An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
Volume-3 | Issue-3
Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
Volume-2 | Issue-1
Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
Volume-3 | Issue-3
A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems
Volume-3 | Issue-3
An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
Volume-3 | Issue-4
An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
Volume-3 | Issue-4
Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
Volume-3 | Issue-4
Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
Volume-3 | Issue-4
Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Volume - 4 | Issue - 4 | december 2022
Published
05 November, 2022
The importance of counting for fairness has increased significantly in the design and engineering of those systems because of the rapid rise and widespread use of Artificial Intelligence (AI) systems and its applications in our daily lives. It is crucial to guarantee that the opinions formed by AI systems do not represent discrimination against particular groups or populations because these systems have the potential to be employed in a variety of sensitive contexts to form significant and life-changing judgments. Recent advances in traditional machine learning and deep learning have addressed these issues in a variety of subfields. Scientists are striving to overcome the biases that these programs may possess because of the industrialization of these systems and are getting familiar with them. This study looks into several practical systems that had exhibited biases in a wide variety of ways, and compiles a list of various biases’ possible sources. Then, in order to eliminate the bias previously existing in AI technologies, a hierarchy for fairness characteristics has been created. Additionally, numerous AI fields and sub domains are studied to highlight what academics have noticed regarding improper conclusions in the most cutting-edge techniques and ways they have attempted to remedy them. To lessen the issue of bias in AI systems, multiple potential future avenues and results are currently present. By examining the current research in their respective domains, it is hoped that this survey may inspire scholars to amend these problems promptly.
KeywordsArtificial intelligence bias fairness machine learning models
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