ESDiT Publications

@article{Sullivan2023,

title = {Do Machine Learning Models Represents Their Targets?},

author = {Emily Sullivan},

url = {https://www.esdit.nl/do-machine-learning-models-represent-their-targets/},

doi = {0.1017/psa.2023.151},

year  = {2023},

date = {2023-10-20},

urldate = {2023-10-20},

journal = {Philosophy of Sciences},

pages = {1-11},

abstract = {I argue that machine learning (ML) models used in science function as highly idealized toy models. If we treat ML models as a type of highly idealized toy model, then we can deploy standard representational and epistemic strategies from the toy model literature to explain why ML models can still provide epistemic success despite their lack of similarity to their targets.},

keywords = {Epistemology, Machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{Malinverno2023,

title = {A historical perspective of biomedical explainable AI research},

author = {Luca Malinverno and Vesna Barros and Francesco Ghisoni and Giovanni Visonà and Roman Kern and Philip Nickel and Barbara Ventura and Ilija Šimić and Sarah Stryeck and Francesca Manni and Cesar Ferri and Claire Jean-Quartier and Laura Genga and Gabriele Schweikert and Mario Lovrić and Michal Rosen-Zvi},

url = {https://www.esdit.nl/1-s2-0-s266638992300199x-main/},

doi = {10.1016/j.patter.2023.100830 },

year  = {2023},

date = {2023-09-08},

urldate = {2023-09-08},

journal = {Patterns},

volume = {4},

issue = {9},

pages = {9},

abstract = {The black-box nature of most artificial intelligence (AI) models encourages the development of explainability methods to engender trust into the AI decision-making process. Such methods can be broadly categorized into two main types: post hoc explanations and inherently interpretable algorithms. We aimed at analyzing the possible associations between COVID-19 and the push of explainable AI (XAI) to the forefront of biomedical research. We automatically extracted from the PubMed database biomedical XAI studies related to concepts of causality or explainability and manually labeled 1,603 papers with respect to XAI categories. To compare the trends pre- and post-COVID-19, we fit a change point detection model and evaluated significant changes in publication rates. We show that the advent of COVID-19 in the beginning of 2020 could be the driving factor behind an increased focus concerning XAI, playing a crucial role in accelerating an already evolving trend. Finally, we present a discussion with future societal use and impact of XAI technologies and potential future directions for those who pursue fostering clinical trust with interpretable machine learning models.},

keywords = {Artificial intelligence, Corona virus, COVID-19, Decision-making, Explainability, Foundation models, Machine learning, Meta-review, PRISMA, Trustworthiness},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Contestable AI by Design: Towards a Framework},

author = {Kars Alfrink and Ianus Keller and Gerd Kortuem and Neelke Doorn},

doi = {10.1007/s11023-022-09611-z},

year  = {2022},

date = {2022-08-13},

journal = {Minds and Machines},

keywords = {Artificial intelligence, Automated decision-making, Contestability, Design, Human-computer interaction, Machine learning, Sociotechnical systems},

pubstate = {published},

tppubtype = {article}

}