Relevant bibliographies by topics / Bias mitigation

Academic literature on the topic 'Bias mitigation'

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Published: 8 June 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Bias mitigation":

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Erkmen, Cherie Parungo, Lauren Kane, and David T. Cooke. "Bias Mitigation in Cardiothoracic Recruitment." Annals of Thoracic Surgery 111, no. 1 (January 2021): 12–15. http://dx.doi.org/10.1016/j.athoracsur.2020.07.005.

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Vejsbjerg, Inge, Elizabeth M. Daly, Rahul Nair, and Svetoslav Nizhnichenkov. "Interactive Human-Centric Bias Mitigation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 23838–40. http://dx.doi.org/10.1609/aaai.v38i21.30582.

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Bias mitigation algorithms differ in their definition of bias and how they go about achieving that objective. Bias mitigation algorithms impact different cohorts differently and allowing end users and data scientists to understand the impact of these differences in order to make informed choices is a relatively unexplored domain. This demonstration presents an interactive bias mitigation pipeline that allows users to understand the cohorts impacted by their algorithm choice and provide feedback in order to provide a bias mitigated pipeline that most aligns with their goals.
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Djebrouni, Yasmine, Nawel Benarba, Ousmane Touat, Pasquale De Rosa, Sara Bouchenak, Angela Bonifati, Pascal Felber, Vania Marangozova, and Valerio Schiavoni. "Bias Mitigation in Federated Learning for Edge Computing." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 4 (December 19, 2023): 1–35. http://dx.doi.org/10.1145/3631455.

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Federated learning (FL) is a distributed machine learning paradigm that enables data owners to collaborate on training models while preserving data privacy. As FL effectively leverages decentralized and sensitive data sources, it is increasingly used in ubiquitous computing including remote healthcare, activity recognition, and mobile applications. However, FL raises ethical and social concerns as it may introduce bias with regard to sensitive attributes such as race, gender, and location. Mitigating FL bias is thus a major research challenge. In this paper, we propose Astral, a novel bias mitigation system for FL. Astral provides a novel model aggregation approach to select the most effective aggregation weights to combine FL clients' models. It guarantees a predefined fairness objective by constraining bias below a given threshold while keeping model accuracy as high as possible. Astral handles the bias of single and multiple sensitive attributes and supports all bias metrics. Our comprehensive evaluation on seven real-world datasets with three popular bias metrics shows that Astral outperforms state-of-the-art FL bias mitigation techniques in terms of bias mitigation and model accuracy. Moreover, we show that Astral is robust against data heterogeneity and scalable in terms of data size and number of FL clients. Astral's code base is publicly available.
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Gallaher, Joshua P., Alexander J. Kamrud, and Brett J. Borghetti. "Detection and Mitigation of Inefficient Visual Searching." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 47–51. http://dx.doi.org/10.1177/1071181320641015.

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A commonly known cognitive bias is a confirmation bias: the overweighting of evidence supporting a hy- pothesis and underweighting evidence countering that hypothesis. Due to high-stress and fast-paced opera- tions, military decisions can be affected by confirmation bias. One military decision task prone to confirma- tion bias is a visual search. During a visual search, the operator scans an environment to locate a specific target. If confirmation bias causes the operator to scan the wrong portion of the environment first, the search is inefficient. This study has two primary goals: 1) detect inefficient visual search using machine learning and Electroencephalography (EEG) signals, and 2) apply various mitigation techniques in an effort to im- prove the efficiency of searches. Early findings are presented showing how machine learning models can use EEG signals to detect when a person might be performing an inefficient visual search. Four mitigation techniques were evaluated: a nudge which indirectly slows search speed, a hint on how to search efficiently, an explanation for why the participant was receiving a nudge, and instructions to instruct the participant to search efficiently. These mitigation techniques are evaluated, revealing the most effective mitigations found to be the nudge and hint techniques.
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Rahmawati, Fitriana, and Fitri Santi. "A Literature Review on the Influence of Availability Bias and Overconfidence Bias on Investor Decisions." East Asian Journal of Multidisciplinary Research 2, no. 12 (December 30, 2023): 4961–76. http://dx.doi.org/10.55927/eajmr.v2i12.6896.

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This research examines the impact of Availability Bias and Overconfidence Bias on investment decisions. Utilizing a literature review approach and VOSviewer analysis, this study explores how these biases affect investor decision-making processes and potential mitigation strategies. The objective is to highlight the significance of understanding and mitigating these biases in achieving more rational investment decisions. The findings underscore the potential negative effects of both biases, leading to overconfident and less rational investment decisions. Awareness of their interplay is crucial, as they reinforce each other's negative effects on investment decision-making. Overcoming these cognitive biases is essential for more effective investment decision-making. This research contributes insights into mitigating biases, aiding in a more balanced and rational approach to investment decision-making.
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Singh, Richa, Puspita Majumdar, Surbhi Mittal, and Mayank Vatsa. "Anatomizing Bias in Facial Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12351–58. http://dx.doi.org/10.1609/aaai.v36i11.21500.

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Existing facial analysis systems have been shown to yield biased results against certain demographic subgroups. Due to its impact on society, it has become imperative to ensure that these systems do not discriminate based on gender, identity, or skin tone of individuals. This has led to research in the identification and mitigation of bias in AI systems. In this paper, we encapsulate bias detection/estimation and mitigation algorithms for facial analysis. Our main contributions include a systematic review of algorithms proposed for understanding bias, along with a taxonomy and extensive overview of existing bias mitigation algorithms. We also discuss open challenges in the field of biased facial analysis.
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Lee, Yu-Hao, Norah E. Dunbar, Claude H. Miller, Brianna L. Lane, Matthew L. Jensen, Elena Bessarabova, Judee K. Burgoon, et al. "Training Anchoring and Representativeness Bias Mitigation Through a Digital Game." Simulation & Gaming 47, no. 6 (August 20, 2016): 751–79. http://dx.doi.org/10.1177/1046878116662955.

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Objective. Humans systematically make poor decisions because of cognitive biases. Can digital games train people to avoid cognitive biases? The goal of this study is to investigate the affordance of different educational media in training people about cognitive biases and to mitigate cognitive biases within their decision-making processes. Method. A between-subject experiment was conducted to compare a digital game, a traditional slideshow, and a combined condition in mitigating two types of cognitive biases: anchoring bias and representativeness bias. We measured both immediate effects and delayed effects after four weeks. Results. The digital game and slideshow conditions were effective in mitigating cognitive biases immediately after the training, but the effects decayed after four weeks. By providing the basic knowledge through the slideshow, then allowing learners to practice bias-mitigation techniques in the digital game, the combined condition was most effective at mitigating the cognitive biases both immediately and after four weeks.
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Patil, Pranita, and Kevin Purcell. "Decorrelation-Based Deep Learning for Bias Mitigation." Future Internet 14, no. 4 (March 29, 2022): 110. http://dx.doi.org/10.3390/fi14040110.

