30 found
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  1. Explainable machine learning practices: opening another black box for reliable medical AI.Emanuele Ratti & Mark Graves - 2022 - AI and Ethics:1-14.
    In the past few years, machine learning (ML) tools have been implemented with success in the medical context. However, several practitioners have raised concerns about the lack of transparency—at the algorithmic level—of many of these tools; and solutions from the field of explainable AI (XAI) have been seen as a way to open the ‘black box’ and make the tools more trustworthy. Recently, Alex London has argued that in the medical context we do not need machine learning tools to be (...)
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  2.  70
    What does it mean to embed ethics in data science? An integrative approach based on the microethics and virtues.Louise Bezuidenhout & Emanuele Ratti - 2021 - AI and Society 36:939–953.
    In the past few years, scholars have been questioning whether the current approach in data ethics based on the higher level case studies and general principles is effective. In particular, some have been complaining that such an approach to ethics is difficult to be applied and to be taught in the context of data science. In response to these concerns, there have been discussions about how ethics should be “embedded” in the practice of data science, in the sense of showing (...)
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  3.  76
    Cultivating Moral Attention: a Virtue-Oriented Approach to Responsible Data Science in Healthcare.Emanuele Ratti & Mark Graves - 2021 - Philosophy and Technology 34 (4):1819-1846.
    In the past few years, the ethical ramifications of AI technologies have been at the center of intense debates. Considerable attention has been devoted to understanding how a morally responsible practice of data science can be promoted and which values have to shape it. In this context, ethics and moral responsibility have been mainly conceptualized as compliance to widely shared principles. However, several scholars have highlighted the limitations of such a principled approach. Drawing from microethics and the virtue theory tradition, (...)
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  4.  74
    Science and values: a two-way direction.Emanuele Ratti & Federica Russo - 2024 - European Journal for Philosophy of Science 14 (1):1-23.
    In the science and values literature, scholars have shown how science is influenced and shaped by values, often in opposition to the ‘value free’ ideal of science. In this paper, we aim to contribute to the science and values literature by showing that the relation between science and values flows not only from values into scientific practice, but also from (allegedly neutral) science to values themselves. The extant literature in the ‘science and values’ field focuses by and large on reconstructing, (...)
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  5. Junk or functional DNA? ENCODE and the function controversy.Pierre-Luc Germain, Emanuele Ratti & Federico Boem - 2014 - Biology and Philosophy 29 (6):807-831.
    In its last round of publications in September 2012, the Encyclopedia Of DNA Elements (ENCODE) assigned a biochemical function to most of the human genome, which was taken up by the media as meaning the end of ‘Junk DNA’. This provoked a heated reaction from evolutionary biologists, who among other things claimed that ENCODE adopted a wrong and much too inclusive notion of function, making its dismissal of junk DNA merely rhetorical. We argue that this criticism rests on misunderstandings concerning (...)
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  6. Big Data Biology: Between Eliminative Inferences and Exploratory Experiments.Emanuele Ratti - 2015 - Philosophy of Science 82 (2):198-218.
    Recently, biologists have argued that data - driven biology fosters a new scientific methodology; namely, one that is irreducible to traditional methodologies of molecular biology defined as the discovery strategies elucidated by mechanistic philosophy. Here I show how data - driven studies can be included into the traditional mechanistic approach in two respects. On the one hand, some studies provide eliminative inferential procedures to prioritize and develop mechanistic hypotheses. On the other, different studies play an exploratory role in providing useful (...)
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  7.  98
    (1 other version)Data science and molecular biology: prediction and mechanistic explanation.Ezequiel López-Rubio & Emanuele Ratti - 2019 - Synthese (4):1-26.
    In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data science (machine learning in (...)
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  8.  94
    What kind of novelties can machine learning possibly generate? The case of genomics.Emanuele Ratti - 2020 - Studies in History and Philosophy of Science Part A 83:86-96.
    Machine learning (ML) has been praised as a tool that can advance science and knowledge in radical ways. However, it is not clear exactly how radical are the novelties that ML generates. In this article, I argue that this question can only be answered contextually, because outputs generated by ML have to be evaluated on the basis of the theory of the science to which ML is applied. In particular, I analyze the problem of novelty of ML outputs in the (...)
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  9.  70
    A relic of design: against proper functions in biology.Emanuele Ratti & Pierre-Luc Germain - 2022 - Biology and Philosophy 37 (4):1-28.
    The notion of biological function is fraught with difficulties—intrinsically and irremediably so, we argue. The physiological practice of functional ascription originates from a time when organisms were thought to be designed and remained largely unchanged since. In a secularized worldview, this creates a paradox which accounts of functions as selected effect attempt to resolve. This attempt, we argue, misses its target in physiology and it brings problems of its own. Instead, we propose that a better solution to the conundrum of (...)
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  10. ‘Models of’ and ‘Models for’: On the Relation between Mechanistic Models and Experimental Strategies in Molecular Biology.Emanuele Ratti - 2020 - British Journal for the Philosophy of Science 71 (2):773-797.
