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Book ; Online: Whither Fair Clustering?

P, Deepak

2020

Abstract: Within the relatively busy area of fair machine learning that has been dominated by classification fairness research, fairness in clustering has started to see some recent attention. In this position paper, we assess the existing work in fair clustering ... ...

Abstract	Within the relatively busy area of fair machine learning that has been dominated by classification fairness research, fairness in clustering has started to see some recent attention. In this position paper, we assess the existing work in fair clustering and observe that there are several directions that are yet to be explored, and postulate that the state-of-the-art in fair clustering has been quite parochial in outlook. We posit that widening the normative principles to target for, characterizing shortfalls where the target cannot be achieved fully, and making use of knowledge of downstream processes can significantly widen the scope of research in fair clustering research. At a time when clustering and unsupervised learning are being increasingly used to make and influence decisions that matter significantly to human lives, we believe that widening the ambit of fair clustering is of immense significance. Comment: Accepted at the AI for Social Good Workshop, Harvard, July 20-21, 2020
Keywords	Computer Science - Computers and Society ; Computer Science - Machine Learning ; Statistics - Machine Learning
Subject code	006
Publishing date	2020-07-08
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Local Connectivity in Centroid Clustering

P, Deepak

2020

Abstract: Clustering is a fundamental task in unsupervised learning, one that targets to group a dataset into clusters of similar objects. There has been recent interest in embedding normative considerations around fairness within clustering formulations. In this ... ...

Abstract	Clustering is a fundamental task in unsupervised learning, one that targets to group a dataset into clusters of similar objects. There has been recent interest in embedding normative considerations around fairness within clustering formulations. In this paper, we propose 'local connectivity' as a crucial factor in assessing membership desert in centroid clustering. We use local connectivity to refer to the support offered by the local neighborhood of an object towards supporting its membership to the cluster in question. We motivate the need to consider local connectivity of objects in cluster assignment, and provide ways to quantify local connectivity in a given clustering. We then exploit concepts from density-based clustering and devise LOFKM, a clustering method that seeks to deepen local connectivity in clustering outputs, while staying within the framework of centroid clustering. Through an empirical evaluation over real-world datasets, we illustrate that LOFKM achieves notable improvements in local connectivity at reasonable costs to clustering quality, illustrating the effectiveness of the method. Comment: In 24th International Database Engineering & Applications Symposium (IDEAS 2020), August 12--14, 2020, Seoul, Republic of Korea
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2020-10-11
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Wastewater-based epidemiology for novel Coronavirus detection in wastewater

G. Saini / P. Deepak

Global Journal of Environmental Science and Management, Vol 7, Iss 4, Pp 643-

2021 Volume 658

Abstract: The entire world is reeling under the worst pandemic of last 100 years. Over 128 million people have been infected with it and 2.8 million deaths have already taken place, till 30th March 2021. The identification of materials positive cases is the first ... ...

Abstract	The entire world is reeling under the worst pandemic of last 100 years. Over 128 million people have been infected with it and 2.8 million deaths have already taken place, till 30th March 2021. The identification of materials positive cases is the first step towards its containment and treatment. However, testing of individuals is an extensive, expensive and time-consuming exercise. In addition, societal taboos are also associated with infected individuals resulting in very few people volunteering for testing, esp. in the developing and under-developed world. An alternative approach that circumvents individual testing is the wastewater-based epidemiology. A state-of-the-art review of this method is provided in context of its utility for COVID-19 detection. This technique relies on collecting and testing samples from sewers and/or wastewater treatment plants for the presence of pathogens and then using that data to determine and predict the spread of the infection, thereby allowing the provision of appropriate containment and treatment steps. The study covers key aspects of wastewater-based epidemiology application for COVID-19 detection including its need, detailed process of detection and assessment, data analysis, economics and challenges to its application. Findings from a number of case studies are presented to elucidate the utility of this technique. It is clearly seen that WBE-based approach is a much better strategy as compared to individual testing and can be adopted to prevent further spread of Covid-19. The work is expected to further emphasize the application of this method for COVID (and other pandemic) detection and implementing containment strategies. This is clearly a much more economical and non-intrusive approach as compared to the individual testing. ==========================================================================================COPYRIGHTS©2021 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.==========================================================================================
Keywords	covid-19 (coronavirus disease) ; infection ; pandemic ; polymerase chain reaction (pcr) ; ribonucleic acid (rna) ; wastewater-based epidemiology (wbe) ; Environmental sciences ; GE1-350
Language	English
Publishing date	2021-10-01T00:00:00Z
Publisher	GJESM Publisher
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: FiSH: fair spatial hot spots.

P, Deepak / Sundaram, Sowmya S

Data mining and knowledge discovery

2022 , Page(s) 1–30

Abstract: Pervasiveness of tracking devices and enhanced availability of spatially located data has deepened interest in using them for various policy interventions, through computational data analysis tasks such as spatial hot spot detection. In this paper, we ... ...