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Although deep learning has proven to be tremendously successful, the main issue is the dependency of its performance on the quality and quantity of training datasets. Since the quality of data can be affected by biases, a novel deep learning method based on decorrelation is presented in this study. The decorrelation specifically learns bias invariant features by reducing the non-linear statistical dependency between features and bias itself. This makes the deep learning models less prone to biased decisions by addressing data bias issues. We introduce Decorrelated Deep Neural Networks (DcDNN) or Decorrelated Convolutional Neural Networks (DcCNN) and Decorrelated Artificial Neural Networks (DcANN) by applying decorrelation-based optimization to Deep Neural Networks (DNN) and Artificial Neural Networks (ANN), respectively. Previous bias mitigation methods result in a drastic loss in accuracy at the cost of bias reduction. Our study aims to resolve this by controlling how strongly the decorrelation function for bias reduction and loss function for accuracy affect the network objective function. The detailed analysis of the hyperparameter shows that for the optimal value of hyperparameter, our model is capable of maintaining accuracy while being bias invariant. The proposed method is evaluated on several benchmark datasets with different types of biases such as age, gender, and color. Additionally, we test our approach along with traditional approaches to analyze the bias mitigation in deep learning. Using simulated datasets, the results of t-distributed stochastic neighbor embedding (t-SNE) of the proposed model validated the effective removal of bias. An analysis of fairness metrics and accuracy comparisons shows that using our proposed models reduces the biases without compromising accuracy significantly. Furthermore, the comparison of our method with existing methods shows the superior performance of our model in terms of bias mitigation, as well as simplicity of training.
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Kim, Hyo-eun. "Fairness Criteria and Mitigation of AI Bias." Korean Journal of Psychology: General 40, no. 4 (December 25, 2021): 459–85. http://dx.doi.org/10.22257/kjp.2021.12.40.4.459.

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Park, Souneil, Seungwoo Kang, Sangyoung Chung, and Junehwa Song. "A Computational Framework for Media Bias Mitigation." ACM Transactions on Interactive Intelligent Systems 2, no. 2 (June 2012): 1–32. http://dx.doi.org/10.1145/2209310.2209311.

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You might also be interested in the extended bibliographies on the topic 'Bias mitigation' for particular source types:
Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Bias mitigation":

1

Gadala, M. "Automation bias : exploring causal mechanisms and potential mitigation strategies." Thesis, City, University of London, 2017. http://openaccess.city.ac.uk/17889/.

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Automated decision support tools are designed to aid users and improve their performance in certain tasks by providing advice in the form of prompts, alarms, assessments, or recommendations. However, recent evidence suggests that sometimes use of such tools introduces decision errors that are not made without the tool. We refer to this phenomenon as “automation bias” (AB), resulting in a broader definition of this term than used by many authors. Sometimes, such automation-induced errors can even result in overall performance (in terms of correct decisions) which is actually worse with the tool than without it. Our literature review reveals an emphasis on mediators affecting automation bias and some mitigation strategies aimed at reducing it. However, there is a lack of research on the cognitive causal explanations for automation bias and on adaptive mitigation strategies that result in tools that adapt to the needs and characteristics of individual users. This thesis aims to address some of these gaps in the literature and focuses on systems consisting of a human and an automated tool which does not replace, but instead supports the human towards making a decision, with the overall responsibility lying with the human user. The overall goal of this thesis is to help reduce the rate of automation bias through a better understanding of its causes and the proposal of innovative, adaptive mitigation strategies. To achieve this, we begin with an extensive literature review on automation bias including examples, mediators, explanations, and mitigations while identifying areas for further research. This review is followed by the presentation of three experiments aimed at reducing the rate of AB in different ways: (1) an experiment to explore causal mechanisms of automation bias, the effect of the mere presence of tool advice before its presentation and the effect of the sequence of tool advice in a glaucoma risk calculator environment, (2) simulations that apply concepts of diversity to human + human systems to improve system performance in a breast cancer double reading programme, and (3) an experiment to study the possibility of improving system performance by tailoring tool setting (sensitivity / specificity combination) for groups of similarly skilled users and cases of similar difficulty level using a spellchecking tool. Results from the glaucoma experiment provide evidence of the effect of the presence of tool advice on user decisions - even before its presentation, as well as evidence of a newly introduced cognitive mechanism (users’ strategic change in decision threshold) which may account for some automation bias errors previously observed but unexplained in the literature. Results from the double reading experiment provide evidence of the benefits of diversity in improving system performance. Finally, results from the spell checker experiment provide evidence that groups of similarly skilled users perform better at different tool settings, that the same group of users perform better using a different tool setting in difficult versus easy tasks, and that use of simple models of user behaviour may allow the prediction, among a subset of tool settings for a certain tool, the tool setting that would be most appropriate for each user ability group and class of case difficulty.
2

Salomon, Sophie. "Bias Mitigation Techniques and a Cost-Aware Framework for Boosted Ranking Algorithms." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1586450345426827.

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Frick, Eric Christopher. "Mitigation of magnetic interference and compensation of bias drift in inertial sensors." Thesis, University of Iowa, 2015. https://ir.uiowa.edu/etd/5472.

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Magnetic interference in the motion capture environment is caused primarily by ferromagnetic objects and current-carrying devices disturbing the ambient, geomagnetic field. Inertial sensors gather magnetic data to determine and stabilize their global heading estimates, and such magnetic field disturbances alter heading estimates. This decreases orientation accuracy and therefore decreases motion capture accuracy. The often used Kalman Filter approach deals with magnetic interference by ignoring the magnetic data during periods interference is encountered, but this method is only effective when the disturbances are ephemeral, and cannot not retroactively repair data from disturbed time periods. The objective of this research is to develop a method of magnetic interference mitigation for environments where magnetic interference is the norm rather than the exception. To the knowledge of this author, the ability to use inertial and magnetic sensors to capture accurate, global, and drift-free orientation data in magnetically disturbed areas has yet to be developed. Furthermore there are no methods known to this author that are able to use data from undisturbed time periods to retroactively repair data from disturbed time periods. The investigation begins by exploring the use of magnetic shielding, with the reasoning that application of shielding so as to impede disturbed fields from affecting the inertial sensors would increase orientation accuracy. It was concluded that while shielding can mitigate the effect of magnetic interference, its application requires a tedious trial and error testing that was not guaranteed to improve results. Furthermore, shielding works by redirecting magnetic field lines, increasing field complexity, and thus has a high potential to exacerbate magnetic interference. Shielding was determined to be an impractical approach, and development of a magnetic inference mitigation algorithm began. The algorithm was constructed such that magnetic data would be filtered before inclusion in the orientation estimate, with the result that exposure in an undisturbed environment would improve estimation, but exposure to a disturbed environment would have no effect. The algorithm was designed for post-processing, rather than real-time use as Kalman Filters are, which enabled magnetic data gathered before and after a time point could affect estimation. The algorithm was evaluated by comparing it with the Kalman Filter approach of the company XSENS, using the gold standard of optical motion capture as the reference point. Under the tested conditions of stationary periods and smooth planar motion, the developed algorithm was resistant to magnetic interference for the duration of testing, while the Kalman Filter began to degrade after approximately 15 seconds. In a 190 second test, of which 180 were spent in a disturbed environment, the developed algorithm resulted in 0.4 degrees of absolute error, compared to the of the Kalman Filter’s 78.8 degrees. The developed algorithm shows the potential for inertial systems to be used effectively in situations of consistent magnetic interference. As the benefits of inertial motion capture make it a more attractive option than optical motion capture, immunity to magnetic interference significantly expands the usable range of motion capture environments. Such expansion would be beneficial for motion capture studies as a whole, allowing for the cheaper, more practical inertial approach to motion capture to supplant the more expensive and time consuming optimal option.
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Isumbingabo, Emma Francoise. "Evaluation and mitigation of the undesired effect of DC bias on inverter power transformer." Master's thesis, University of Cape Town, 2009. http://hdl.handle.net/11427/5202.

