scholarly journals USING DATA SCIENCE TO RISK ASSESSMENT

2020 ◽  
Author(s):  
Olha Chubukova ◽  
Ihor Ponomarenko ◽  
Oksana Domantovych
Keyword(s):  
Risk Assessment ◽  
Data Science ◽  
Using Data

Challenges and Opportunities in Using Data Science for Homelessness Service Provision

2021 ◽  
Author(s):  
Charalampos Chelmis ◽  
Wenting Qi ◽  
Wonhyung Lee
Keyword(s):  
Service Provision ◽  
Data Science ◽  
Challenges And Opportunities ◽  
Using Data

scholarly journals New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
César de Oliveira Ferreira Silva ◽  
Mariana Matulovic ◽  
Rodrigo Lilla Manzione
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Data Science ◽  
Final Decision ◽  
Aquifer Recharge ◽  
Groundwater Governance ◽  
Ethical Attitude ◽  
New Information ◽  
Modeling Experiment ◽  
Using Data

Abstract Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract

Benchmarking driving efficiency using data science techniques applied on large - scale smartphone data

2018 ◽  
Author(s):  
Δημήτριος Τσελέντης
Keyword(s):  
Data Envelopment Analysis ◽  
Convex Hull ◽  
Large Scale ◽  
Data Science ◽  
Data Envelopment ◽  
Using Data

Ο κύριος στόχος της παρούσας διδακτορικής διατριβής είναι η ανάπτυξη μιας ολοκληρωμένης μεθοδολογικής προσέγγισης για τη συγκριτική αξιολόγηση της οδηγικής επίδοσης, όσον αφορά την οδική ασφάλεια, τόσο σε επίπεδο διαδρομής, όσο και οδηγού, με τη χρήση τεχνικών της επιστήμης δεδομένων. Η μεθοδολογική προσέγγιση στηρίζεται στον καθορισμό ενός δείκτη επίδοσης που βασίζεται στη θεωρία της Περιβάλλουσας Ανάλυσης Δεδομένων (Data Envelopment Analysis - DEA) και σχετίζεται με μακροσκοπικά συμπεριφοριστικά χαρακτηριστικά οδήγησης, όπως ο αριθμός των απότομων επιταχύνσεων/ επιβραδύνσεων, ο χρόνος χρήσης του κινητού τηλεφώνου και ο χρόνος υπέρβασης του ορίου ταχύτητας. Ακόμα, αναπτύσσονται μοντέλα μηχανικής μάθησης για τον προσδιορισμό διακριτών προφίλ οδήγησης που βασίζονται στη χρονική εξέλιξη της οδηγικής επίδοσης. Η προτεινόμενη μεθοδολογική προσέγγιση εφαρμόζεται σε πραγματικά δεδομένα οδήγησης ευρείας κλίμακας που συλλέγονται από έξυπνες συσκευές κινητών τηλεφώνων (smartphones), τα οποία αναλύονται μέσω στατιστικών μεθόδων για τον προσδιορισμό της απαιτούμενης ποσότητας δεδομένων οδήγησης που θα χρησιμοποιηθούν στην ανάλυση. Τα αποτελέσματα δείχνουν ότι ο βελτιστοποιημένος αλγόριθμος convex hull – DEA δίνει εξίσου ακριβή και ταχύτερα αποτελέσματα σε σχέση με τις κλασικές προσεγγίσεις της DEA. Ακόμα, η μεθοδολογία επιτρέπει τον προσδιορισμό των λιγότερο αποδοτικών ταξιδιών σε μια βάση δεδομένων καθώς και το αποδοτικό επίπεδο οδηγικών στοιχείων ενός ταξιδιού για να καταστεί αποδοτικότερη από την άποψη της ασφάλειας. Η περαιτέρω ομάδοποίηση των οδηγών με βάση της απόδοσή τους σε βάθος χρόνου οδηγεί στον εντοπισμό τριών ομάδων οδηγών, αυτή του μέσου οδηγού, του ασταθή οδηγού και του λιγότερο επικίνδυνου οδηγού. Τα αποτελέσματα δείχνουν ότι η εκ των προτέρων γνώση σχετικά με το ιστορικό ατυχημάτων του χρήστη φαίνεται να επηρεάζουν μόνο τη σύσταση της δεύτερης συστάδας των πιο ασταθών οδηγών, η οποία ενσωματώνει τους οδηγούς που είναι λιγότερο αποδοτικοί και ασταθής ως προς την ασφάλεια. Φαίνεται επίσης ότι η χρήση κινητών τηλεφώνων δεν αποτελεί κρίσιμο παράγοντα για τον καθορισμό της επίδοσης της ασφάλειας ενός οδηγού, καθώς διαπιστώθηκαν μικρές διαφορές σε σχέση με αυτό το χαρακτηριστική οδήγησης μεταξύ οδηγών διαφορετικών κατηγοριών επίδοσης. Επιπλέον, δείχνεται ότι απαιτείται μια διαφορετική δειγματοληψίας δεδομένων οδήγησης για κάθε α) οδικό τύπο, β) χαρακτηριστικό οδήγησης και γ) οδηγική επιθετικότητα για να συγκεντρωθούν αρκετά δεδομένα και να αποκτηθεί μια σαφής εικόνα της οδηγικής συμπεριφοράς και να εκτελεστεί ανάλυση με χρήση DEA. Τα αποτελέσματα θα μπορούσαν να αξιοποιηθούν για την παροχή εξατομικευμένης ανατροφοδότησης στους οδηγούς σχετικά με τη συνολική τους οδηγική επίδοση και την εξέλιξή της, προκειμένου να βελτιωθεί και να μειωθεί ο κίνδυνος ατυχήματος.

