scholarly journals People’s Councils for Ethical Machine Learning

2018 ◽  
Vol 4 (2) ◽  
pp. 205630511876830 ◽  
Author(s):  
Dan McQuillan
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Big Data ◽  
Knowledge Production ◽  
The Core ◽  
Machine Learning Methods ◽  
The Family ◽  
The World ◽  
Social Media Platforms ◽  
Drone Warfare

Machine learning is a form of knowledge production native to the era of big data. It is at the core of social media platforms and everyday interactions. It is also being rapidly adopted for research and discovery across academia, business, and government. This article will explores the way the affordances of machine learning itself, and the forms of social apparatus that it becomes a part of, will potentially erode ethics and draw us in to a drone-like perspective. Unconstrained machine learning enables and delimits our knowledge of the world in particular ways: the abstractions and operations of machine learning produce a “view from above” whose consequences for both ethics and legality parallel the dilemmas of drone warfare. The family of machine learning methods is not somehow inherently bad or dangerous, nor does implementing them signal any intent to cause harm. Nevertheless, the machine learning assemblage produces a targeting gaze whose algorithms obfuscate the legality of its judgments, and whose iterations threaten to create both specific injustices and broader states of exception. Given the urgent need to provide some kind of balance before machine learning becomes embedded everywhere, this article proposes people’s councils as a way to contest machinic judgments and reassert openness and discourse.

scholarly journals People's Councils for Ethical Machine Learning

2018 ◽  
Author(s):  
Dan McQuillan
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Big Data ◽  
Knowledge Production ◽  
The Core ◽  
Machine Learning Methods ◽  
The Family ◽  
The World ◽  
Social Media Platforms ◽  
Drone Warfare

Machine learning is a form of knowledge production native to the era of big data. It is at the core of social media platforms and everyday interactions. It is also being rapidly adopted for research and discovery across academia, business and government. This paper will explore the way the affordances of machine learning itself, and the forms of social apparatus that it becomes a part of, will potentially erode ethics and draw us in to a drone-like perspective. Unconstrained machine learning enables and delimits our knowledge of the world in particular ways: the abstractions and operations of machine learning produce a ‘view from above’ whose consequences for both ethics and legality parallel the dilemmas of drone warfare. The family of machine learning methods is not somehow inherently bad or dangerous, nor does implementing them signal any intent to cause harm. Nevertheless, the machine learning assemblage produces a targeting gaze whose algorithms obfuscate the legality of its judgements, and whose iterations threaten to create both specific injustices and broader states of exception. Given the urgent need to provide some kind of balance before machine learning becomes embedded everywhere, this paper proposes people’s councils as a way to contest machinic judgements and reassert openness and discourse.

How Should Data Science Education Be?

2020 ◽  
Vol 9 (2) ◽  
pp. 25-36
Author(s):  
Necmi Gürsakal ◽  
Ecem Ozkan ◽  
Fırat Melih Yılmaz ◽  
Deniz Oktay
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Science Education ◽  
Data Science ◽  
Doctoral Programs ◽  
Time Data ◽  
High Demand ◽  
The Core ◽  
The World ◽  
The Subject

The interest in data science is increasing in recent years. Data science, including mathematics, statistics, big data, machine learning, and deep learning, can be considered as the intersection of statistics, mathematics and computer science. Although the debate continues about the core area of data science, the subject is a huge hit. Universities have a high demand for data science. They are trying to live up to this demand by opening postgraduate and doctoral programs. Since the subject is a new field, there are significant differences between the programs given by universities in data science. Besides, since the subject is close to statistics, most of the time, data science programs are opened in the statistics departments, and this also causes differences between the programs. In this article, we will summarize the data science education developments in the world and in Turkey specifically and how data science education should be at the graduate level.

scholarly journals Combating Fake News, Misinformation, and Machine Learning Generated Fakes: Insight's from the Islamic Ethical Tradition

ICR Journal ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 189-212
Author(s):  
Talat Zubair ◽  
Amana Raquib ◽  
Junaid Qadir
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
World Wide ◽  
Security And Privacy ◽  
Fake News ◽  
Islamic Ethics ◽  
The World ◽  
Social Media Platforms ◽  
Filter Bubbles

The growing trend of sharing and acquiring news through social media platforms and the World Wide Web has impacted individuals as well as societies, spreading misinformation and disinformation. This trend—along with rapid developments in the field of machine learning, particularly with the emergence of techniques such as deep learning that can be used to generate data—has grave political, social, ethical, security, and privacy implications for society. This paper discusses the technologies that have led to the rise of problems such as fake news articles, filter bubbles, social media bots, and deep-fake videos, and their implications, while providing insights from the Islamic ethical tradition that can aid in mitigating them. We view these technologies and artifacts through the Islamic lens, concluding that they violate the commandment of spreading truth and countering falsehood. We present a set of guidelines, with reference to Qur‘anic and Prophetic teachings and the practices of the early Muslim scholars, on countering deception, putting forward ideas on developing these technologies while keeping Islamic ethics in perspective.