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Inverters have traditionally been used mostly in standalone systems (non-grid connected), Uninterruptible Power Supplies (UPS) and, more recently, in distributed generated systems (DGs). DG systems are based on grid connected inverters and are increasingly being connected to utility grids to convert renewable energy sources to the utility grids. Such sources are likely to have a significant impact in the future in meeting the electricity demands of industry and domestic consumption. Common DGs utilize DC power sources such as fuel cells, batteries, photovoltaic (solar) power, and wind power. Most of power supplies in domestic and industries are AC power consumers and, for this reason, the DC power has to be converted to meet the requirement. Two main causes of DC current in inverter power transformer are: 1) Non-linearity and asymmetry in its switching mechanism which may result in undesired DC current at its input. This DC current introduced into an inverter transformer results in the transformer's magnetic flux distortion and in some cases magnetic saturation. This, in turn, results in asymmetrical primary currents in the transformer (inverter side). This is due to the non linear characteristics of the transformer magnetic flux. 2) The same effects can be produced by the connection of asymmetrical loads (e.g. asymmetrical rectifier) to the inverter output. The result in both cases is an asymmetrical magnetic flux in the transformer. This is manifested as even and odd current harmonics as well as an increase in the reactive power requirement from the inverter. vi To remedy this situation, it is, therefore, necessary to incorporate into the inverter's control system a mechanism of cancelling the DC magnetic motive force (mmf) that causes the magnetic flux distortion. This Thesis presents a method of introducing a DC voltage component in the inverter's voltage output so as to inject the necessary DC current into the primary side of the inverter's transformer so as to cancel the total DC mmf that the transformer is subjected to ( supply and load side). This project consists of three main parts namely: Modeling, Simulation and Laboratory Experiment. Activities undertaken under Modeling and Simulation were as follows: Determining the effects of DC current on a power transformer. Investigating the likely occurrence of saturation of the power transformer incorporated in inverter systems. Mitigating the effects that can be caused by the presence of a DC component in the windings of a power transformer. After understanding the literature on the subject of interest, MATLAB SIMULINK and MATLAB m-files were used to simulate the behavior of the power transformer under three situations : The transformer under linear load. The transformer subjected to asymmetrical loading. The inverter system that has a power transformer on its output were designed in MATLAB and used to simulate the situation for each case. To validate the theory and simulation results, experimental work was carried out as follows: vii Investigation of the effects that DC (current) injection can have on a 6 kVA power transformer. Investigation of the performance of a 6 kVA power transformer under linear loading. Investigation of the performance of a 6 kVA power transformer under non-linear loads. Investigation of the likely occurrence of DC offset in inverter system. Mitigation of the effect of DC bias on power transformer using extra windings. Mitigation of the effects of DC offset in power inverter transformer by using the second harmonic content of the primary current as a feedback signal. Results obtained showed a successful implementation of the proposed method. However limitations of the controller performances were experienced and will require future work. It was concluded that a total removal of the undesired effects of DC bias is achievable and that total removal of DC offset in power inverter transformer is possible if the limitations of the controller are overcome.
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Wu, Lin. "Contribution to spatial bias mitigation in interferometric radiometers devoted to Earth observation : application to the SMOS mission." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/144655.

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This work has been undertaken within the frame of the FPI scholarship BES-2009-028505 of 30/07/2009, by the "Secretario de Estado de Investigación del Ministerio de Ciencia e Innovación", related to the project TEC2008-06764-C02-01 (Universitat Politècnica de Catalunya) titled "Advanced concepts on active and passive microwave remote sensing: technology and applications". In a more general scope, this thesis is related to the Remote Sensing Laboratory (Signal Theory & Communication Department, UPC) on-going activities, within the SMOS (Soil Moisture and Ocean Salinity) mission by the European Space Agency (ESA). These activities have been organized to provide original advances in the following four main topics: 1) SMOS system performance assessment. SMOS commissioning phase finalized in May 2010 providing preliminary performance results. Therefore, as a first step in this work, radiometric sensitivity and spatial bias (systematic spatial errors) have been reviewed and assessed. To achieve this, new techniques have been developed to better compare SMOS images to ground truth targets. SMOS ocean views have been selected as the best option since provide smooth brightness temperature distributions for which accurate models are available. 2) Spatial bias mitigation techniques. Spatial bias was a major issue in SMOS first images since it was larger than expected. In order to identify the dominant sources of spatial bias, each potential source of error, at calibration level,has been simulatedand compared tomeasured SMOS error patterns. 3) Spatial bias in the EAF-FoV. Whereas SMOS performance in the alias free FoV was well within mission requirements, this issue was especially important for the four polarimetric temperatures in the extended regions. This issuehas been addressed in two ways. First a new approach has been developed to systematically analyze spatial bias by splitting SMOS EAF-FoV into different regions of interest. Also, the pixels have been arranged per angle of incidence. Secondly, the impact in each region of calibration errors and different image reconstruction has been addressed. 4) Spatial bias in full-pol measurement.The main objective of this work has been achieved by developing, implementing and validating a SMOS full-pol G-matrix image reconstruction procedure that resulted into significantly improved polarimetric images. This tool allowed a comprehensive assessment of the two error contributions that have been identified as the main sources of residual spatial bias: the so-called floor error and antenna pattern uncertainty. Finally, as a validation of the quality of SMOS Level 1 fully polarimetric data that has been achieved, the last chapter of this work has been devoted to provide operational Faraday rotation retrievals in a per snap-shot basis.
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Le, Berre Guillaume. "Vers la mitigation des biais en traitement neuronal des langues." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0074.

