Detection and Mitigation of Algorithmic Bias via Predictive Rate Parity

Recently, numerous studies have demonstrated the presence of bias in machine learning powered decision-making systems. Although most definitions of algorithmic bias have solid mathematical foundations, the corresponding bias detection techniques often lack statistical rigor, especially for non-iid data. We fill this gap in the literature by presenting a rigorous non-parametric testing procedure for bias according to Predictive Rate Parity, a commonly considered notion of algorithmic bias. We adapt traditional asymptotic results for non-parametric estimators to test for bias in the presence of dependence commonly seen in user-level data generated by technology industry applications and illustrate how these approaches can be leveraged for mitigation. We further propose modifications of this methodology to address bias measured through marginal outcome disparities in classification settings and extend notions of predictive rate parity to multi-objective models. Experimental results on real data show the efficacy of the proposed detection and mitigation methods

Condition monitoring of helical gears using automated selection of features and sensors

The selection of most sensitive sensors and signal processing methods is essential process for the design of condition monitoring and intelligent fault diagnosis and prognostic systems. Normally, sensory data includes high level of noise and irrelevant or red undant information which makes the selection of the most sensitive sensor and signal processing method a difficult task. This paper introduces a new application of the Automated Sensor and Signal Processing Approach (ASPS), for the design of condition monitoring systems for developing an effective monitoring system for gearbox fault diagnosis. The approach is based on using Taguchi's orthogonal arrays, combined with automated selection of sensory characteristic features, to provide economically effective and optimal selection of sensors and signal processing methods with reduced experimental work. Multi-sensory signals such as acoustic emission, vibration, speed and torque are collected from the gearbox test rig under different health and operating conditions. Time and frequency domain signal processing methods are utilised to assess the suggested approach. The experiments investigate a single stage gearbox system with three level of damage in a helical gear to evaluate the proposed approach. Two different classification models are employed using neural networks to evaluate the methodology. The results have shown that the suggested approach can be applied to the design of condition monitoring systems of gearbox monitoring without the need for implementing pattern recognition tools during the design phase; where the pattern recognition can be implemented as part of decision making for diagnostics. The suggested system has a wide range of applications including industrial machinery as well as wind turbines for renewable energy applications

Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy Applications in Public Cloud

Multi-tenancy in public clouds may lead to co-location interference on shared resources, which possibly results in performance degradation of cloud applications. Cloud providers want to know when such events happen and how serious the degradation is, to perform interference-aware migrations and alleviate the problem. However, virtual machines (VM) in Infrastructure-as-a-Service public clouds are black-boxes to providers, where application-level performance information cannot be acquired. This makes performance monitoring intensely challenging as cloud providers can only rely on low-level metrics such as CPU usage and hardware counters. We propose a novel machine learning framework, Alioth, to monitor the performance degradation of cloud applications. To feed the data-hungry models, we first elaborate interference generators and conduct comprehensive co-location experiments on a testbed to build Alioth-dataset which reflects the complexity and dynamicity in real-world scenarios. Then we construct Alioth by (1) augmenting features via recovering low-level metrics under no interference using denoising auto-encoders, (2) devising a transfer learning model based on domain adaptation neural network to make models generalize on test cases unseen in offline training, and (3) developing a SHAP explainer to automate feature selection and enhance model interpretability. Experiments show that Alioth achieves an average mean absolute error of 5.29% offline and 10.8% when testing on applications unseen in the training stage, outperforming the baseline methods. Alioth is also robust in signaling quality-of-service violation under dynamicity. Finally, we demonstrate a possible application of Alioth's interpretability, providing insights to benefit the decision-making of cloud operators. The dataset and code of Alioth have been released on GitHub.Comment: Accepted by 2023 IEEE International Parallel & Distributed Processing Symposium (IPDPS

A graph theoretical perspective for the unsupervised clustering of free text corpora