    Molecular biologists exploit information conveyed by mechanistic models for experimental purposes. In this article, I make sense of this aspect of biological practice by developing Keller’s idea of the distinction between ‘models of’ and ‘models for’. ‘Models of (phenomena)’ should be understood as models representing phenomena and are valuable if they explain phenomena. ‘Models for (manipulating phenomena)’ are new types of material manipulations and are important not because of their explanatory force, but because of the interventionist strategies they afford. This (...)
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  11. Mechanistic Models and the Explanatory Limits of Machine Learning.Emanuele Ratti & Ezequiel López-Rubio - unknown
    We argue that mechanistic models elaborated by machine learning cannot be explanatory by discussing the relation between mechanistic models, explanation and the notion of intelligibility of models. We show that the ability of biologists to understand the model that they work with severely constrains their capacity of turning the model into an explanatory model. The more a mechanistic model is complex, the less explanatory it will be. Since machine learning increases its performances when more components are added, then it generates (...)
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  12.  90
    Conceptual Challenges in the Theoretical Foundations of Systems Biology.Marta Bertolaso & Emanuele Ratti - 2018 - In Mariano Bizzarri (ed.), Systems Biology. Springer, Humana Press. pp. 1-13.
    In the last decade, Systems Biology has emerged as a conceptual and explanatory alternative to reductionist-based approaches in molecular biology. However, the foundations of this new discipline need to be fleshed out more carefully. In this paper, we claim that a relational ontology is a necessary tool to ground both the conceptual and explanatory aspects of Systems Biology. A relational ontology holds that relations are prior—both conceptually and explanatory—to entities, and that in the biological realm entities are defined primarily by (...)
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  13.  60
    Integrating Artificial Intelligence in Scientific Practice: Explicable AI as an Interface.Emanuele Ratti - 2022 - Philosophy and Technology 35 (3):1-5.
    A recent article by Herzog provides a much-needed integration of ethical and epistemological arguments in favor of explicable AI in medicine. In this short piece, I suggest a way in which its epistemological intuition of XAI as “explanatory interface” can be further developed to delineate the relation between AI tools and scientific research.
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  14. Microethics for healthcare data science: attention to capabilities in sociotechnical systems.Mark Graves & Emanuele Ratti - 2021 - The Future of Science and Ethics 6:64-73.
    It has been argued that ethical frameworks for data science often fail to foster ethical behavior, and they can be difficult to implement due to their vague and ambiguous nature. In order to overcome these limitations of current ethical frameworks, we propose to integrate the analysis of the connections between technical choices and sociocultural factors into the data science process, and show how these connections have consequences for what data subjects can do, accomplish, and be. Using healthcare as an example, (...)
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  15. The End of 'Small Biology'? Some Thoughts About Biomedicine and Big Science.Emanuele Ratti - 2016 - Big Data and Society:1-6.
    In biology—as in other scientific fields—there is a lively opposition between big and small science projects. In this commentary, I try to contextualize this opposition in the field of biomedicine, and I argue that, at least in this context, big science projects should come first.
     
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  16. Towards a Notion of Intervention in Big-Data Biology and Molecular Medicine.Emanuele Ratti & Federico Boem - 2016 - In Marco Nathan & Giovanni Boniolo (eds.), Foundational Issues in Molecular Medicine. Routledge.
    We claim that in contemporary studies in molecular biology and biomedicine, the nature of ‘manipulation’ and ‘intervention’ has changed. Traditionally, molecular biology and molecular studies in medicine are considered experimental sciences, whereas experiments take the form of material manipulation and intervention. On the contrary “big science” projects in biology focus on the practice of data mining of biological databases. We argue that the practice of data mining is a form of intervention although it does not require material manipulation. We also (...)
     
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  17.  40
    Science, Technology, and Virtues: Contemporary Perspectives.Emanuele Ratti & Tom Stapleford (eds.) - 2021 - Oxford University Press.
  18.  58
    What Do We Teach to Engineering Students: Embedded Ethics, Morality, and Politics.Avigail Ferdman & Emanuele Ratti - 2024 - Science and Engineering Ethics 30 (1):1-26.
    In the past few years, calls for integrating ethics modules in engineering curricula have multiplied. Despite this positive trend, a number of issues with these ‘embedded’ programs remains. First, learning goals are underspecified. A second limitation is the conflation of different dimensions under the same banner, in particular confusion between ethics curricula geared towards addressing the ethics of individual conduct and curricula geared towards addressing ethics at the societal level. In this article, we propose a tripartite framework to overcome these (...)
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  19. Phronesis and Automated Science: The Case of Machine Learning and Biology.Emanuele Ratti - 2020 - In Marta Bertolaso & Fabio Sterpetti (eds.), A Critical Reflection on Automated Science: Will Science Remain Human? Cham: Springer.