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Il est de notoriété que les modèles d'apprentissage profond sont sensibles aux biais qui peuvent être présents dans les données utilisées pour l'apprentissage. Ces biais qui peuvent être définis comme de l'information inutile ou préjudiciable pour la tâche considérée, peuvent être de différentes natures: on peut par exemple trouver des biais dans les styles d'écriture utilisés, mais aussi des biais bien plus problématiques portant sur le sexe ou l'origine ethnique des individus. Ces biais peuvent provenir de différentes sources, comme des annotateurs ayant créé les bases de données, ou bien du processus d'annotation lui-même. Ma thèse a pour sujet l'étude de ces biais et, en particulier, s'organise autour de la mitigation des effets des biais sur l'apprentissage des modèles de Traitement Automatique des Langues (TAL). J'ai notamment beaucoup travaillé avec les modèles pré-entraînés comme BERT, RoBERTa ou UnifiedQA qui sont devenus incontournables ces dernières années dans tous les domaines du TAL et qui, malgré leur large pré-entraînement, sont très sensibles à ces problèmes de biais. Ma thèse s'organise en trois volets, chacun présentant une façon différente de gérer les biais présents dans les données. Le premier volet présente une méthode permettant d'utiliser les biais présents dans une base de données de résumé automatique afin d'augmenter la variabilité et la contrôlabilité des résumés générés. Puis, dans le deuxième volet, je m'intéresse à la génération automatique d'une base de données d'entraînement pour la tâche de question-réponse à choix multiples. L'intérêt d'une telle méthode de génération est qu'elle permet de ne pas faire appel à des annotateurs et donc d'éliminer les biais venant de ceux-ci dans les données. Finalement, je m'intéresse à l'entraînement d'un modèle multitâche pour la reconnaissance optique de texte. Je montre dans ce dernier volet qu'il est possible d'augmenter les performances de nos modèles en utilisant différents types de données (manuscrites et tapuscrites) lors de leur entraînement
It is well known that deep learning models are sensitive to biases that may be present in the data used for training. These biases, which can be defined as useless or detrimental information for the task in question, can be of different kinds: one can, for example, find biases in the writing styles used, but also much more problematic biases relating to the sex or ethnic origin of individuals. These biases can come from different sources, such as annotators who created the databases, or from the annotation process itself. My thesis deals with the study of these biases and, in particular, is organized around the mitigation of the effects of biases on the training of Natural Language Processing (NLP) models. In particular, I have worked a lot with pre-trained models such as BERT, RoBERTa or UnifiedQA which have become essential in recent years in all areas of NLP and which, despite their extensive pre-training, are very sensitive to these bias problems.My thesis is organized in three parts, each presenting a different way of managing the biases present in the data. The first part presents a method allowing to use the biases present in an automatic summary database in order to increase the variability and the controllability of the generated summaries. Then, in the second part, I am interested in the automatic generation of a training dataset for the multiple-choice question-answering task. The advantage of such a generation method is that it makes it possible not to call on annotators and therefore to eliminate the biases coming from them in the data. Finally, I am interested in training a multitasking model for optical text recognition. I show in this last part that it is possible to increase the performance of our models by using different types of data (handwritten and typed) during their training
7

Dougherty, John Paul. "Three Essays on the Economic Sustainability of Drought Insurance and Soil Investment for Smallholder Farmers in the Developing World." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531672015876609.

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Fyrvald, Johanna. "Mitigating algorithmic bias in Artificial Intelligence systems." Thesis, Uppsala universitet, Matematiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388627.

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Artificial Intelligence (AI) systems are increasingly used in society to make decisions that can have direct implications on human lives; credit risk assessments, employment decisions and criminal suspects predictions. As public attention has been drawn towards examples of discriminating and biased AI systems, concerns have been raised about the fairness of these systems. Face recognition systems, in particular, are often trained on non-diverse data sets where certain groups often are underrepresented in the data. The focus of this thesis is to provide insights regarding different aspects that are important to consider in order to mitigate algorithmic bias as well as to investigate the practical implications of bias in AI systems. To fulfil this objective, qualitative interviews with academics and practitioners with different roles in the field of AI and a quantitative online survey is conducted. A practical scenario covering face recognition and gender bias is also applied in order to understand how people reason about this issue in a practical context. The main conclusion of the study is that despite high levels of awareness and understanding about challenges and technical solutions, the academics and practitioners showed little or no awareness of legal aspects regarding bias in AI systems. The implication of this finding is that AI can be seen as a disruptive technology, where organizations tend to develop their own mitigation tools and frameworks as well as use their own moral judgement and understanding of the area instead of turning to legal authorities.
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Taylor, Stephen Luke. "Analyzing methods of mitigating initialization bias in transportation simulation models." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37208.

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All computer simulation models require some form of initialization before their outputs can be considered meaningful. Simulation models are typically initialized in a particular, often "empty" state and therefore must be "warmed-up" for an unknown amount of simulation time before reaching a "quasi-steady-state" representative of the systems' performance. The portion of the output series that is influenced by the arbitrary initialization is referred to as the initial transient and is a widely recognized problem in simulation analysis. Although several methods exist for removing the initial transient, there are no methods that perform well in all applications. This research evaluates the effectiveness of several techniques for reducing initialization bias from simulations using the commercial transportation simulation model VISSIM®. The three methods ultimately selected for evaluation are Welch's Method, the Marginal Standard Error Rule (MSER) and the Volume Balancing Method currently being used by the CORSIM model. Three model instances - a single intersection, a corridor, and a large network - were created to analyze the length of the initial transient for varying scenarios, under high and low demand scenarios. After presenting the results of each initialization method, advantages and criticisms of each are discussed as well as issues that arose during the implementation. The results for estimation of the extent of the initial transient are compared across each method and across the varying model sizes and volume levels. Based on the results of this study, Welch's Method is recommended based on is consistency and ease of implementation.
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Sweeney, Christopher(Christopher J. ). M. Eng Massachusetts Institute of Technology. "Understanding and mitigating unintended demographic bias in machine learning systems." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123131.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 81-84).
Machine Learning is becoming more and more influential in our society. Algorithms that learn from data are streamlining tasks in domains like employment, banking, education, heath care, social media, etc. Unfortunately, machine learning models are very susceptible to unintended bias, resulting in unfair and discriminatory algorithms with the power to adversely impact society. This unintended bias is usually subtle, emanating from many different sources and taking on many forms. This thesis will focus on understanding how unfair biases with respect to various demographic groups show up in machine learning systems. Furthermore, we develop multiple techniques to mitigate unintended demographic bias at various stages of typical machine learning pipelines. Using Natural Language Processing as a framework, we show substantial improvements in fairness for standard machine learning systems, when using our bias mitigation techniques.
by Christopher Sweeney.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Books on the topic "Bias mitigation":

1

Li, Junfeng. Jian huan qi hou bian hua: Yuan ze, mu biao, xing dong ji dui ce. 8th ed. Beijing Shi: Zhongguo ji hua chu ban she, 2011.