This thesis introduces a robust end to end topic discovery framework that extracts a set of coherent topics stemming intrinsically from document similarities. Some topic clustering methods can support embedded vectors instead of traditional Bag-of-Words (BoW) representation. Some can be free from the number of topics hyperparameter and some others can extract a multi-scale relation between topics. However, no topic clustering method supports all these properties together. This thesis focuses on this gap in the literature by designing a framework that supports any type of document-level features especially the embedded vectors. This framework does not require any uninformed decision making about the underlying data such as the number of topics, instead, the framework extracts topics in multiple resolutions. To achieve this goal, we combine existing methods from natural language processing (NLP) for feature generation and graph theory, first for graph construction based on semantic document similarities, then for graph partitioning to extract corresponding topics in multiple resolutions. Finally, we use specific methods from statistical machine learning to obtain highly generalisable supervised models to deploy topic classifiers for the deployment of topic extraction in real-time. Our applications on both a noisy and specialised corpus of medical records (i.e., descriptions for patient incidents within the NHS) and public news articles in daily language show that our framework extracts coherent topics that have better quantitative benchmark scores than other methods in most cases. The resulting multi-scale topics in both applications enable us to capture specific details more easily and choose the relevant resolutions for the specific objective. This study contributes to topic clustering literature by introducing a novel graph theoretical perspective that provides a combination of new properties. These properties are multiple resolutions, independence from uninformed decisions about the corpus, and usage of recent NLP features, such as vector embeddings.Open Acces

Quantifying the Sustainability of Products and Suppliers in Food Distribution Companies

