scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

scholarly journals Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

2021 ◽  
Vol 18 (3) ◽  
pp. 1117
Author(s):  
Tarik Alafif ◽  
Abdul Muneeim Tehame ◽  
Saleh Bajaba ◽  
Ahmed Barnawi ◽  
Saad Zia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
State Of The Art ◽  
Diagnosis And Treatment ◽  
Future Directions ◽  
Health Community ◽  
The World ◽  
The Status ◽  
And Performance

With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.

Deep Learning Network

2020 ◽  
pp. 1-30
Author(s):  
Bhanu Chander
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Great Power ◽  
Human Being ◽  
Technology Improvement ◽  
Deep Learning Network ◽  
High Level ◽  
Human Brains ◽  
Near Future

Artificial intelligence (AI) is defined as a machine that can do everything a human being can do and produce better results. Means AI enlightening that data can produce a solution for its own results. Inside the AI ellipsoidal, Machine learning (ML) has a wide variety of algorithms produce more accurate results. As a result of technology, improvement increasing amounts of data are available. But with ML and AI, it is very difficult to extract such high-level, abstract features from raw data, moreover hard to know what feature should be extracted. Finally, we now have deep learning; these algorithms are modeled based on how human brains process the data. Deep learning is a particular kind of machine learning that provides flexibility and great power, with its attempts to learn in multiple levels of representation with the operations of multiple layers. Deep learning brief overview, platforms, Models, Autoencoders, CNN, RNN, and Appliances are described appropriately. Deep learning will have many more successes in the near future because it requires very little engineering by hand.

Deep learning architecture using rough sets and rough neural networks

Kybernetes ◽  
2017 ◽  
Vol 46 (4) ◽  
pp. 693-705 ◽  
Author(s):  
Yasser F. Hassan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Soft Computing ◽  
Rough Set ◽  
Real World ◽  
Rough Sets ◽  
Decision Table ◽  
Content Type ◽  
Real World Applications ◽  
Decision Tables

Purpose This paper aims to utilize machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications. Design/methodology/approach The objective of this work is to propose a model for deep rough set theory that uses more than decision table and approximating these tables to a classification system, i.e. the paper propose a novel framework of deep learning based on multi-decision tables. Findings The paper tries to coordinate the local properties of individual decision table to provide an appropriate global decision from the system. Research limitations/implications The rough set learning assumes the existence of a single decision table, whereas real-world decision problem implies several decisions with several different decision tables. The new proposed model can handle multi-decision tables. Practical implications The proposed classification model is implemented on social networks with preferred features which are freely distribute as social entities with accuracy around 91 per cent. Social implications The deep learning using rough sets theory simulate the way of brain thinking and can solve the problem of existence of different information about same problem in different decision systems Originality/value This paper utilizes machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications.

scholarly journals State of the Art in Computational Bioacoustics and Machine Learning: How far have we come?

2019 ◽  
Vol 3 ◽  
Author(s):  
Dan Stowell
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
State Of The Art ◽  
Learning Networks ◽  
Recent Developments ◽  
Audio Data ◽  
Ecological Applications ◽  
Near Future ◽  
Statistical Ecology

Terrestrial bioacoustics, like many other domains, has recently witnessed some transformative results from the application of deep learning and big data (Stowell 2017, Mac Aodha et al. 2018, Fairbrass et al. 2018, Mercado III and Sturdy 2017). Generalising over specific projects, which bioacoustic tasks can we consider "solved"? What can we expect in the near future, and what remains hard to do? What does a bioacoustician need to understand about deep learning? This contribution will address these questions, giving the audience a concise summary of recent developments and ways forward. It builds on recent projects and evaluation campaigns led by the author (Stowell et al. 2015, Stowell et al. 2018), as well as broader developments in signal processing, machine learning and bioacoustic applications of these. We will discuss which type of deep learning networks are appropriate for audio data, how to address zoological/ecological applications which often have few available data, and issues in integrating deep learning predictions with existing workflows in statistical ecology.

scholarly journals Discovering hidden information in biosignals from patients using artificial intelligence

2020 ◽  
Vol 73 (4) ◽  
pp. 275-284
Author(s):  
Dukyong Yoon ◽  
Jong-Hwan Jang ◽  
Byung Jin Choi ◽  
Tae Young Kim ◽  
Chang Ho Han
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Medical Condition ◽  
Real Life ◽  
Black Box ◽  
Basic Knowledge ◽  
Hidden Information ◽  
Near Future ◽  
Invasive Evaluation

