Machine Learning and the Future of Scientific Explanation

Boge, Florian J.; Poznic, Michael

doi:10.1007/s10838-020-09537-z

Cited by 5 publications

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“…It highlights instances where AI has significantly advanced fields like pure mathematics and molecular biology, demonstrating the predictive power and the ability to guide human intuition in complex scientific challenges. Boge et al [31] review the role of ML as an optimization tool executed by digital computers and how its increasing use signifies a shift from traditional scientific aims of explanation towards pattern recognition and prediction. It explores the implications of this shift for scientific explanation and the potential future directions of ML in scientific research.…”

Section: And Scientific Methodsmentioning

confidence: 99%

The Challenges of Machine Learning: A Critical Review

Barbierato,

Gatti

2024

Electronics

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The concept of learning has multiple interpretations, ranging from acquiring knowledge or skills to constructing meaning and social development. Machine Learning (ML) is considered a branch of Artificial Intelligence (AI) and develops algorithms that can learn from data and generalize their judgment to new observations by exploiting primarily statistical methods. The new millennium has seen the proliferation of Artificial Neural Networks (ANNs), a formalism able to reach extraordinary achievements in complex problems such as computer vision and natural language recognition. In particular, designers claim that this formalism has a strong resemblance to the way the biological neurons operate. This work argues that although ML has a mathematical/statistical foundation, it cannot be strictly regarded as a science, at least from a methodological perspective. The main reason is that ML algorithms have notable prediction power although they cannot necessarily provide a causal explanation about the achieved predictions. For example, an ANN could be trained on a large dataset of consumer financial information to predict creditworthiness. The model takes into account various factors like income, credit history, debt, spending patterns, and more. It then outputs a credit score or a decision on credit approval. However, the complex and multi-layered nature of the neural network makes it almost impossible to understand which specific factors or combinations of factors the model is using to arrive at its decision. This lack of transparency can be problematic, especially if the model denies credit and the applicant wants to know the specific reasons for the denial. The model’s “black box” nature means it cannot provide a clear explanation or breakdown of how it weighed the various factors in its decision-making process. Secondly, this work rejects the belief that a machine can simply learn from data, either in supervised or unsupervised mode, just by applying statistical methods. The process of learning is much more complex, as it requires the full comprehension of a learned ability or skill. In this sense, further ML advancements, such as reinforcement learning and imitation learning denote encouraging similarities to similar cognitive skills used in human learning.

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Section: And Scientific Methodsmentioning

confidence: 99%

The Challenges of Machine Learning: A Critical Review

Barbierato,

Gatti

2024

Electronics

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“…In line with this, the philosophy of science literature has tended to discuss machine learning mostly in terms of a choice between competing epistemic values. For example, Boge and Poznic (2021) raise the worry that machine learning will turn science away from the aim of explanation towards mere pattern recognition and prediction. If explanations remain an important goal, it is argued, then additional "second-order" explanatory efforts must be made to first understand how machine learning models perform their predictive tasks (cf.…”

Section: Machine Learning and The End Of Theorymentioning

confidence: 99%

“…Philosophers of science and scientists have taken up the central question of this debate by addressing the impact that the introduction of AI and machine learning is likely to have on the general character of scientific research (e.g. Pietsch, 2015;Canali, 2016;Coveney et al, 2016;Boon, 2020;Creel, 2020;Ourmazd, 2020;Boge and Poznic, 2021;López-Rubio and Ratti, 2021;Boge et al, 2022;Krenn et al, 2022;Duede, 2023;Andrews, 2023). The areas of genetics and molecular biology, which over the last few decades have become highly "data-centric" (Leonelli, 2016), seem particular prone to making the shift from a theory-or hypothesis-driven mode towards purely data-driven modes of research.…”

Section: Introductionmentioning

confidence: 99%

The Explanatory Role of Machine Learning in Molecular Biology

Gross

2024

Erkenn

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The philosophical debate around the impact of machine learning in science is often framed in terms of a choice between AI and classical methods as mutually exclusive alternatives involving difficult epistemological trade-offs. A common worry regarding machine learning methods specifically is that they lead to opaque models that make predictions but do not lead to explanation or understanding. Focusing on the field of molecular biology, I argue that in practice machine learning is often used with explanatory aims. More specifically, I argue that machine learning can be tightly integrated with other, more traditional, research methods and in a clear sense can contribute to insight into the causal processes underlying phenomena of interest to biologists. One could even say that machine learning is not the end of theory in important areas of biology, as has been argued, but rather a new beginning. I support these claims with a detailed discussion of a case study involving gene regulation by microRNAs.