[EN] Supplier evaluation is a relevant task of supply chain management where multicriteria methods make great contributions to manufacturing industries. This is not the case in food distribution companies, which have a key role in providing safe and affordable food to society. The purpose of this research is to measure the sustainability of products and suppliers in food distribution companies through a multiple criteria approach. Firstly, the system proposed provides indicators to qualify products and assess the food quality, using the compensatory Multi-Attribute Utility Theory (MAUT) model. Secondly, these indicators are included in supplier evaluation, which takes economic, environmental, and social criteria into account. MAUT and Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE), a non-compensatory method, are used for supplier evaluation. This approach has been validated for fresh food in a supermarket chain, mainly using historical data. Partial indicators, such as food safety scores, together with global indicators of suppliers, inform the most appropriate decisions and the most appropriate relations between companies and providers. Poor performance in food safety can lead to the disqualification of some suppliers. MAUT is good for qualifying products and is easy to apply at the operational level in logistic platforms, while PROMETHEE is more suitable for supplier segmentation, as it helps to identify supplier strengths and weaknesses.This research was funded by the Regional Ministry of Education, Research, Culture and Sport of the Autonomous Government of the Valencian Region, Spain, grant number AICO/2017/066.Segura Maroto, M.; Maroto Álvarez, MC.; Segura García Del Río, B. (2019). Quantifying the Sustainability of Products and Suppliers in Food Distribution Companies. Sustainability. 11(21):1-18. https://doi.org/10.3390/su11215875S1181121Thies, C., Kieckhäfer, K., Spengler, T. S., & Sodhi, M. S. (2019). Operations research for sustainability assessment of products: A review. European Journal of Operational Research, 274(1), 1-21. doi:10.1016/j.ejor.2018.04.039Diaz-Balteiro, L., González-Pachón, J., & Romero, C. (2017). Measuring systems sustainability with multi-criteria methods: A critical review. European Journal of Operational Research, 258(2), 607-616. doi:10.1016/j.ejor.2016.08.075Zimmer, K., Fröhling, M., & Schultmann, F. (2015). Sustainable supplier management – a review of models supporting sustainable supplier selection, monitoring and development. International Journal of Production Research, 54(5), 1412-1442. doi:10.1080/00207543.2015.1079340Chai, J., & Ngai, E. W. T. (2020). Decision-making techniques in supplier selection: Recent accomplishments and what lies ahead. Expert Systems with Applications, 140, 112903. doi:10.1016/j.eswa.2019.112903Chai, J., Liu, J. N. K., & Ngai, E. W. T. (2013). Application of decision-making techniques in supplier selection: A systematic review of literature. Expert Systems with Applications, 40(10), 3872-3885. doi:10.1016/j.eswa.2012.12.040Govindan, K., Rajendran, S., Sarkis, J., & Murugesan, P. (2015). Multi criteria decision making approaches for green supplier evaluation and selection: a literature review. Journal of Cleaner Production, 98, 66-83. doi:10.1016/j.jclepro.2013.06.046Ansari, Z. N., & Kant, R. (2017). A state-of-art literature review reflecting 15 years of focus on sustainable supply chain management. Journal of Cleaner Production, 142, 2524-2543. doi:10.1016/j.jclepro.2016.11.023Ho, W., Xu, X., & Dey, P. K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202(1), 16-24. doi:10.1016/j.ejor.2009.05.009Rajeev, A., Pati, R. K., Padhi, S. S., & Govindan, K. (2017). Evolution of sustainability in supply chain management: A literature review. Journal of Cleaner Production, 162, 299-314. doi:10.1016/j.jclepro.2017.05.026Demir, L., Akpınar, M. E., Araz, C., & Ilgın, M. A. (2018). A green supplier evaluation system based on a new multi-criteria sorting method: VIKORSORT. Expert Systems with Applications, 114, 479-487. doi:10.1016/j.eswa.2018.07.071Dweiri, F., Kumar, S., Khan, S. A., & Jain, V. (2016). Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Systems with Applications, 62, 273-283. doi:10.1016/j.eswa.2016.06.030Chang, L., Ouzrout, Y., Nongaillard, A., Bouras, A., & Jiliu, Z. (2014). Multi-criteria decision making based on trust and reputation in supply chain. International Journal of Production Economics, 147, 362-372. doi:10.1016/j.ijpe.2013.04.014Ekici, A. (2013). An improved model for supplier selection under capacity constraint and multiple criteria. International Journal of Production Economics, 141(2), 574-581. doi:10.1016/j.ijpe.2012.09.013Lin, R.