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@inproceedings{saw-1,
  title={The self-avoiding walk},
  author={Neal Madras and Gordon Slade},
  year={1991}
}

@article{saw-2,
  title = {Exact Critical Point and Critical Exponents of $\mathrm{O}(n)$ Models in Two Dimensions},
  author = {Nienhuis, Bernard},
  journal = {Phys. Rev. Lett.},
  volume = {49},
  issue = {15},
  pages = {1062--1065},
  numpages = {0},
  year = {1982},
  month = {10},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevLett.49.1062},
  url = {https://link.aps.org/doi/10.1103/PhysRevLett.49.1062}
}

@article{saw-3,
doi = {10.1088/0305-4470/36/15/201},
url = {https://dx.doi.org/10.1088/0305-4470/36/15/201},
year = {2003},
month = {04},
publisher = {},
volume = {36},
number = {15},
pages = {R11},
author = {E J Janse van Rensburg},
title = {Statistical mechanics of directed models of polymers in the square lattice},
journal = {Journal of Physics A: Mathematical and General},
abstract = {Directed square lattice models of polymers and vesicles have received considerable attention in the recent mathematical and physical sciences literature. These are idealized geometric directed lattice models introduced to study phase behaviour in polymers, and include Dyck paths, partially directed paths, directed trees and directed vesicles models. Directed models are closely related to models studied in the combinatorics literature (and are often exactly solvable). They are also simplified versions of a number of statistical mechanics models, including the self-avoiding walk, lattice animals and lattice vesicles. The exchange of approaches and ideas between statistical mechanics and combinatorics have considerably advanced the description and understanding of directed lattice models, and this will be explored in this review.

The combinatorial nature of directed lattice path models makes a study using generating function approaches most natural. In contrast, the statistical mechanics approach would introduce partition functions and free energies, and then investigate these using the general framework of critical phenomena. Generating function and statistical mechanics approaches are closely related. For example, questions regarding the limiting free energy may be approached by considering the radius of convergence of a generating function, and the scaling properties of thermodynamic quantities are related to the asymptotic properties of the generating function.

In this review the methods for obtaining generating functions and determining free energies in directed lattice path models of linear polymers is presented. These methods include decomposition methods leading to functional recursions, as well as the Temperley method (that is implemented by creating a combinatorial object, one slice at a time). A constant term formulation of the generating function will also be reviewed.

The thermodynamic features and critical behaviour in models of directed paths may be informative about the underlying properties that determine phase diagrams for wider classes of models, including physical models of polymers. Of particular interest are adsorption and collapse transitions in models of polymers and copolymers. The properties of thermodynamic quantities in those models are described by tricritical scaling. This is reviewed for directed path models, and the generating function approaches can be used to apply tricritical scaling to models of adsorbing, inflating and collapsing directed lattice paths. Critical exponents for a variety of models can be obtained in this manner, and with it a better understanding, and a classification, of the models.}
}





@article{DBLP:journals/corr/abs-2009-10576,
  author    = {Irene Y. Chen and
               Emma Pierson and
               Sherri Rose and
               Shalmali Joshi and
               Kadija Ferryman and
               Marzyeh Ghassemi},
  title     = {Ethical Machine Learning in Health Care},
  journal   = {CoRR},
  volume    = {abs/2009.10576},
  year      = {2020},
  url       = {https://arxiv.org/abs/2009.10576},
  eprinttype = {arXiv},
  eprint    = {2009.10576},
  timestamp = {Wed, 30 Sep 2020 08:21:46 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2009-10576.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2201-06921,
  author    = {Miguel Sicart and
               Irina Shklovski and
               Mirabelle Jones},
  title     = {Can Machine Learning be Moral?},
  journal   = {CoRR},
  volume    = {abs/2201.06921},
  year      = {2022},
  url       = {https://arxiv.org/abs/2201.06921},
  eprinttype = {arXiv},
  eprint    = {2201.06921},
  timestamp = {Fri, 21 Jan 2022 13:57:15 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2201-06921.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@misc{saw,
  doi = {10.48550/ARXIV.1206.2092},
  
  url = {https://arxiv.org/abs/1206.2092},
  
  author = {Bauerschmidt, Roland and Duminil-Copin, Hugo and Goodman, Jesse and Slade, Gordon},
  
  keywords = {Probability (math.PR), Mathematical Physics (math-ph), Combinatorics (math.CO), FOS: Mathematics, FOS: Mathematics, FOS: Physical sciences, FOS: Physical sciences},
  