Using of data science in healthcare

2021 ◽  
pp. 149-156
Author(s):  
Ihor Ponomarenko ◽  
Oleksandra Lubkovska
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Business Intelligence ◽  
Data Science ◽  
Large Data ◽  
Science Methods ◽  
Medical Field ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data

The subject of the research is the approach to the possibility of using data science methods in the field of health care for integrated data processing and analysis in order to optimize economic and specialized processes The purpose of writing this article is to address issues related to the specifics of the use of Data Science methods in the field of health care on the basis of comprehensive information obtained from various sources. Methodology. The research methodology is system-structural and comparative analyzes (to study the application of BI-systems in the process of working with large data sets); monograph (the study of various software solutions in the market of business intelligence); economic analysis (when assessing the possibility of using business intelligence systems to strengthen the competitive position of companies). The scientific novelty the main sources of data on key processes in the medical field. Examples of innovative methods of collecting information in the field of health care, which are becoming widespread in the context of digitalization, are presented. The main sources of data in the field of health care used in Data Science are revealed. The specifics of the application of machine learning methods in the field of health care in the conditions of increasing competition between market participants and increasing demand for relevant products from the population are presented. Conclusions. The intensification of the integration of Data Science in the medical field is due to the increase of digitized data (statistics, textual informa- tion, visualizations, etc.). Through the use of machine learning methods, doctors and other health professionals have new opportunities to improve the efficiency of the health care system as a whole. Key words: Data science, efficiency, information, machine learning, medicine, Python, healthcare.

