scholarly journals Artificial intelligence, risk assessment, and potential racial implications

2021 ◽  
Vol 22 ◽  
pp. 68-73
Author(s):  
Pamela Ugwudike
Keyword(s):  
Artificial Intelligence ◽  
Risk Assessment

Making algorithms safe for workers: occupational risks associated with work managed by artificial intelligence

2021 ◽  
pp. 102425892110350
Author(s):  
Adrián Todolí-Signes
Keyword(s):  
Artificial Intelligence ◽  
Risk Assessment ◽  
Health And Safety ◽  
Health Hazards ◽  
Risk Prevention ◽  
Occupational Risks ◽  
Technological Management

It is increasingly common for companies to use artificial intelligence mechanisms to manage work. This study examines the health hazards caused by these new forms of technological management. Occupational risks can be reduced if they are taken into account when programming an algorithm. This study confirms the need for algorithms to be correctly programmed, taking account of these occupational risks. In the same way as supervisors have to be trained in risk prevention to be able to perform their work, the algorithm must be programmed to weigh up the occupational risks – and when such features do not exist, steps must be taken to prevent the algorithm being used to direct workers. The algorithm must assess all (known) factors posing a risk to workers’ health and safety. It therefore seems necessary to incorporate a mandatory risk assessment performed by specialists in the programming of algorithms so that all ascertained risks can be taken into account.

scholarly journals The place of artificial intelligence in the risk management process

2021 ◽  
Vol 120 ◽  
pp. 02013
Author(s):  
Petya Biolcheva
Keyword(s):  
Artificial Intelligence ◽  
Risk Assessment ◽  
Risk Management ◽  
Real Time ◽  
Risk Identification ◽  
Management Process ◽  
Whole Process ◽  
Risk Management Process ◽  
Problematic Issue ◽  
The Individual

In recent years, there has been increasing talk of the rapid entry of artificial intelligence into risk management. All the benefits it would bring over the whole process are often commented on: real-time results, processing large amounts of data, more complete risk identification, more accurate risk assessment, etc. There are also negative moods that make various experts feel threatened by their need to be replaced by artificial intelligence. Another problematic issue that arises is related to the transparency of algorithms and the increase in cyber risks [6]. This material aims to identify the individual elements at the stages of risk management in which artificial intelligence (AI) can and should be applied alone, in combination with expert opinion or not. Here it is shown that because of the use of AI the efficiency of the whole process is significantly increased, first of all by conducting in-depth analyses, and the decisions are made by the risk management experts. This proves its usefulness and increases the confidence of experts in it.

Conclusion

2020 ◽  
pp. 97-102
Author(s):  
Benjamin Wiggins
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Machine Learning ◽  
Risk Assessment ◽  
Big Data ◽  
The United States ◽  
Blaise Pascal ◽  
Division Problem ◽  
The World

Can risk assessment be made fair? The conclusion of Calculating Race returns to actuarial science’s foundations in probability. The roots of probability rest in a pair of problems posed to Blaise Pascal and Pierre de Fermat in the summer of 1654: “the Dice Problem” and “the Division Problem.” From their very foundation, the mathematics of probability offered the potential not only to be used to gain an advantage (as in the case of the Dice Problem), but also to divide material fairly (as in the case of the Division Problem). As the United States and the world enter an age driven by Big Data, algorithms, artificial intelligence, and machine learning and characterized by an actuarialization of everything, we must remember that risk assessment need not be put to use for individual, corporate, or government advantage but, rather, that it has always been capable of guiding how to distribute risk equitably instead.

scholarly journals Artificial Intelligence Based Commercial Risk Management Framework for SMEs

2019 ◽  
Vol 11 (16) ◽  
pp. 4501
Author(s):  
Gerda Žigienė ◽  
Egidijus Rybakovas ◽  
Robertas Alzbutas
Keyword(s):  
Artificial Intelligence ◽  
Risk Assessment ◽  
Risk Management ◽  
Academic Research ◽  
Building Blocks ◽  
Learning Technologies ◽  
Potential Contribution ◽  
Risk Management Framework ◽  
Development Goals ◽  
Risk Assessment And Management

Risk management in commercial processes is among the most important procedures affecting the competitiveness of small and medium-sized enterprises (SMEs), their innovativeness and potential contribution to global sustainable development goals (SDGs). The ecosystem of commercial processes is the prerequisite to manage risk faced by SMEs. Commercial risk assessment and management using elements of artificial intelligence, big data, and machine learning technologies could be developed and maintained as external services for a group of SMEs allowing to share costs and benefits. This paper aims to provide a conceptual framework of commercial risk assessment and management solution based on elements of artificial intelligence. This conceptualization is done on the background of scientific literature, policy documents, and risk management standards. Main building blocks of the framework in terms of commercial risk categories, data sources and workflow phases are presented in the article. Business companies, state policy, and academic research focused recommendations on the further development of the framework and its implementation are elaborated.