  title = {Lectures on Self-Avoiding Walks},
  
  publisher = {arXiv},
  
  year = {2012},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}


@article{DBLP:journals/corr/abs-1908-09635,
  author    = {Ninareh Mehrabi and
               Fred Morstatter and
               Nripsuta Saxena and
               Kristina Lerman and
               Aram Galstyan},
  title     = {A Survey on Bias and Fairness in Machine Learning},
  journal   = {CoRR},
  volume    = {abs/1908.09635},
  year      = {2019},
  url       = {http://arxiv.org/abs/1908.09635},
  eprinttype = {arXiv},
  eprint    = {1908.09635},
  timestamp = {Thu, 14 Oct 2021 09:17:44 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1908-09635.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{10.1145/3494672,
author = {Pessach, Dana and Shmueli, Erez},
title = {A Review on Fairness in Machine Learning},
year = {2022},
issue_date = {March 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {55},
number = {3},
issn = {0360-0300},
url = {https://doi.org/10.1145/3494672},
doi = {10.1145/3494672},
abstract = {An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans, and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop ML algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision making may be inherently prone to unfairness, even when there is no intention for it. This article presents an overview of the main concepts of identifying, measuring, and improving algorithmic fairness when using ML algorithms, focusing primarily on classification tasks. The article begins by discussing the causes of algorithmic bias and unfairness and the common definitions and measures for fairness. Fairness-enhancing mechanisms are then reviewed and divided into pre-process, in-process, and post-process mechanisms. A comprehensive comparison of the mechanisms is then conducted, toward a better understanding of which mechanisms should be used in different scenarios. The article ends by reviewing several emerging research sub-fields of algorithmic fairness, beyond classification.},
journal = {ACM Comput. Surv.},
month = {02},
articleno = {51},
numpages = {44},
keywords = {Algorithmic bias, fairness in machine learning, fairness-aware machine learning, algorithmic fairness}
}

@article{DBLP:journals/corr/abs-1901-10002,
  author    = {Harini Suresh and
               John V. Guttag},
  title     = {A Framework for Understanding Unintended Consequences of Machine Learning},
  journal   = {CoRR},
  volume    = {abs/1901.10002},
  year      = {2019},
  url       = {http://arxiv.org/abs/1901.10002},
  eprinttype = {arXiv},
  eprint    = {1901.10002},
  timestamp = {Sat, 02 Feb 2019 16:56:00 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1901-10002.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{aae,
  author    = {Moein Khajehnejad and
               Ahmad Asgharian Rezaei and
               Mahmoudreza Babaei and
               Jessica Hoffmann and
               Mahdi Jalili and
               Adrian Weller},
  title     = {Adversarial Graph Embeddings for Fair Influence Maximization over
               Social Networks},
  journal   = {CoRR},
  volume    = {abs/2005.04074},
  year      = {2020},
  url       = {https://arxiv.org/abs/2005.04074},
  eprinttype = {arXiv},
  eprint    = {2005.04074},
  timestamp = {Sat, 23 Jan 2021 01:20:27 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2005-04074.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{fairwalk,
  title     = {Fairwalk: Towards Fair Graph Embedding},
  author    = {Rahman, Tahleen and Surma, Bartlomiej and Backes, Michael and Zhang, Yang},
  booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on
               Artificial Intelligence, {IJCAI-19}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  pages     = {3289--3295},
  year      = {2019},
  month     = {7},
  doi       = {10.24963/ijcai.2019/456},
  url       = {https://doi.org/10.24963/ijcai.2019/456},
}


@article{word_representations,
author = {Mikolov, Tomas and Chen, Kai and Corrado, G.s and Dean, Jeffrey},
year = {2013},
month = {01},
pages = {},
title = {Efficient Estimation of Word Representations in Vector Space},
volume = {2013},
journal = {Proceedings of Workshop at ICLR}
}

@article{deepwalk,
  author    = {Bryan Perozzi and
               Rami Al{-}Rfou and
               Steven Skiena},
  title     = {DeepWalk: Online Learning of Social Representations},
  journal   = {CoRR},
  volume    = {abs/1403.6652},
  year      = {2014},
  url       = {http://arxiv.org/abs/1403.6652},
  eprinttype = {arXiv},
  eprint    = {1403.6652},
  timestamp = {Thu, 30 Apr 2020 11:17:18 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/PerozziAS14.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@article{node2vec,
  author    = {Aditya Grover and
               Jure Leskovec},
  title     = {node2vec: Scalable Feature Learning for Networks},
  journal   = {CoRR},
  volume    = {abs/1607.00653},
  year      = {2016},
  url       = {http://arxiv.org/abs/1607.00653},
  eprinttype = {arXiv},
  eprint    = {1607.00653},
  timestamp = {Mon, 13 Aug 2018 16:48:14 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/GroverL16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@misc{crosswalk,
  doi = {10.48550/ARXIV.2105.02725},
  url = {https://arxiv.org/abs/2105.02725},
  author = {Khajehnejad, Ahmad and Khajehnejad, Moein and Babaei, Mahmoudreza and Gummadi, Krishna P. and Weller, Adrian and Mirzasoleiman, Baharan},
  keywords = {Machine Learning (cs.LG), Social and Information Networks (cs.SI), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {CrossWalk: Fairness-enhanced Node Representation Learning},
  publisher = {arXiv},
  year = {2021},
  copyright = {Creative Commons Attribution 4.0 International}
}