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Chen, Junwu. Zhongguo zhong chang qi tan jian pai zhan lüe mu biao yan jiu. 8th ed. Beijing Shi: Zhongguo shi hua chu ban she, 2012.

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Hu, Angang. Zhongguo ying dui quan qiu qi hou bian hua. 8th ed. Beijing: Qing hua da xue chu ban she, 2009.

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Shi, Xinfeng. Qi hou bian hua yu di tan jing ji. 8th ed. Beijing Shi: Zhongguo shui li shui dian chu ban she, 2010.

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Wu, Shaohong. Zhongguo zong he qi hou bian hua feng xian. 8th ed. Beijing: Ke xue chu ban she, 2011.

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Yang, Deping. Zhongguo di tan zheng ce xi tong gou jian yan jiu: Zhu ti, gong ju yu bian qian. 8th ed. Beijing Shi: Jing ji ke xue chu ban she, 2016.

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Zhenqing, Sun. Feng zhi mu biao xia Zhongguo di tan fa zhan lu jing xuan ze yan jiu: Yi Tianjin wei li. 8th ed. Beijing Shi: Ren min chu ban she, 2016.

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Xu, He. Qi hou bian hua xin shi jiao xia de Zhongguo zhan lüe huan jing ping jia: Integrating the Climate Change Issues into Strategic Environmental Assessment in China. 8th ed. Beijing: Ke xue chu ban she, 2013.

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Shi, Wenzhen. WTO, qi hou bian qian yu neng yuan. 8th ed. Taibei Shi: Yuan zhao chu ban you xian gong si, 2013.

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Li, Jianping. "Tan jin" shi dai: Quan qiu bian nuan, wo men ru he yu huo chong sheng. 8th ed. Beijing: Zhongguo huan jing ke xue chu ban she, 2010.

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Book chapters on the topic "Bias mitigation":

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Pat, Croskerry. "Cognitive Bias Mitigation: Becoming Better Diagnosticians." In Diagnosis, 257–87. Boca Raton : Taylor & Francis, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315116334-15.

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Tarallo, Mark. "Dancing with Myself: Self-Management and Bias Mitigation." In Modern Management and Leadership, 27–34. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003095620-6.

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Shi, Sheng, Shanshan Wei, Zhongchao Shi, Yangzhou Du, Wei Fan, Jianping Fan, Yolanda Conyers, and Feiyu Xu. "Algorithm Bias Detection and Mitigation in Lenovo Face Recognition Engine." In Natural Language Processing and Chinese Computing, 442–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60457-8_36.

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Wang, Xing, Guoqiang Zhao, Feng Zhang, and Yongan Yang. "Characterization and Mitigation of BeiDou Triple-Frequency Code Multipath Bias." In Lecture Notes in Electrical Engineering, 467–80. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0014-1_39.

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Parsons, Paul. "Promoting Representational Fluency for Cognitive Bias Mitigation in Information Visualization." In Cognitive Biases in Visualizations, 137–47. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95831-6_10.

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Ghadage, Adinath, Dewei Yi, George Coghill, and Wei Pang. "Multi-stage Bias Mitigation for Individual Fairness in Algorithmic Decisions." In Artificial Neural Networks in Pattern Recognition, 40–52. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20650-4_4.

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Vorontsov, Eugene, and Samuel Kadoury. "Label Noise in Segmentation Networks: Mitigation Must Deal with Bias." In Deep Generative Models, and Data Augmentation, Labelling, and Imperfections, 251–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88210-5_25.

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Sharma, Ashish, Rajeshwar Mehrotra, and Fiona Johnson. "A New Framework for Modeling Future Hydrologic Extremes: Nested Bias Correction as a Precursor to Stochastic Rainfall Downscaling." In Climate Change Modeling, Mitigation, and Adaptation, 357–86. Reston, VA: American Society of Civil Engineers, 2013. http://dx.doi.org/10.1061/9780784412718.ch13.

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Dost, Katharina, Hamish Duncanson, Ioannis Ziogas, Patricia Riddle, and Jörg Wicker. "Divide and Imitate: Multi-cluster Identification and Mitigation of Selection Bias." In Advances in Knowledge Discovery and Data Mining, 149–60. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05936-0_12.

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Saxena, Akrati, Harsh Saxena, and Ralucca Gera. "k-TruthScore: Fake News Mitigation in the Presence of Strong User Bias." In Computational Data and Social Networks, 113–26. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66046-8_10.

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Conference papers on the topic "Bias mitigation":

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Qraitem, Maan, Kate Saenko, and Bryan A. Plummer. "Bias Mimicking: A Simple Sampling Approach for Bias Mitigation." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.01945.

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Jeon, Eojin, Mingyu Lee, Juhyeong Park, Yeachan Kim, Wing-Lam Mok, and SangKeun Lee. "Improving Bias Mitigation through Bias Experts in Natural Language Understanding." In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.emnlp-main.681.

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Jiang, Jian, Viswonathan Manoranjan, Hanan Salam, and Oya Celiktutan. "Generalised Bias Mitigation for Personality Computing." In MM '23: The 31st ACM International Conference on Multimedia. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3607865.3616175.

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Calegari, Roberta, Gabriel G. Castañé, Michela Milano, and Barry O'Sullivan. "Assessing and Enforcing Fairness in the AI Lifecycle." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/735.

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A significant challenge in detecting and mitigating bias is creating a mindset amongst AI developers to address unfairness. The current literature on fairness is broad, and the learning curve to distinguish where to use existing metrics and techniques for bias detection or mitigation is difficult. This survey systematises the state-of-the-art about distinct notions of fairness and relative techniques for bias mitigation according to the AI lifecycle. Gaps and challenges identified during the development of this work are also discussed.
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Akl, Naeem, and Ahmed Tewfik. "Optimal information sequencing for cognitive bias mitigation." In 2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP). IEEE, 2014. http://dx.doi.org/10.1109/isccsp.2014.6877806.

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Heuss, Maria, Daniel Cohen, Masoud Mansoury, Maarten de Rijke, and Carsten Eickhoff. "Predictive Uncertainty-based Bias Mitigation in Ranking." In CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3583780.3615011.

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Dervişoğlu, Havvanur, and Mehmet Fatih Amasyali. "Racial Bias Mitigation with Federated Learning Approach." In 2023 8th International Conference on Computer Science and Engineering (UBMK). IEEE, 2023. http://dx.doi.org/10.1109/ubmk59864.2023.10286618.