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“…This was done as part of a project called The impact of computer simulations and machine learning on the epistemic status of LHC Data, in which F. J. Boge is also involved as a postdoctoral researcher. Said project, in turn, is part of an interdisciplinary research unit between physics, philosophy, history and social science, called The Epistemology of the Large Hadron Collider and co-funded by the German Research foundation (DFG) and the Austrian Science Fund (FWF).Much in the spirit of the research unit, the resulting workshop was an interdisciplinary effort, as it involved, next to philosophers, also scholars from the earth sciences (see Boge & Poznic, 2021). Given the fruitfulness of this workshop, the present Special Issue was created as a follow-up publication, even though the contributions to both largely differ.The essays collected in this Special Issue represent a broad spectrum of perspectives on the issue of explanation in the context of ML, as used in science and beyond.…”

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confidence: 99%

“…Much in the spirit of the research unit, the resulting workshop was an interdisciplinary effort, as it involved, next to philosophers, also scholars from the earth sciences (see Boge & Poznic, 2021). Given the fruitfulness of this workshop, the present Special Issue was created as a follow-up publication, even though the contributions to both largely differ.…”

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confidence: 99%

Minds and Machines Special Issue: Machine Learning: Prediction Without Explanation?

Boge

Grünke²,

Hillerbrand³

2022

Minds & Machines

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design drugs to turn proteins off (or on)? How can we design proteins to perform new functions? Hence, there is a sense in which AlphaFold2's remarkable prediction comes without an explanation.This is an important sense, to be sure: AlphaFold2 does not explain how protein folding works. It seems to have somehow learned to bypass the step of explicitly modeling the biological mechanisms leading to the folded protein. Or maybe, there is an image of this mechanism somehow contained in the activation patterns of the nonlinear functions making up AlphaFold2. But this brings us to yet another sense in which the prediction comes without an explanation: AlphaFold2's own functioning in many ways needs an explanation itself.The implications of this want of explanation in the face of successful predictions are far reaching. For instance, the trustworthiness of ML algorithms in society is a big issue, and it depends, for the most part, on the ability to explain their functioning. Ethical issues like these generally profit from philosophers' inputs, as evidenced by the numerous projects and niches on the ethics and societal impact of Artificial Intelligence that we can see originate today. 1 However, for the sake of using ML in science, the other need for explanationthat concerned with an understanding of the mechanisms whose outcome is so successfully predicted by the ML algorithm-certainly obtains a special relevance as well. For, assuming that it remains difficult to understand and explain ML predictions, but that the scientific use of these methods keeps increasing over time, the question arises whether this changes the aims of science from explanation to 'mere' prediction.These and further issues were first explored, by the editors of this issue, in a workshop organized by P. Grünke and R. Hillerbrand at the Karlsruhe Institute of Technology. This was done as part of a project called The impact of computer simulations and machine learning on the epistemic status of LHC Data, in which F. J. Boge is also involved as a postdoctoral researcher. Said project, in turn, is part of an interdisciplinary research unit between physics, philosophy, history and social science, called The Epistemology of the Large Hadron Collider and co-funded by the German Research foundation (DFG) and the Austrian Science Fund (FWF).Much in the spirit of the research unit, the resulting workshop was an interdisciplinary effort, as it involved, next to philosophers, also scholars from the earth sciences (see Boge & Poznic, 2021). Given the fruitfulness of this workshop, the present Special Issue was created as a follow-up publication, even though the contributions to both largely differ.The essays collected in this Special Issue represent a broad spectrum of perspectives on the issue of explanation in the context of ML, as used in science and beyond. Below, we offer a brief summary of their core theses for the reader's orientation.1 Two of us (P. Grünke & R. Hillerbrand) have, for instance, as members of the AI Ethics Impact Group (AIEIG), particip...

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Machine Learning and the Future of Scientific Explanation

Cited by 5 publications

References 0 publications

The Challenges of Machine Learning: A Critical Review

The Challenges of Machine Learning: A Critical Review

The Explanatory Role of Machine Learning in Molecular Biology

Minds and Machines Special Issue: Machine Learning: Prediction Without Explanation?

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