science inquiry practice into more traditionally designed laboratories often taught in larger universities

Science Friction: Phenomenology, Naturalism and Cognitive Science

Recent years have seen growing evidence of a fruitful engagement between phenomenology and cognitive science. This paper confronts an in-principle problem that stands in the way of this (perhaps unlikely) intellectual coalition, namely the fact that a tension exists between the transcendentalism that characterizes phenomenology and the naturalism that accompanies cognitive science. After articulating the general shape of this tension, I respond as follows. First, I argue that, if we view things through a kind of neo-McDowellian lens, we can open up a conceptual space in which phenomenology and cognitive science may exert productive constraints on each other. Second, I describe some examples of phenomenological cognitive science that illustrate such constraints in action. Third, I use the mutually constraining relationship at work here as the platform from which to bring to light a domesticated version of the transcendental and a minimal form of naturalism that are compatible with each other

Towards a Digital Ecosystem of Trust: Ethical, Legal and Societal Implications

The European vision of a digital ecosystem of trust rests on innovation, powerful technological solutions, a comprehensive regulatory framework and respect for the core values and principles of ethics. Innovation in the digital domain strongly relies on data, as has become obvious during the current pandemic. Successful data science, especially where health data are concerned, necessitates establishing a framework where data subjects can feel safe to share their data. In this paper, methods for facilitating data sharing, privacy-preserving technologies, decentralization, data altruism, as well as the interplay between the Data Governance Act and the GDPR, are presented and discussed by reference to use cases from the largest pan-European social science data research project, SoBigData++. In doing so, we argue that innovation can be turned into responsible innovation and Europe can make its ethics work in digital practice