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Kumar, Deepak, Oleg Lesota, George Zerveas, Daniel Cohen, Carsten Eickhoff, Markus Schedl, and Navid Rekabsaz. "Parameter-efficient Modularised Bias Mitigation via AdapterFusion." In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.eacl-main.201.

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Huang, Hui, Shuangzhi Wu, Kehai Chen, Hui Di, Muyun Yang, and Tiejun Zhao. "Improving Translation Quality Estimation with Bias Mitigation." In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-long.121.

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Patrikar, Ajay M., Arjuna Mahenthiran, and Ahmad Said. "Leveraging synthetic data for AI bias mitigation." In Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications, edited by Kimberly E. Manser, Raghuveer M. Rao, and Christopher L. Howell. SPIE, 2023. http://dx.doi.org/10.1117/12.2662276.

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Reports on the topic "Bias mitigation":

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Dolabella, Marcelo, and Mauricio Mesquita Moreira. Fighting Global Warming: Is Trade Policy in Latin America and the Caribbean a Help or a Hindrance? Inter-American Development Bank, August 2022. http://dx.doi.org/10.18235/0004426.

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The dire prospects of global warming have been increasing the pressure on policymakers to use trade policy as a mitigation tool, challenging trade economists canonical “targeting principle.” Even though the justifications for this stance remain as valid as ever, it no longer seems feasible in a world that is already engaging actively in using trade policy for climate purposes. However, the search for second-best solutions remains warranted. In this paper, we focus on the climate benefits of tariff reform for a broad sample of Latin American and Caribbean countries, drawing on Shapiros (2021) insights about the environmental bias of trade policy. Using a partial equilibrium approach and GTAP 10-MRIO data for 2014, we show that even though there is evidence of a negative bias toward “dirty goods” in half of the countries studied, translating this into actionable tariff reforms is plagued by interpretation and implementation difficulties, as well as by jurisdictional and efficiency trade-offs. There are also questions about their efficacy in curbing greenhouse gas emissions.
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Tipton, Kelley, Brian F. Leas, Emilia Flores, Christopher Jepson, Jaya Aysola, Jordana Cohen, Michael Harhay, et al. Impact of Healthcare Algorithms on Racial and Ethnic Disparities in Health and Healthcare. Agency for Healthcare Research and Quality (AHRQ), December 2023. http://dx.doi.org/10.23970/ahrqepccer268.

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Objectives. To examine the evidence on whether and how healthcare algorithms (including algorithm-informed decision tools) exacerbate, perpetuate, or reduce racial and ethnic disparities in access to healthcare, quality of care, and health outcomes, and examine strategies that mitigate racial and ethnic bias in the development and use of algorithms. Data sources. We searched published and grey literature for relevant studies published between January 2011 and February 2023. Based on expert guidance, we determined that earlier articles are unlikely to reflect current algorithms. We also hand-searched reference lists of relevant studies and reviewed suggestions from experts and stakeholders. Review methods. Searches identified 11,500 unique records. Using predefined criteria and dual review, we screened and selected studies to assess one or both Key Questions (KQs): (1) the effect of algorithms on racial and ethnic disparities in health and healthcare outcomes and (2) the effect of strategies or approaches to mitigate racial and ethnic bias in the development, validation, dissemination, and implementation of algorithms. Outcomes of interest included access to healthcare, quality of care, and health outcomes. We assessed studies’ methodologic risk of bias (ROB) using the ROBINS-I tool and piloted an appraisal supplement to assess racial and ethnic equity-related ROB. We completed a narrative synthesis and cataloged study characteristics and outcome data. We also examined four Contextual Questions (CQs) designed to explore the context and capture insights on practical aspects of potential algorithmic bias. CQ 1 examines the problem’s scope within healthcare. CQ 2 describes recently emerging standards and guidance on how racial and ethnic bias can be prevented or mitigated during algorithm development and deployment. CQ 3 explores stakeholder awareness and perspectives about the interaction of algorithms and racial and ethnic disparities in health and healthcare. We addressed these CQs through supplemental literature reviews and conversations with experts and key stakeholders. For CQ 4, we conducted an in-depth analysis of a sample of six algorithms that have not been widely evaluated before in the published literature to better understand how their design and implementation might contribute to disparities. Results. Fifty-eight studies met inclusion criteria, of which three were included for both KQs. One study was a randomized controlled trial, and all others used cohort, pre-post, or modeling approaches. The studies included numerous types of clinical assessments: need for intensive care or high-risk care management; measurement of kidney or lung function; suitability for kidney or lung transplant; risk of cardiovascular disease, stroke, lung cancer, prostate cancer, postpartum depression, or opioid misuse; and warfarin dosing. We found evidence suggesting that algorithms may: (a) reduce disparities (i.e., revised Kidney Allocation System, prostate cancer screening tools); (b) perpetuate or exacerbate disparities (e.g., estimated glomerular filtration rate [eGFR] for kidney function measurement, cardiovascular disease risk assessments); and/or (c) have no effect on racial or ethnic disparities. Algorithms for which mitigation strategies were identified are included in KQ 2. We identified six types of strategies often used to mitigate the potential of algorithms to contribute to disparities: removing an input variable; replacing a variable; adding one or more variables; changing or diversifying the racial and ethnic composition of the patient population used to train or validate a model; creating separate algorithms or thresholds for different populations; and modifying the statistical or analytic techniques used by an algorithm. Most mitigation efforts improved proximal outcomes (e.g., algorithmic calibration) for targeted populations, but it is more challenging to infer or extrapolate effects on longer term outcomes, such as racial and ethnic disparities. The scope of racial and ethnic bias related to algorithms and their application is difficult to quantify, but it clearly extends across the spectrum of medicine. Regulatory, professional, and corporate stakeholders are undertaking numerous efforts to develop standards for algorithms, often emphasizing the need for transparency, accountability, and representativeness. Conclusions. Algorithms have been shown to potentially perpetuate, exacerbate, and sometimes reduce racial and ethnic disparities. Disparities were reduced when race and ethnicity were incorporated into an algorithm to intentionally tackle known racial and ethnic disparities in resource allocation (e.g., kidney transplant allocation) or disparities in care (e.g., prostate cancer screening that historically led to Black men receiving more low-yield biopsies). It is important to note that in such cases the rationale for using race and ethnicity was clearly delineated and did not conflate race and ethnicity with ancestry and/or genetic predisposition. However, when algorithms include race and ethnicity without clear rationale, they may perpetuate the incorrect notion that race is a biologic construct and contribute to disparities. Finally, some algorithms may reduce or perpetuate disparities without containing race and ethnicity as an input. Several modeling studies showed that applying algorithms out of context of original development (e.g., illness severity scores used for crisis standards of care) could perpetuate or exacerbate disparities. On the other hand, algorithms may also reduce disparities by standardizing care and reducing opportunities for implicit bias (e.g., Lung Allocation Score for lung transplantation). Several mitigation strategies have been shown to potentially reduce the contribution of algorithms to racial and ethnic disparities. Results of mitigation efforts are highly context specific, relating to unique combinations of algorithm, clinical condition, population, setting, and outcomes. Important future steps include increasing transparency in algorithm development and implementation, increasing diversity of research and leadership teams, engaging diverse patient and community groups in the development to implementation lifecycle, promoting stakeholder awareness (including patients) of potential algorithmic risk, and investing in further research to assess the real-world effect of algorithms on racial and ethnic disparities before widespread implementation.
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Serakos, Demetrios, John E. Gray, and Hazim Youssef. Topics in Mitigating Radar Bias. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada604137.

