Identifying Perverse Incentives in Buyer Profiling on Online Trading Platforms

2021 ◽  
Author(s):  
Karthik Kannan ◽  
Rajib L. Saha ◽  
Warut Khern-am-nuai
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Analytical Model ◽  
Perverse Incentive ◽  
Intelligence Models ◽  
Online Trading ◽  
Artificial Intelligence Models ◽  
Perverse Incentives ◽  
Consumer Profiling ◽  
Underlying Mechanisms

With advance machine learning and artificial intelligence models, the capability of online trading platforms to profile consumers to identify and understand their needs has substantially increased. In this study, we use an analytical model to study whether these platforms have an incentive to profile their customers as accurately as possible. We find that “payments-for-transactions” platforms (i.e., platforms that charge for transactions that occur on the platform) indeed have such incentives to accurately profile the customers. However, surprisingly, “payments-for-discoveries” platform (i.e., platforms that charge customers for discoveries) have a perverse incentive to deviate from accurate consumer profiling. Our study provides insights into underlying mechanisms that drive this perverse incentive and discuss circumstances that lead to such a perverse incentive.

Role of machine learning and artificial intelligence algorithms for teaching reform of linguistics

2020 ◽  
pp. 1-12
Author(s):  
Wang Li
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Search Space ◽  
Teaching Experiment ◽  
Machine Learning Algorithms ◽  
Teaching Process ◽  
Root Cause ◽  
Pruning Strategy ◽  
Intelligence Models ◽  
Artificial Intelligence Models

The teaching of linguistics is limited by the influence of various factors, which leads to poor teaching effect, and the teaching process is difficult to evaluate. In order to improve the efficiency of linguistics teaching, this paper uses improved machine learning algorithms to construct a linguistics artificial intelligence teaching model. According to the teaching needs of linguistics, the efficiency of the teaching process is improved, and the teaching evaluation is performed, and the root cause analysis algorithm based on MCTS is optimized. Moreover, according to the frequent item set algorithm in data mining, a layered pruning strategy is proposed to further reduce the search space and improve the efficiency of the model. In addition, this study combines with the comparative teaching experiment to study the efficiency of artificial intelligence models in linguistics teaching. The statistical results show that the model proposed in this paper has a certain effect.

scholarly journals Artificial Intelligence – Creating the Future in Orthodontics – A Review

2021 ◽  
Vol 10 (28) ◽  
pp. 2108-2113
Author(s):  
Jeyaram Palanivel ◽  
Davis D ◽  
Dilip Srinivasan ◽  
Sushil Chakravarthi N.C. ◽  
Priya Kalidass ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Treatment Plan ◽  
Complete Information ◽  
Diagnosis And Treatment ◽  
Human Intelligence ◽  
Need For Treatment ◽  
Clinical Scenarios ◽  
Intelligence Models ◽  
Artificial Intelligence Models

With the search for a smarter, faster, and technological ways of getting things accomplished, Artificial Intelligence (AI) is developing at a faster pace. The technology has become a part of daily life, where the blend of human intelligence and machine learning has reached heights in various fields of science and technology. The machine simulates the human intelligence and improves their abilities with the help of self-adapting algorithms. Artificial intelligence has provided many benefits in various fields, particularly in medicine, where it plays a major role in the advancement of the medical field, ranging from virtual assistants to creating a better diagnosis and treatment using accumulated patient data. In orthodontics, the treatment focuses on altering the occlusion, controlling the development of dentoalveolar components and growth abnormalities. An effective assessment of these problems enables in determining the need for treatment and to prioritize it. Precise diagnosis, offering relevant and complete information is a key to a successful practice in orthodontics. Of late artificial intelligence is applied in orthodontics in decision making and planning effective treatment outcomes. Artificial intelligence is useful in simulation of various clinical scenarios in the three-essential sequence - diagnosis, treatment planning and treatment, which is efficient enough in reducing the workload, time and also increases the accuracy and monitoring. In no ways artificial intelligence can replace the dentist because clinical practice is not just about the diagnosis and treatment plan. So, humans should have a basic understanding on artificial intelligence models to assist in clinical judgement and not to replace the knowledge and expertise of humans. KEY WORDS Artificial intelligence, Machine Learning, Artificial Neural Network, Orthodontics, Review

scholarly journals Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Albert T. Young ◽  
Kristen Fernandez ◽  
Jacob Pfau ◽  
Rasika Reddy ◽  
Nhat Anh Cao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Stress Testing ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Skin Lesions ◽  
Stress Tests ◽  
Performance Metric ◽  
Intelligence Models ◽  
Artificial Intelligence Models

AbstractArtificial intelligence models match or exceed dermatologists in melanoma image classification. Less is known about their robustness against real-world variations, and clinicians may incorrectly assume that a model with an acceptable area under the receiver operating characteristic curve or related performance metric is ready for clinical use. Here, we systematically assessed the performance of dermatologist-level convolutional neural networks (CNNs) on real-world non-curated images by applying computational “stress tests”. Our goal was to create a proxy environment in which to comprehensively test the generalizability of off-the-shelf CNNs developed without training or evaluation protocols specific to individual clinics. We found inconsistent predictions on images captured repeatedly in the same setting or subjected to simple transformations (e.g., rotation). Such transformations resulted in false positive or negative predictions for 6.5–22% of skin lesions across test datasets. Our findings indicate that models meeting conventionally reported metrics need further validation with computational stress tests to assess clinic readiness.

scholarly journals How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Xinran Wang ◽  
Liang Wang ◽  
Hong Bu ◽  
Ningning Zhang ◽  
Meng Yue ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Clinical Practice ◽  
Subjective Assessment ◽  
Current Guideline ◽  
Scoring Method ◽  
Programmed Death ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Scoring Accuracy

AbstractProgrammed death ligand-1 (PD-L1) expression is a key biomarker to screen patients for PD-1/PD-L1-targeted immunotherapy. However, a subjective assessment guide on PD-L1 expression of tumor-infiltrating immune cells (IC) scoring is currently adopted in clinical practice with low concordance. Therefore, a repeatable and quantifiable PD-L1 IC scoring method of breast cancer is desirable. In this study, we propose a deep learning-based artificial intelligence-assisted (AI-assisted) model for PD-L1 IC scoring. Three rounds of ring studies (RSs) involving 31 pathologists from 10 hospitals were carried out, using the current guideline in the first two rounds (RS1, RS2) and our AI scoring model in the last round (RS3). A total of 109 PD-L1 (Ventana SP142) immunohistochemistry (IHC) stained images were assessed and the role of the AI-assisted model was evaluated. With the assistance of AI, the scoring concordance across pathologists was boosted to excellent in RS3 (0.950, 95% confidence interval (CI): 0.936–0.962) from moderate in RS1 (0.674, 95% CI: 0.614–0.735) and RS2 (0.736, 95% CI: 0.683–0.789). The 2- and 4-category scoring accuracy were improved by 4.2% (0.959, 95% CI: 0.953–0.964) and 13% (0.815, 95% CI: 0.803–0.827) (p < 0.001). The AI results were generally accepted by pathologists with 61% “fully accepted” and 91% “almost accepted”. The proposed AI-assisted method can help pathologists at all levels to improve the PD-L1 assay (SP-142) IC assessment in breast cancer in terms of both accuracy and concordance. The AI tool provides a scheme to standardize the PD-L1 IC scoring in clinical practice.

Sign in / Sign up

Export Citation Format

Share Document