-H. (2012). An integrated model for supplier selection under a fuzzy situation. International Journal of Production Economics, 138(1), 55-61. doi:10.1016/j.ijpe.2012.02.024Amid, A., Ghodsypour, S. H., & O’Brien, C. (2011). A weighted max–min model for fuzzy multi-objective supplier selection in a supply chain. International Journal of Production Economics, 131(1), 139-145. doi:10.1016/j.ijpe.2010.04.044Chen, Y.-J. (2011). Structured methodology for supplier selection and evaluation in a supply chain. Information Sciences, 181(9), 1651-1670. doi:10.1016/j.ins.2010.07.026Zeydan, M., Çolpan, C., & Çobanoğlu, C. (2011). A combined methodology for supplier selection and performance evaluation. Expert Systems with Applications, 38(3), 2741-2751. doi:10.1016/j.eswa.2010.08.064Şen, C. G., Baraçlı, H., Şen, S., & Başlıgil, H. (2009). An integrated decision support system dealing with qualitative and quantitative objectives for enterprise software selection. Expert Systems with Applications, 36(3), 5272-5283. doi:10.1016/j.eswa.2008.06.070Bottani, E., & Rizzi, A. (2008). An adapted multi-criteria approach to suppliers and products selection—An application oriented to lead-time reduction. International Journal of Production Economics, 111(2), 763-781. doi:10.1016/j.ijpe.2007.03.012Govindan, K., Kadziński, M., & Sivakumar, R. (2017). Application of a novel PROMETHEE-based method for construction of a group compromise ranking to prioritization of green suppliers in food supply chain. Omega, 71, 129-145. doi:10.1016/j.omega.2016.10.004Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. doi:10.1016/j.omega.2014.11.009Rezaei, J., & Ortt, R. (2013). Multi-criteria supplier segmentation using a fuzzy preference relations based AHP. European Journal of Operational Research, 225(1), 75-84. doi:10.1016/j.ejor.2012.09.037Segura, M., & Maroto, C. (2017). A multiple criteria supplier segmentation using outranking and value function methods. Expert Systems with Applications, 69, 87-100. doi:10.1016/j.eswa.2016.10.031Bloemhof, J. M., & Soysal, M. (2016). Sustainable Food Supply Chain Design. Springer Series in Supply Chain Management, 395-412. doi:10.1007/978-3-319-29791-0_18Grimm, J. H., Hofstetter, J. S., & Sarkis, J. (2014). Critical factors for sub-supplier management: A sustainable food supply chains perspective. International Journal of Production Economics, 152, 159-173. doi:10.1016/j.ijpe.2013.12.011Lau, H., Nakandala, D., & Shum, P. K. (2018). A business process decision model for fresh-food supplier evaluation. Business Process Management Journal, 24(3), 716-744. doi:10.1108/bpmj-01-2016-0015Beske, P., Land, A., & Seuring, S. (2014). Sustainable supply chain management practices and dynamic capabilities in the food industry: A critical analysis of the literature. International Journal of Production Economics, 152, 131-143. doi:10.1016/j.ijpe.2013.12.026Schmitt, E., Galli, F., Menozzi, D., Maye, D., Touzard, J.-M., Marescotti, A., … Brunori, G. (2017). Comparing the sustainability of local and global food products in Europe. Journal of Cleaner Production, 165, 346-359. doi:10.1016/j.jclepro.2017.07.039Behzadian, M., Kazemzadeh, R. B., Albadvi, A., & Aghdasi, M. (2010). PROMETHEE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research, 200(1), 198-215. doi:10.1016/j.ejor.2009.01.021The PROMETHEE Bibliographical Databasehttp://www.promethee-gaia.net/bibliographical-database.htmlChen, Y.-H., Wang, T.-C., & Wu, C.-Y. (2011). Strategic decisions using the fuzzy PROMETHEE for IS outsourcing. Expert Systems with Applications, 38(10), 13216-13222. doi:10.1016/j.eswa.2011.04.137Araz, C., & Ozkarahan, I. (2007). Supplier evaluation and management system for strategic sourcing based on a new multicriteria sorting procedure. International Journal of Production Economics, 106(2), 585-606. doi:10.1016/j.ijpe.2006.08.008Dulmin, R., & Mininno, V. (2003). Supplier selection using a multi-criteria decision aid method. Journal of Purchasing and Supply Management, 9(4), 177-187. doi:10.1016/s1478-4092(03)00032-3Seuring, S. (2013). A review of modeling approaches for sustainable supply chain management. Decision Support Systems, 54(4), 1513-1520. doi:10.1016/j.dss.2012.05.053Brandenburg, M., Govindan, K., Sarkis, J., & Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233(2), 299-312. doi:10.1016/j.ejor.2013.09.032Xu, Z. (2000). On consistency of the weighted geometric mean complex judgement matrix in AHP. European Journal of Operational Research, 126(3), 683-687. doi:10.1016/s0377-2217(99)00082-xKonys. (2019). Green Supplier Selection Criteria: From a Literature Review to a Comprehensive Knowledge Base. Sustainability, 11(15), 4208. doi:10.3390/su11154208D-Sight CDMhttp://www.d-sight.com/solutions/d-sight-cd