Biosignals such as electrocardiogram or photoplethysmogram are widely used for determining and monitoring the medical condition of patients. It was recently discovered that more information could be gathered from biosignals by applying artificial intelligence (AI). At present, one of the most impactful advancements in AI is deep learning. Deep learning-based models can extract important features from raw data without feature engineering by humans, provided the amount of data is sufficient. This AI-enabled feature presents opportunities to obtain latent information that may be used as a digital biomarker for detecting or predicting a clinical outcome or event without further invasive evaluation. However, the black box model of deep learning is difficult to understand for clinicians familiar with a conventional method of analysis of biosignals. A basic knowledge of AI and machine learning is required for the clinicians to properly interpret the extracted information and to adopt it in clinical practice. This review covers the basics of AI and machine learning, and the feasibility of their application to real-life situations by clinicians in the near future.

scholarly journals Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

2020 ◽  
Vol 12 (20) ◽  
pp. 3338
Author(s):  
Rami Al-Ruzouq ◽  
Mohamed Barakat A. Gibril ◽  
Abdallah Shanableh ◽  
Abubakir Kais ◽  
Osman Hamed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Oil Spill ◽  
State Of The Art ◽  
Remotely Sensed ◽  
Classification Systems ◽  
Remotely Sensed Data ◽  
Future Directions ◽  
Oil Spill Detection ◽  
Different Sources

Remote sensing technologies and machine learning (ML) algorithms play an increasingly important role in accurate detection and monitoring of oil spill slicks, assisting scientists in forecasting their trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective treatments to contain and alleviate adverse effects. Review and analysis of different sources of remotely sensed data and various components of ML classification systems for oil spill detection and monitoring are presented in this study. More than 100 publications in the field of oil spill remote sensing, published in the past 10 years, are reviewed in this paper. The first part of this review discusses the strengths and weaknesses of different sources of remotely sensed data used for oil spill detection. Necessary preprocessing and preparation of data for developing classification models are then highlighted. Feature extraction, feature selection, and widely used handcrafted features for oil spill detection are subsequently introduced and analyzed. The second part of this review explains the use and capabilities of different classical and developed state-of-the-art ML techniques for oil spill detection. Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions and insights into future directions.

A Review of Deep Machine Learning

2016 ◽  
Vol 24 ◽  
pp. 124-136 ◽  
Author(s):  
Ben Bright Benuwa ◽  
Yong Zhao Zhan ◽  
Benjamin Ghansah ◽  
Dickson Keddy Wornyo ◽  
Frank Banaseka Kataka
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Paradigm Shift ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Algorithm Design ◽  
Prior State ◽  
Art Techniques

The rapid increase of information and accessibility in recent years has activated a paradigm shift in algorithm design for artificial intelligence. Recently, deep learning (a surrogate of Machine Learning) have won several contests in pattern recognition and machine learning. This review comprehensively summarises relevant studies, much of it from prior state-of-the-art techniques. This paper also discusses the motivations and principles regarding learning algorithms for deep architectures.

scholarly journals ProtTox: Toxin identification from Protein Sequences

2020 ◽  
Author(s):  
Sathappan Muthiah ◽  
Debanjan Datta ◽  
Mohammad Raihanul Islam ◽  
Patrick Butler ◽  
Andrew Warren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Synthetic Biology ◽  
Real World ◽  
Protein Sequences ◽  
Structural Diversity ◽  
Manual Annotation ◽  
Real World Applications ◽  
Automated Machine Learning

AbstractToxin classification of protein sequences is a challenging task with real world applications in healthcare and synthetic biology. Due to an ever expanding database of proteins and the inordinate cost of manual annotation, automated machine learning based approaches are crucial. Approaches need to overcome challenges of homology, multi-functionality, and structural diversity among proteins in this task. We propose a novel deep learning based method ProtTox, that aims to address some of the shortcomings of previous approaches in classifying proteins as toxins or not. Our method achieves a performance of 0.812 F1-score which is about 5% higher than the closest performing baseline.

scholarly journals Systems AI: A Declarative Learning Based Programming Perspective

2018 ◽  
Author(s):  
Parisa Kordjamshidi ◽  
Dan Roth ◽  
Kristian Kersting
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Data Driven ◽  
Real World Data ◽  
Future Directions ◽  
Knowledge Representations ◽  
Real World Applications ◽  
Need To Evaluate ◽  
Declarative Learning ◽  
High Level

Data-driven approaches are becoming dominant problem-solving techniques in many areas of research and industry. Unfortunately, current technologies do not make such techniques easy to use for application experts who are not fluent in machine learning nor for machine learning experts who aim at testing ideas on real-world data and need to evaluate those as a part of an end-to-end system. We review key efforts made by various AI communities to provide languages for high-level abstractions over learning and reasoning techniques needed for designing complex AI systems. We classify the existing frameworks based on the type of techniques as well as the data and knowledge representations they use, provide a comparative study of the way they address the challenges of programming real-world applications, and highlight some shortcomings and future directions.

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