Evidence-Based Sentencing

Criminology ◽  
2021 ◽  
Author(s):  
James C. Oleson
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Risk Measures ◽  
Criminal History ◽  
Community Safety ◽  
Assessment Instruments ◽  
Evidence Based ◽  
Criminogenic Needs ◽  
Actuarial Risk ◽  
Using Data

The evidence-based practice (EBP) movement can be traced to a 1992 article in the Journal of the American Medical Association, although decision-making with empirical evidence (rather than tradition, anecdote, or intuition) is obviously much older. Neverthless, for the last twenty-five years, EBP has played a pivotal role in criminal justice, particularly within community corrections. While the prediction of recidivism in parole or probation decisions has attracted relatively little attention, the use of risk measures by sentencing judges is controversial. This might be because sentencing typically involves both backward-looking decisions, related to the blameworthiness of the crime, as well as forward-looking decisions, about the offender’s prospective risk of recidivism. Evidence-based sentencing quantifies the predictive aspects of decision-making by incorporating an assessment of risk factors (which increase recidivism risk), protective factors (which reduce recidivism risk), criminogenic needs (impairments that, if addressed, will reduce recidivism risk), the measurement of recidivism risk, and the identification of optimal recidivism-reducing sentencing interventions. Proponents for evidence-based sentencing claim that it can allow judges to “sentence smarter” by using data to distinguish high-risk offenders (who might be imprisoned to mitigate their recidivism risk) from low-risk offenders (who might be released into the community with relatively little danger). This, proponents suggest, can reduce unnecessary incarceration, decrease costs, and enhance community safety. Critics, however, note that risk assessment typically looks beyond criminal conduct, incorporating demographic and socioeconomic variables. Even if a risk factor is facially neutral (e.g., criminal history), it might operate as a proxy for a constitutionally protected category (e.g., race). The same objectionable variables are used widely in presentence reports, but their incorporation into an actuarial risk score has greater potential to obfuscate facts and reify underlying disparities. The evidence-based sentencing literature is dynamic and rapidly evolving, but this bibliography identifies sources that might prove useful. It first outlines the theoretical foundations of traditional (non-evidence-based) sentencing, identifying resources and overviews. It then identifies sources related to decision-making and prediction, risk assessment logic, criminogenic needs, and responsivity. The bibliography then describes and defends evidence-based sentencing, and identifies works on sentencing variables and risk assessment instruments. It then relates evidence-based sentencing to big data and identifies data issues. Several works on constitutional problems are listed, the proxies problem is described, and sources on philosophical issues are described. The bibliography concludes with a description of validation research, the politics of evidence-based sentencing, and the identification of several current initiatives.

Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science

2021 ◽  
Author(s):  
◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Data Science ◽  
Defense Acquisition ◽  
Acquisition Workforce ◽  
Using Data

An Assessment on Classification in Python Using Data Science

2021 ◽  
Author(s):  
Margaret Mary T ◽  
Soumya K ◽  
Ramanathan G ◽  
Clinton G
Keyword(s):  
Data Science ◽  
Using Data

Layers of Bias: A Unified Approach for Understanding Problems With Risk Assessment

2018 ◽  
Vol 46 (2) ◽  
pp. 185-209 ◽  
Author(s):  
Laurel Eckhouse ◽  
Kristian Lum ◽  
Cynthia Conti-Cook ◽  
Julie Ciccolini
Keyword(s):  
Risk Assessment ◽  
Criminal Justice ◽  
Justice System ◽  
Quantitative Risk Assessment ◽  
Risk Scores ◽  
Risk Models ◽  
Risk Assessment Models ◽  
Final Layer ◽  
The Supreme Court ◽  
Using Data

Scholars in several fields, including quantitative methodologists, legal scholars, and theoretically oriented criminologists, have launched robust debates about the fairness of quantitative risk assessment. As the Supreme Court considers addressing constitutional questions on the issue, we propose a framework for understanding the relationships among these debates: layers of bias. In the top layer, we identify challenges to fairness within the risk-assessment models themselves. We explain types of statistical fairness and the tradeoffs between them. The second layer covers biases embedded in data. Using data from a racially biased criminal justice system can lead to unmeasurable biases in both risk scores and outcome measures. The final layer engages conceptual problems with risk models: Is it fair to make criminal justice decisions about individuals based on groups? We show that each layer depends on the layers below it: Without assurances about the foundational layers, the fairness of the top layers is irrelevant.

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