The Integrated Behavioural Model for Water, Sanitation, and Hygiene: a systematic review of behavioural models and a framework for designing and evaluating behaviour change interventions in infrastructure-restricted settings.

BACKGROUND: Promotion and provision of low-cost technologies that enable improved water, sanitation, and hygiene (WASH) practices are seen as viable solutions for reducing high rates of morbidity and mortality due to enteric illnesses in low-income countries. A number of theoretical models, explanatory frameworks, and decision-making models have emerged which attempt to guide behaviour change interventions related to WASH. The design and evaluation of such interventions would benefit from a synthesis of this body of theory informing WASH behaviour change and maintenance. METHODS: We completed a systematic review of existing models and frameworks through a search of related articles available in PubMed and in the grey literature. Information on the organization of behavioural determinants was extracted from the references that fulfilled the selection criteria and synthesized. Results from this synthesis were combined with other relevant literature, and from feedback through concurrent formative and pilot research conducted in the context of two cluster-randomized trials on the efficacy of WASH behaviour change interventions to inform the development of a framework to guide the development and evaluation of WASH interventions: the Integrated Behavioural Model for Water, Sanitation, and Hygiene (IBM-WASH). RESULTS: We identified 15 WASH-specific theoretical models, behaviour change frameworks, or programmatic models, of which 9 addressed our review questions. Existing models under-represented the potential role of technology in influencing behavioural outcomes, focused on individual-level behavioural determinants, and had largely ignored the role of the physical and natural environment. IBM-WASH attempts to correct this by acknowledging three dimensions (Contextual Factors, Psychosocial Factors, and Technology Factors) that operate on five-levels (structural, community, household, individual, and habitual). CONCLUSIONS: A number of WASH-specific models and frameworks exist, yet with some limitations. The IBM-WASH model aims to provide both a conceptual and practical tool for improving our understanding and evaluation of the multi-level multi-dimensional factors that influence water, sanitation, and hygiene practices in infrastructure-constrained settings. We outline future applications of our proposed model as well as future research priorities needed to advance our understanding of the sustained adoption of water, sanitation, and hygiene technologies and practices

Development, test and comparison of two Multiple Criteria Decision Analysis(MCDA) models: A case of healthcare infrastructure location

When planning a new development, location decisions have always been a major issue. This paper examines and compares two modelling methods used to inform a healthcare infrastructure location decision. Two Multiple Criteria Decision Analysis (MCDA) models were developed to support the optimisation of this decision-making process, within a National Health Service (NHS) organisation, in the UK. The proposed model structure is based on seven criteria (environment and safety, size, total cost, accessibility, design, risks and population profile) and 28 sub-criteria. First, Evidential Reasoning (ER) was used to solve the model, then, the processes and results were compared with the Analytical Hierarchy Process (AHP). It was established that using ER or AHP led to the same solutions. However, the scores between the alternatives were significantly different; which impacted the stakeholders‟ decision-making. As the processes differ according to the model selected, ER or AHP, it is relevant to establish the practical and managerial implications for selecting one model or the other and providing evidence of which models best fit this specific environment. To achieve an optimum operational decision it is argued, in this study, that the most transparent and robust framework is achieved by merging ER process with the pair-wise comparison, an element of AHP. This paper makes a defined contribution by developing and examining the use of MCDA models, to rationalise new healthcare infrastructure location, with the proposed model to be used for future decision. Moreover, very few studies comparing different MCDA techniques were found, this study results enable practitioners to consider even further the modelling characteristics to ensure the development of a reliable framework, even if this means applying a hybrid approach

A two-step fusion process for multi-criteria decision applied to natural hazards in mountains

Mountain river torrents and snow avalanches generate human and material damages with dramatic consequences. Knowledge about natural phenomenona is often lacking and expertise is required for decision and risk management purposes using multi-disciplinary quantitative or qualitative approaches. Expertise is considered as a decision process based on imperfect information coming from more or less reliable and conflicting sources. A methodology mixing the Analytic Hierarchy Process (AHP), a multi-criteria aid-decision method, and information fusion using Belief Function Theory is described. Fuzzy Sets and Possibilities theories allow to transform quantitative and qualitative criteria into a common frame of discernment for decision in Dempster-Shafer Theory (DST ) and Dezert-Smarandache Theory (DSmT) contexts. Main issues consist in basic belief assignments elicitation, conflict identification and management, fusion rule choices, results validation but also in specific needs to make a difference between importance and reliability and uncertainty in the fusion process