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Panek, Krol, and Huth. PR-312-12208-R03 USEPA AERMOD Plume Rise and Volume Formulations and Implications for Existing RICE. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), February 2016. http://dx.doi.org/10.55274/r0010858.

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AERMOD is the EPA recommended dispersion modeling tool for evaluating impacts from typical compressor station engine sources. This is a companion document to two previous PRCI reports that addressed AERMOD Fortran compiler issues and a subsequent report that examined AERMOD Plume Volume Molar ratio Method (PVMRM) issues that lead to conservative model over-predictions. This report further explores AERMOD plume rise and volume estimates as a possible cause or contributor of model over-prediction and resulting plume chemistry concerns. AERMOD over-prediction bias has significant negative implications for permitting new sources, permit renewal for existing sources, and NAAQS compliance analyses, where modeled impacts are compared to the NO2 NAAQS at or beyond the facility fenceline. AERMOD conservatism also impacts state agency State Implementation Plans and resulting control strategies. Permitting requirements associated with the new 1-hour standard could impose unnecessary controls, overly stringent controls, and a significant compliance burden. Where mitigation may be warranted, costs will escalate due to �over-control� in response to model conservatism and deficiencies in model performance.
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Carter, Sara, Jane Griffin, Samantha Lako, Cheryl Harewood, Lisa Kessler, and Elizabeth Parish. The impacts of COVID-19 on schools’ willingness to participate in research. RTI Press, January 2024. http://dx.doi.org/10.3768/rtipress.2024.rb.0036.2401.

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COVID-19 had significant impacts on the field of education and, in turn, on school-based research. During this unprecedented time, nearly all schools closed, disrupting learning as schools shifted to a virtual format. Addressing the lasting effects of school closures is a major challenge in the post-pandemic education climate. Educators indicate these challenges have limited their willingness or ability to participate in research. We analyzed over 700 reasons for refusal in four recent education studies to examine the effects of COVID-19 on school-based research. About 4% of education leaders cited COVID-19 as the primary factor impacting their unwillingness to participate, while related factors such as learning loss, instructional time, or teacher shortage were cited approximately 16%. Over 40% of schools declined because of required testing and surveys. Given the voluntary nature of participation, the remaining schools declined for various reasons not necessarily related to COVID-19. Insufficient participation can be detrimental to research by impacting the quantity and quality of data collected and possibly introducing bias into the data, thus skewing findings. In the post-pandemic era, school-based researchers must be mindful of the challenges schools face and develop mitigation strategies to contend with the reluctance to participate in external research.
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Golub, Alla, Benjamin Henderson, Thomas Hertel, Steven Rose, Misak Avetisyan, and Brent Sohngen. Effects of GHG Mitigation Policies on Livestock Sectors. GTAP Working Paper, July 2010. http://dx.doi.org/10.21642/gtap.wp62.

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In this paper we have investigated effects of GHG mitigation policies on livestock sectors. We used a global computable general equilibrium GTAP-AEZ-GHG model with explicit unique regional land types, land uses and related GHG emissions. The model is then augmented with cost and GHG response information from partial equilibrium approaches to abatement of land-based greenhouse gas emissions. With this framework we analyze changes in regional livestock output, sector competitiveness and regional food consumption under different climate change mitigation policy regimes. Scenarios we have considered differ by participation/exclusion of agricultural sectors and non-Annex I countries, as well as policy instruments. The imposition of carbon tax in agriculture has adverse affects on food consumption, especially in developing countries. The reductions in food consumption are smaller if the agricultural producer subsidy is introduced to compensate for carbon tax the producers pay. The global forest carbon sequestration subsidy effectively controls emission leakage when carbon tax is imposed only in Annex I regions. The sequestration subsidy bids land away from agriculture in non-Annex 1 regions and prevents expansion of agricultural sectors. Though the sequestration subsidy allows reduction of GHG emissions, if implemented, the policy may adversely affect food security and agricultural development in developing countries.
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Avis, William. Refugee and Mixed Migration Displacement from Afghanistan. Institute of Development Studies (IDS), August 2021. http://dx.doi.org/10.19088/k4d.2022.002.

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This rapid literature review summarises evidence and key lessons that exist regarding previous refugee and mixed migration displacement from Afghanistan to surrounding countries. The review identified a diverse literature that explored past refugee and mixed migration, with a range of quantitative and qualitative studies identified. A complex and fluid picture is presented with waves of mixed migration (both outflow and inflow) associated with key events including the: Soviet–Afghan War (1979–1989); Afghan Civil War (1992–96); Taliban Rule (1996–2001); War in Afghanistan (2001–2021). A contextual picture emerges of Afghans having a long history of using mobility as a survival strategy or as social, economic and political insurance for improving livelihoods or to escape conflict and natural disasters. Whilst violence has been a principal driver of population movements among Afghans, it is not the only cause. Migration has also been associated with natural disasters (primarily drought) which is considered a particular issue across much of the country – this is associated primarily with internal displacement. Further to this, COVID-19 is impacting upon and prompting migration to and from Afghanistan. Data on refugee and mixed migration movement is diverse and at times contradictory given the fluidity and the blurring of boundaries between types of movements. Various estimates exist for numbers of Afghanistan refugees globally. It is also important to note that migratory flows are often fluid involving settlement in neighbouring countries, return to Afghanistan. In many countries, Afghani migrants and refugees face uncertain political situations and have, in recent years, been ‘coerced’ into returning to Afghanistan with much discussion of a ‘return bias’ being evident in official policies. The literature identified in this report (a mix of academic, humanitarian agency and NGO) is predominantly focused on Pakistan and Iran with a less established evidence base on the scale of Afghan refugee and migrant communities in other countries in the region. . Whilst conflict has been a primary driver of displacement, it has intersected with drought conditions and poor adherence to COVID-19 mitigation protocols. Past efforts to address displacement internationally have affirmed return as the primary objective in relation to durable solutions; practically, efforts promoted improved programming interventions towards creating conditions for sustainable return and achieving improved reintegration prospects for those already returned to Afghanistan.
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Bray, Jonathan, Ross Boulanger, Misko Cubrinovski, Kohji Tokimatsu, Steven Kramer, Thomas O'Rourke, Ellen Rathje, Russell Green, Peter Robertson, and Christine Beyzaei. U.S.—New Zealand— Japan International Workshop, Liquefaction-Induced Ground Movement Effects, University of California, Berkeley, California, 2-4 November 2016. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, March 2017. http://dx.doi.org/10.55461/gzzx9906.