    The applications of machine learning and deep learning to the natural sciences has fostered the idea that the automated nature of algorithmic analysis will gradually dispense human beings from scientific work. In this paper, I will show that this view is problematic, at least when ML is applied to biology. In particular, I will claim that ML is not independent of human beings and cannot form the basis of automated science. Computer scientists conceive their work as being a case of (...)
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  20.  27
    Character Comes from Practice: Longitudinal Practice-Based Ethics Training in Data Science.Louise Bezuidenhout & Emanuele Ratti - 2024 - In E. Hildt, K. Laas, C. Miller & E. Brey (eds.), Building Inclusive Ethical Cultures in STEM. Springer Verlag. pp. 181-201.
    In this chapter, we propose a non-traditional RCR training in data science that is grounded in a virtue theory framework. First, we delineate the approach in more theoretical detail by discussing how the goal of RCR training is to foster the cultivation of certain moral abilities. We specify the nature of these ‘abilities’: while the ideal is the cultivation of virtues, the limited space allowed by RCR modules can only facilitate the cultivation of superficial abilities or proto-virtues, which help students (...)
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  21.  33
    Epistemological Challenges of Artificial Intelligence Clinical Decision Support Tools in Otolaryngology: The Black Box Problem.Emanuele Ratti, Christopher Babu, Christopher Holsinger, Lena Zuchowski & Anaïs Rameau - 2023 - Otolaryngology - Head and Neck Surgery 1:1-4.
  22.  41
    The Main Faces of Robustness.Giovanni Boniolo, Mattia Andreoletti, Federico Boem & Emanuele Ratti - 2017 - Dialogue and Universalism 27 (3):157-172.
    In the last decade, robustness has been extensively mentioned and discussed in biology as well as in the philosophy of the life sciences. Nevertheless, from both fields, someone has affirmed that this debate has resulted in more semantic confusion than in semantic clearness. Starting from this claim, we wish to offer a sort of prima facie map of the different usages of the term. In this manner we would intend to predispose a sort of “semantic platform” which could be exploited (...)
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  23.  67
    Who Is a Good Data Scientist? A Reply to Curzer and Epstein.Mark Graves & Emanuele Ratti - 2022 - Philosophy and Technology 35 (2):1-5.
  24.  62
    Levels of abstraction, emergentism and artificial life.Emanuele Ratti - 2014 - Journal of Experimental & Theoretical Artificial Intelligence:1-12.
    I diagnose the current debate between epistemological and ontological emergentism as a Kantian antinomy, which has reasonable but irreconcilable thesis and antithesis. Kantian antinomies have recently returned to contemporary philosophy in part through the work of Luciano Floridi, and the method of levels of abstraction. I use a thought experiment concerning a computer simulation to show how to resolve the epistemological/ontological antinomy about emergence. I also use emergentism and simulations in artificial life to illuminate both levels of abstraction and theoretical (...)
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  25.  73
    Diverse perspectives on ontology: A joint report on the First IAOA Interdisciplinary Summer School on Ontological Analysis.Emilio Sanfilippo, Emanuele Ratti, Francesca Quattri, Aleksandra Sojic, Federico Boem, Gaoussou Camara & Eric Chuk - 2013 - Applied ontology 8 (1):59-71.
  26.  28
    Science and Politics in a Time of Pandemic: Some Epistemological and Political Lessons from the Italian Story.Federico Boem & Emanuele Ratti - 2021 - Humana Mente 14 (40).
    Making public policy choices based on available scientific evidence is an ideal condition for any policy making. However, the mechanisms governing these scenarios are complex, non-linear, and, alongside the medical-health and epidemiological issues, involve socio-economic, political, communicative, informational, ethical and epistemological aspects. In this article we analyze the role of scientific evidence when implementing political decisions that strictly depend on it, as in the case of the COVID-19 pandemic. In carrying out this analysis, we will focus above all on the (...)
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  27.  21
    Philipp Fischer, Gabriele Gramelsberger, Christoph Hoffmann, Hans Hofmann, Hans-Jorg Rheinberger, Hannes Rickli, Natures of Data: A Discussion Between Biology, History and Philosophy of Science and Art, Zurich: Diaphanes, 2020.Emanuele Ratti - 2022 - History and Philosophy of the Life Sciences 44 (1):1-4.
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  28.  33
    Unity of Science and Ethics of Belief.Emanuele Ratti - 2018 - Philosophy, Theology and the Sciences 5 (1):5.
  29.  55
    Big data in the experimental life sciences: Bruno J. Strasser: Collecting experiments: Making big data biology. Chicago: The University of Chicago Press, 2019, 392 pp, $45.00. [REVIEW]Emanuele Ratti - 2020 - Metascience 29 (3):403-408.
  30.  21
    D enis N oble, Dance to the Tune of Life — Biological Relativity, Cambridge: Cambridge University Press, December 2016, 302 pp., £ 17.99. [REVIEW]Emanuele Ratti - 2018 - History and Philosophy of the Life Sciences 40 (3):54.