Kim Kardashian: the Leader of the Influencers

2019 ◽  
Author(s):  
Laila Fariha Zein ◽  
Adib Rifqi Setiawan
Keyword(s):  
Social Media ◽  
The United States ◽  
Armenian Genocide ◽  
The Public ◽  
Self Defense ◽  
Global Commerce ◽  
The World ◽  
Social Media Platforms ◽  
International Issues ◽  
Global Influence

In today’s world, it is easier and easier to stay connected with people who are halfway across the world. Social media and a globalizing economy have created new methods of business, trade and socialization resulting in vast amounts of communication and effecting global commerce. Like her or hate her, Kimberly Noel Kardashian West as known as Kim Kardashian has capitalized on social media platforms and the globalizing economy. Kim is known for two things: famous for doing nothing and infamous for a sex tape. But Kim has not let those things define her. With over 105 million Instagram followers and 57 million Twitter followers, Kim has become a major global influence. Kim has travelled around the world, utilizing the success she has had on social media to teach make-up master classes with professional make-up artist, Mario Dedivanovic. She owns or has licensed several different businesses including: an emoji app, a personal app, a gaming app, a cosmetics line, and a fragrance line. Not to be forgotten, the Kardashian family show, ‘Keeping Up with the Kardashians’ has been on the air for ten years with Kim at the forefront. Kim also has three books: ‘Kardashian Konfidential’, ‘Dollhouse’, and ‘Selfish’. With her rising social media following, Kim has used the platforms to show her support for politicians and causes, particularly, recognition of the Armenian genocide. Kim also recently spoke at the Forbes’ women’s summit. Following the summit, Kim tweeted out her support for a recent movement on Twitter, #freeCyntoiaBrown which advocated for a young woman who claimed to have shot and killed the man who held her captive as a teenage sex slave in self-defense. Kim had her own personal lawyers help out Cyntoia on her case. Kim has also moved beyond advocating for issues within the confines of the United States. As mentioned earlier, she is known for advocating for recognition of the Armenian genocide. In the last two years, her show has made it a point to address the Armenian situation as it was then and as it is now. Kim has been recognized as a global influencer by others across the wordl. We believe Kim has become the same as political leaders when it comes to influencing the public. Kim’s story reveals that the new reality creates a perfect opportunity for mass disturbances or for initiating mass support or mass disapproval. Although Kim is typically viewed for her significance to pop culture, Kim’s business and social media following have placed her deep into the mix of international commerce. As her businesses continue to grow and thrive, we may see more of her influence on international issues and an increase in the commerce from which her businesses benefit.

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

A Survey for Predicting Enzyme Family Classes Using Machine Learning Methods

2019 ◽  
Vol 20 (5) ◽  
pp. 540-550 ◽  
Author(s):  
Jiu-Xin Tan ◽  
Hao Lv ◽  
Fang Wang ◽  
Fu-Ying Dao ◽  
Wei Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Catalytic Mechanism ◽  
Biological Function ◽  
Learning Methods ◽  
Biochemical Processes ◽  
Machine Learning Methods ◽  
Enzyme Family ◽  
The Family ◽  
Speed Up ◽  
Family Classification

Enzymes are proteins that act as biological catalysts to speed up cellular biochemical processes. According to their main Enzyme Commission (EC) numbers, enzymes are divided into six categories: EC-1: oxidoreductase; EC-2: transferase; EC-3: hydrolase; EC-4: lyase; EC-5: isomerase and EC-6: synthetase. Different enzymes have different biological functions and acting objects. Therefore, knowing which family an enzyme belongs to can help infer its catalytic mechanism and provide information about the relevant biological function. With the large amount of protein sequences influxing into databanks in the post-genomics age, the annotation of the family for an enzyme is very important. Since the experimental methods are cost ineffective, bioinformatics tool will be a great help for accurately classifying the family of the enzymes. In this review, we summarized the application of machine learning methods in the prediction of enzyme family from different aspects. We hope that this review will provide insights and inspirations for the researches on enzyme family classification.

Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-steps Rule and General Pseudo Components

2019 ◽  
Vol 19 (25) ◽  
pp. 2301-2317 ◽  
Author(s):  
Ruirui Liang ◽  
Jiayang Xie ◽  
Chi Zhang ◽  
Mengying Zhang ◽  
Hai Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Growth Rate ◽  
Big Data ◽  
Human Genome Project ◽  
Genome Project ◽  
Support Vector ◽  
Successful Implementation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Vector Machines

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of ‘big data’ derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

A critical appraisal of diversity in digital knowledge production: Segregated inclusion on YouTube

2021 ◽  
pp. 146144482110348
Author(s):  
Kaiping Chen ◽  
June Jeon ◽  
Yanxi Zhou
Keyword(s):  
Social Media ◽  
Knowledge Production ◽  
Science Communication ◽  
Critical Appraisal ◽  
Digital Platforms ◽  
The Core ◽  
Inclusion Phenomenon ◽  
Media Platform ◽  
The Rich ◽  
Digital Knowledge

Diversity in knowledge production is a core challenge facing science communication. Despite extensive works showing how diversity has been undermined in science communication, little is known about to what extent social media augments or hinders diversity for science communication. This article addresses this gap by examining the profile and network diversities of knowledge producers on a popular social media platform—YouTube. We revealed the pattern of the juxtaposition of inclusiveness and segregation in this digital platform, which we define as “segregated inclusion.” We found that diverse profiles are presented in digital knowledge production. However, the network among these knowledge producers reveals the rich-get-richer effect. At the intersection of profile and network diversities, we found a decrease in the overall profile diversity when we moved toward the center of the core producers. This segregated inclusion phenomenon questions how inequalities in science communication are replicated and amplified in relation to digital platforms.

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