Abstract	Pervasiveness of tracking devices and enhanced availability of spatially located data has deepened interest in using them for various policy interventions, through computational data analysis tasks such as spatial hot spot detection. In this paper, we consider, for the first time to our best knowledge, fairness in detecting spatial hot spots. We motivate the need for ensuring fairness through statistical parity over the collective population covered across chosen hot spots. We then characterize the task of identifying a diverse set of solutions in the noteworthiness-fairness trade-off spectrum, to empower the user to choose a trade-off justified by the policy domain. Being a novel task formulation, we also develop a suite of evaluation metrics for fair hot spots, motivated by the need to evaluate pertinent aspects of the task. We illustrate the computational infeasibility of identifying fair hot spots using naive and/or direct approaches and devise a method, codenamed
Language	English
Publishing date	2022-11-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	1479890-6
ISSN	1573-756X ; 1384-5810
ISSN (online)	1573-756X
ISSN	1384-5810
DOI	10.1007/s10618-022-00887-4
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Gender Bias in Fake News

Sahadevan, Navya / P, Deepak

An Analysis

2022

Abstract: Data science research into fake news has gathered much momentum in recent years, arguably facilitated by the emergence of large public benchmark datasets. While it has been well-established within media studies that gender bias is an issue that pervades ... ...

Abstract	Data science research into fake news has gathered much momentum in recent years, arguably facilitated by the emergence of large public benchmark datasets. While it has been well-established within media studies that gender bias is an issue that pervades news media, there has been very little exploration into the relationship between gender bias and fake news. In this work, we provide the first empirical analysis of gender bias vis-a-vis fake news, leveraging simple and transparent lexicon-based methods over public benchmark datasets. Our analysis establishes the increased prevalance of gender bias in fake news across three facets viz., abundance, affect and proximal words. The insights from our analysis provide a strong argument that gender bias needs to be an important consideration in research into fake news. Comment: Accepted paper in "Integrity in Social Networks and Media 2023" workshop
Keywords	Computer Science - Computers and Society ; Computer Science - Machine Learning
Subject code	070
Publishing date	2022-09-24
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: High-performance computing for static security assessment of large power systems

Venkateswara Rao Kagita / Sanjaya Kumar Panda / Ram Krishan / P. Deepak Reddy / Jabba Aswanth

Connection Science, Vol 35, Iss

2023 Volume 1

Abstract: Contingency analysis (CA) is one of the essential tools for the optimal design and security assessment of a reliable power system. However, its computational requirements rise with the growth of distributed generations in the interconnected power system. ...

Abstract	Contingency analysis (CA) is one of the essential tools for the optimal design and security assessment of a reliable power system. However, its computational requirements rise with the growth of distributed generations in the interconnected power system. As CA is a complex and computationally intensive problem, it requires a fast and accurate calculation to ensure the secure operation. Therefore, efficient mathematical modelling and parallel programming are key to efficient static security analysis. This paper proposes a parallel algorithm for static CA that uses both central processing units (CPUs) and graphical processing units (GPUs). To enhance the accuracy, AC load flow is used, and parallel computation of load flow is done simultaneously, with efficient screening and ranking of the critical contingencies. We perform extensive experiments to evaluate the efficacy of the proposed algorithm. As a result, we establish that the proposed parallel algorithm with high-performance computing (HPC) computing is much faster than the traditional algorithms. Furthermore, the HPC experiments were conducted using the national supercomputing facility, which demonstrates the proposed algorithm in the context of N−1 and N−2 static CA with immense power systems, such as the Indian northern regional power grid (NRPG) 246-bus and the polish 2383-bus networks.
Keywords	contingency analysis ; high-performance computing ; large power systems ; n−1 contingency ; n−2 contingency ; security assessment ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	006
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	Taylor & Francis Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: AI and Core Electoral Processes

P, Deepak / Simoes, Stanley / MacCarthaigh, Muiris

Mapping the Horizons

2023

Abstract: Significant enthusiasm around AI uptake has been witnessed across societies globally. The electoral process -- the time, place and manner of elections within democratic nations -- has been among those very rare sectors in which AI has not penetrated much. ...