scholarly journals An Artificial Intelligence-based Multimodality-Integration for Precise Prediction of Prostate Cancer Lymph Node Metastasis: A Retrospective Two-center Study

2020 ◽  
Author(s):  
Ying Hou ◽  
Jie Bao ◽  
Yang Song ◽  
Mei-Ling Bao ◽  
Ke-Wen Jiang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Risk Assessment ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Assessment Tool ◽  
Prostate Cancer Risk ◽  
Pelvic Lymph Node ◽  
Cancer Center ◽  
Node Metastasis

Abstract BackgroundAccurate identification of pelvic lymph node metastasis (PLNM) in patients with prostate cancer (PCa) is crucial for determining appropriate treatment options. However, there is no clear consensus on the integration of clinicopathological and imaging findings available to predict PLNM. Therefore, we built a Prostate Cancer Risk (PRISK) tool using an artificial intelligence-based multimodality-integration to obtain a precisely informed decision about whether to perform extended pelvic lymph node dissection (ePLND). MethodsPRISK provides a novel precise risk assessment tool to reduce unnecessary ePLNDs while controlling PLNM missing rate. It was developed in 280 patients and verified in 71 patients internally and in 50 patients externally by integrating a set of radiologists’ interpretations, clinicopathological factors and newly refined imaging indicators from MR images with radiomics machine learning and deep learning algorithms. Its clinical applicability was compared with Briganti and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms.ResultsPRISK yielded the best diagnostic performance with areas under the receiver operating characteristic curve (AUC) of 0.932 (95% CI, 0.895-0.958), 0.924 (95% CI, 0.837-0.974) and 0.758 (95% CI, 0.616-0.868) in the training/validation, internal test and external test cohorts. If the No. of ePLNDs missed for risk assessment is controlled at < 2%, PRISK can provide both higher No. of ePLNDs spared (PRISK 59.6% vs MSKCC 44.9% vs Briganti 37.7%) and lower No. of false-positives (PRISK 59.3% vs MSKCC 70.1% and Briganti 72.7%) as compared with MSKCC and Briganti score. In follow-up, patients stratified by PRISK showed significantly different biochemical recurrence rate after surgery.ConclusionsPRISK offers a noninvasive clinical biomarker to predict PLNM for patients with PCa. It shows improved accuracy of diagnosis support and reduced overtreatment burdens for patients with findings suggested of PCa.

The application of artificial intelligence and machine learning to automate Gleason grading: Novel tools to develop next generation risk assessment assays.

2018 ◽  
Vol 36 (6_suppl) ◽  
pp. 170-170 ◽  
Author(s):  
Michael Joseph Donovan ◽  
Richard Scott ◽  
Faisal m Khan ◽  
Jack Zeineh ◽  
Gerardo Fernandez
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Assessment ◽  
Feature Selection ◽  
Clinical Decision Making ◽  
P Value ◽  
Gleason Grade ◽  
Next Generation ◽  
Ring Fusion ◽  
Gleason Grading

170 Background: Postoperative risk assessment remains an important variable in the treatment of prostate cancer (PCA). Advances in genomic risk classifiers have aided clinical-decision making; however, clinical-pathologic variables such as Gleason grade and pathologic stage remain significant comparators for accurate prognostication. We aimed to standardize the descriptive pathology of PCA through automation of Gleason grading with artificial intelligence and image analysis feature selection Methods: Retrospective study using radical prostatectomy (RP) tissue microarrays from Henry Ford Hospital and Roswell Park Cancer Center with 8-year median follow-up. Samples were stained with a multiplex immunofluorescent assay: Androgen Receptor (AR), Ki67, Cytokeratin 18, Cytokeratin 5/6 and Alpha-methylacyl-CoA racemase); imaged with a CRI Nuance FX camera and then analyzed with proprietary software to generate a suite of morphometric - attributes that quantitatively characterize the Gleason spectrum. Derived features were univariately correlated with disease progression using the concordance index (CI) along with the hazards ratio and p-value. Results: Starting with a training cohort of 306 patients and a 15% event rate, MIF PCA images were subjected to a machine learning analysis program which incorporates a graph theory-based approach for characterization of gland / ring fusion and fragmentation of tumor architecture (TA) with biomarker quantitation (BQ) (i.e. AR and Ki67). 19 unique image features with 7 TA and 12 TA+BQ were identified, by univariate CI, all TA features were strongly associated with Gleason grading with CI’s reflecting degree of tumor differentiation (CI 0.29-.33, p-value = 0.005). Four TA+BQ features were selected in a training risk model and effectively replaced the clinical Gleason features. By comparison, dominant RP Gleason had a CI of 0.31. Conclusions: Image-based feature selection guided by principles of machine learning has the potential to automate and replace traditional Gleason grading. Such approaches provide the necessary foundation for next generation risk assessment assays.

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