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There is much to learn from the recent New Zealand and Japan earthquakes. These earthquakes produced differing levels of liquefaction-induced ground movements that damaged buildings, bridges, and buried utilities. Along with the often spectacular observations of infrastructure damage, there were many cases where well-built facilities located in areas of liquefaction-induced ground failure were not damaged. Researchers are working on characterizing and learning from these observations of both poor and good performance. The “Liquefaction-Induced Ground Movements Effects” workshop provided an opportunity to take advantage of recent research investments following these earthquake events to develop a path forward for an integrated understanding of how infrastructure performs with various levels of liquefaction. Fifty-five researchers in the field, two-thirds from the U.S. and one-third from New Zealand and Japan, convened in Berkeley, California, in November 2016. The objective of the workshop was to identify research thrusts offering the greatest potential for advancing our capabilities for understanding, evaluating, and mitigating the effects of liquefaction-induced ground movements on structures and lifelines. The workshop also advanced the development of younger researchers by identifying promising research opportunities and approaches, and promoting future collaborations among participants. During the workshop, participants identified five cross-cutting research priorities that need to be addressed to advance our scientific understanding of and engineering procedures for soil liquefaction effects during earthquakes. Accordingly, this report was organized to address five research themes: (1) case history data; (2) integrated site characterization; (3) numerical analysis; (4) challenging soils; and (5) effects and mitigation of liquefaction in the built environment and communities. These research themes provide an integrated approach toward transformative advances in addressing liquefaction hazards worldwide. The archival documentation of liquefaction case history datasets in electronic data repositories for use by the broader research community is critical to accelerating advances in liquefaction research. Many of the available liquefaction case history datasets are not fully documented, published, or shared. Developing and sharing well-documented liquefaction datasets reflect significant research efforts. Therefore, datasets should be published with a permanent DOI, with appropriate citation language for proper acknowledgment in publications that use the data. Integrated site characterization procedures that incorporate qualitative geologic information about the soil deposits at a site and the quantitative information from in situ and laboratory engineering tests of these soils are essential for quantifying and minimizing the uncertainties associated site characterization. Such information is vitally important to help identify potential failure modes and guide in situ testing. At the site scale, one potential way to do this is to use proxies for depositional environments. At the fabric and microstructure scale, the use of multiple in situ tests that induce different levels of strain should be used to characterize soil properties. The development of new in situ testing tools and methods that are more sensitive to soil fabric and microstructure should be continued. The development of robust, validated analytical procedures for evaluating the effects of liquefaction on civil infrastructure persists as a critical research topic. Robust validated analytical procedures would translate into more reliable evaluations of critical civil infrastructure iv performance, support the development of mechanics-based, practice-oriented engineering models, help eliminate suspected biases in our current engineering practices, and facilitate greater integration with structural, hydraulic, and wind engineering analysis capabilities for addressing multi-hazard problems. Effective collaboration across countries and disciplines is essential for developing analytical procedures that are robust across the full spectrum of geologic, infrastructure, and natural hazard loading conditions encountered in practice There are soils that are challenging to characterize, to model, and to evaluate, because their responses differ significantly from those of clean sands: they cannot be sampled and tested effectively using existing procedures, their properties cannot be estimated confidently using existing in situ testing methods, or constitutive models to describe their responses have not yet been developed or validated. Challenging soils include but are not limited to: interbedded soil deposits, intermediate (silty) soils, mine tailings, gravelly soils, crushable soils, aged soils, and cemented soils. New field and laboratory test procedures are required to characterize the responses of these materials to earthquake loadings, physical experiments are required to explore mechanisms, and new soil constitutive models tailored to describe the behavior of such soils are required. Well-documented case histories involving challenging soils where both the poor and good performance of engineered systems are documented are also of high priority. Characterizing and mitigating the effects of liquefaction on the built environment requires understanding its components and interactions as a system, including residential housing, commercial and industrial buildings, public buildings and facilities, and spatially distributed infrastructure, such as electric power, gas and liquid fuel, telecommunication, transportation, water supply, wastewater conveyance/treatment, and flood protection systems. Research to improve the characterization and mitigation of liquefaction effects on the built environment is essential for achieving resiliency. For example, the complex mechanisms of ground deformation caused by liquefaction and building response need to be clarified and the potential bias and dispersion in practice-oriented procedures for quantifying building response to liquefaction need to be quantified. Component-focused and system-performance research on lifeline response to liquefaction is required. Research on component behavior can be advanced by numerical simulations in combination with centrifuge and large-scale soil–structure interaction testing. System response requires advanced network analysis that accounts for the propagation of uncertainty in assessing the effects of liquefaction on large, geographically distributed systems. Lastly, research on liquefaction mitigation strategies, including aspects of ground improvement, structural modification, system health monitoring, and rapid recovery planning, is needed to identify the most effective, cost-efficient, and sustainable measures to improve the response and resiliency of the built environment.
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Eslava, Marcela, Alessandro Maffioli, and Marcela Meléndez Arjona. Second-tier Government Banks and Access to Credit: Micro-Evidence from Colombia. Inter-American Development Bank, March 2012. http://dx.doi.org/10.18235/0011364.

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Government-owned development banks have often been justified by the need to respond to financial market imperfections that hinder the establishment and growth of promising businesses, and as a result, stifle economic development more generally. However, evidence on the effectiveness of these banks in mitigating financial constraints is still lacking. To fill this gap, this paper analyzes the impact of Bancoldex, Colombia's publicly owned development bank, on access to credit. It uses a unique dataset that contains key characteristics of all loans issued to businesses in Colombia, including the financial intermediary through which the loan was granted and whether the loan was funded with Bancoldex resources. The paper assesses effects on access to credit by comparing Bancoldex loans to loans from other sources and study the impact of receiving credit from Bancoldex on a firm's subsequent credit history. To address concerns about selection bias, it uses a combination of models that control for fixed effects and matching techniques. The findings herein show that credit relationships involving Bancoldex funding are characterized by lower interest rates, larger loans, and loans with longer terms. These characteristics translated into lower average interest rates and larger average loans for firms that used Bancoldex credit. Average loans of Bancoldex' beneficiaries also exhibit longer terms, although this effect can take two years to materialize. Finally, the findings show evidence of a demonstration effect of Bancoldex: beneficiary firms that have access Bancoldex credit are able to significantly expand the number of intermediaries with whom they have credit relationships.

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