Abstract	Significant enthusiasm around AI uptake has been witnessed across societies globally. The electoral process -- the time, place and manner of elections within democratic nations -- has been among those very rare sectors in which AI has not penetrated much. Electoral management bodies in many countries have recently started exploring and deliberating over the use of AI in the electoral process. In this paper, we consider five representative avenues within the core electoral process which have potential for AI usage, and map the challenges involved in using AI within them. These five avenues are: voter list maintenance, determining polling booth locations, polling booth protection processes, voter authentication and video monitoring of elections. Within each of these avenues, we lay down the context, illustrate current or potential usage of AI, and discuss extant or potential ramifications of AI usage, and potential directions for mitigating risks while considering AI usage. We believe that the scant current usage of AI within electoral processes provides a very rare opportunity, that of being able to deliberate on the risks and mitigation possibilities, prior to real and widespread AI deployment. This paper is an attempt to map the horizons of risks and opportunities in using AI within the electoral processes and to help shape the debate around the topic. Comment: 19 pages, 7 figures, to be published in AI Magazine (Fall 2023)
Keywords	Computer Science - Computers and Society ; Computer Science - Machine Learning
Publishing date	2023-02-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: FiSH

P, Deepak / Sundaram, Sowmya S

Fair Spatial Hotspots

2021

Abstract	Pervasiveness of tracking devices and enhanced availability of spatially located data has deepened interest in using them for various policy interventions, through computational data analysis tasks such as spatial hot spot detection. In this paper, we consider, for the first time to our best knowledge, fairness in detecting spatial hot spots. We motivate the need for ensuring fairness through statistical parity over the collective population covered across chosen hot spots. We then characterize the task of identifying a diverse set of solutions in the noteworthiness-fairness trade-off spectrum, to empower the user to choose a trade-off justified by the policy domain. Being a novel task formulation, we also develop a suite of evaluation metrics for fair hot spots, motivated by the need to evaluate pertinent aspects of the task. We illustrate the computational infeasibility of identifying fair hot spots using naive and/or direct approaches and devise a method, codenamed {\it FiSH}, for efficiently identifying high-quality, fair and diverse sets of spatial hot spots. FiSH traverses the tree-structured search space using heuristics that guide it towards identifying effective and fair sets of spatial hot spots. Through an extensive empirical analysis over a real-world dataset from the domain of human development, we illustrate that FiSH generates high-quality solutions at fast response times.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Subject code	004
Publishing date	2021-06-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Cluster-level Group Representativity Fairness in $k$-means Clustering

Simoes, Stanley / P, Deepak / MacCarthaigh, Muiris

2022

Abstract: There has been much interest recently in developing fair clustering algorithms that seek to do justice to the representation of groups defined along sensitive attributes such as race and gender. We observe that clustering algorithms could generate ... ...

Abstract	There has been much interest recently in developing fair clustering algorithms that seek to do justice to the representation of groups defined along sensitive attributes such as race and gender. We observe that clustering algorithms could generate clusters such that different groups are disadvantaged within different clusters. We develop a clustering algorithm, building upon the centroid clustering paradigm pioneered by classical algorithms such as $k$-means, where we focus on mitigating the unfairness experienced by the most-disadvantaged group within each cluster. Our method uses an iterative optimisation paradigm whereby an initial cluster assignment is modified by reassigning objects to clusters such that the worst-off sensitive group within each cluster is benefitted. We demonstrate the effectiveness of our method through extensive empirical evaluations over a novel evaluation metric on real-world datasets. Specifically, we show that our method is effective in enhancing cluster-level group representativity fairness significantly at low impact on cluster coherence.
Keywords	Computer Science - Machine Learning ; Computer Science - Computers and Society
Subject code	006
Publishing date	2022-12-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Exploring Rawlsian Fairness for K-Means Clustering

Simoes, Stanley / P, Deepak / MacCarthaigh, Muiris

2022

Abstract: We conduct an exploratory study that looks at incorporating John Rawls' ideas on fairness into existing unsupervised machine learning algorithms. Our focus is on the task of clustering, specifically the k-means clustering algorithm. To the best of our ... ...

Abstract	We conduct an exploratory study that looks at incorporating John Rawls' ideas on fairness into existing unsupervised machine learning algorithms. Our focus is on the task of clustering, specifically the k-means clustering algorithm. To the best of our knowledge, this is the first work that uses Rawlsian ideas in clustering. Towards this, we attempt to develop a postprocessing technique i.e., one that operates on the cluster assignment generated by the standard k-means clustering algorithm. Our technique perturbs this assignment over a number of iterations to make it fairer according to Rawls' difference principle while minimally affecting the overall utility. As the first step, we consider two simple perturbation operators -- $\mathbf{R_1}$ and $\mathbf{R_2}$ -- that reassign examples in a given cluster assignment to new clusters; $\mathbf{R_1}$ assigning a single example to a new cluster, and $\mathbf{R_2}$ a pair of examples to new clusters. Our experiments on a sample of the Adult dataset demonstrate that both operators make meaningful perturbations in the cluster assignment towards incorporating Rawls' difference principle, with $\mathbf{R_2}$ being more efficient than $\mathbf{R_1}$ in terms of the number of iterations. However, we observe that there is still a need to design operators that make significantly better perturbations. Nevertheless, both operators provide good baselines for designing and comparing any future operator, and we hope our findings would aid future work in this direction. Comment: Accepted at ICDSE 2021
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2022-05-04
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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