Transparency and Fairness in Machine Learning Applications

Businesses and consumers increasingly use artificial intelligence (“AI”)— and specifically machine learning (“ML”) applications—in their daily work. ML is often used as a tool to help people perform their jobs more efficiently, but increasingly it is becoming a technology that may eventually replace humans in performing certain functions. An AI recently beat humans in a reading comprehension test, and there is an ongoing race to replace human drivers with self-driving cars and trucks. Tomorrow there is the potential for much more—as AI is even learning to build its own AI. As the use of AI technologies continues to expand, and especially as machines begin to act more autonomously with less human intervention, important questions arise about how we can best integrate this new technology into our society, particularly within our legal and compliance frameworks. The questions raised are different from those that we have already addressed with other technologies because AI is different. Most previous technologies functioned as a tool, operated by a person, and for legal purposes we could usually hold that person responsible for actions that resulted from using that tool. For example, an employee who used a computer to send a discriminatory or defamatory email could not have done so without the computer, but the employee would still be held responsible for creating the email. While AI can function as merely a tool, it can also be designed to act after making its own decisions, and in the future, will act even more autonomously. As AI becomes more autonomous, it will be more difficult to determine who—or what—is making decisions and taking actions, and determining the basis and responsibility for those actions. These are the challenges that must be overcome to ensure AI’s integration for legal and compliance purposes.

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The Education of the Human Factors Engineer in the Age of Data Science

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641109 ◽

2020 ◽

Vol 64 (1) ◽

pp. 480-484

Author(s):

Daniel Hannon ◽

Esa Rantanen ◽

Ben Sawyer ◽

Ashley Hughes ◽

Katherine Darveau ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Human Factors ◽

Data Science ◽

Panel Discussion ◽

Quantitative Skills ◽

The Future ◽

Relevant Role ◽

Machine Learning Applications

The continued advances in artificial intelligence and automation through machine learning applications, under the heading of data science, gives reason for pause within the educator community as we consider how to position future human factors engineers to contribute meaningfully in these projects. Do the lessons we learned and now teach regarding automation based on previous generations of technology still apply? What level of DS and ML expertise is needed for a human factors engineer to have a relevant role in the design of future automation? How do we integrate these topics into a field that often has not emphasized quantitative skills? This panel discussion brings together human factors engineers and educators at different stages of their careers to consider how curricula are being adapted to include data science and machine learning, and what the future of human factors education may look like in the coming years.

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Artificial Intelligence in Medicine

Principles of Medical Professionalism ◽

10.1093/med/9780197506226.003.0004 ◽

2021 ◽

pp. 49-68

Author(s):

Gia Merlo

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

New Technologies ◽

New Technology ◽

Technological System ◽

Medical Professionals ◽

Human Intelligence ◽

Connected Health ◽

The Future

Disruptive forces are challenging the future of medicine. One of the key forces bringing change is the development of artificial intelligence (AI). AI is a technological system designed to perform tasks that are commonly associated with human intelligence and ability. Machine learning is a subset of AI, and deep learning is an aspect of machine learning. AI can be categorized as either applied or generalized. Machine learning is key to applied AI; it is dynamic and can become more accurate through processing different results. Other new technologies include blockchain, which allows for the storage of all of patients’ records to create a connected health ecosystem. Medical professionals ought to be willing to accept new technology, while also developing the skills that technology will not be able to replicate.

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Artificial Intelligence Driven Crypto Currencies

Global Disclosure of Economics and Business ◽

10.18034/gdeb.v9i2.557 ◽

2020 ◽

Vol 9 (2) ◽

pp. 107-118

Author(s):

Apoorva Ganapathy ◽

Md. Redwanuzzaman ◽

Md. Mahbubur Rahaman ◽

Wahiduzzaman Khan

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Technological Development ◽

Third Parties ◽

Human Intelligence ◽

Human Intervention ◽

Specific Data ◽

Development Framework ◽

The Future

Artificial intelligence-driven cryptocurrencies are cryptocurrencies created by Artificial intelligence using the traditional human cryptocurrency development framework without human intervention. An AI explores the data from each different stream and arriving at the framework which can host these cryptocurrencies following the standards of legality. Cryptography is the encryption of specific data to conceal it and keep it a secret from unwanted third parties. Cryptocurrencies are encrypted currencies with unique keys as developed by developers. Artificial intelligence is an advanced machine programmed to simulate and emulate human intelligence by carrying tasks and reaching conclusions with little or no human intervention. This work considered the use of AI through machine learning and deep learning in the development of cryptocurrencies. The AI machine will set all the parameters and structure of the cryptocurrency. This will include how data is added, removed, and verified on the stream. Blockchain is an open ledger of a cryptocurrency's transactions. It stores files in the system, arranged in blocks, and connected on a list called chains. The article considers how AI-driven cryptocurrency will run using the blockchain network and its impact on it. Artificial intelligence and cryptocurrency are technological very essential technological development currently. The effect of the combination of both technologies would be enormous in the future as both technologies will develop each other remarkably.

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How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

Journal of Personalized Medicine ◽

10.3390/jpm11010032 ◽

2021 ◽

Vol 11 (1) ◽

pp. 32

Author(s):

Oliwia Koteluk ◽

Adrian Wartecki ◽

Sylwia Mazurek ◽

Iga Kołodziejczak ◽

Andrzej Mackiewicz

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Health Care ◽

Medical Data ◽

General Process ◽

New Era ◽

Automated Evaluation ◽

Machine Learning Applications ◽

And Training ◽

Current Standards

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

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Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

Cancer Cell International ◽

10.1186/s12935-021-01981-1 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Muhammad Javed Iqbal ◽

Zeeshan Javed ◽

Haleema Sadia ◽

Ijaz A. Qureshi ◽

Asma Irshad ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Cancer Diagnosis ◽

Disease Risk ◽

Clinical Applications ◽

Optimal Decision ◽

Great Promise ◽

Time Range ◽

Base System ◽

The Future

AbstractArtificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

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Before and beyond trust: reliance in medical AI

Journal of Medical Ethics ◽

10.1136/medethics-2020-107095 ◽

2021 ◽

pp. medethics-2020-107095

Author(s):

Charalampia (Xaroula) Kerasidou ◽

Angeliki Kerasidou ◽

Monika Buscher ◽

Stephen Wilkinson

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

New Technology ◽

Public Trust ◽

Learning Technologies ◽

Data Driven ◽

Policy Makers ◽

The Public ◽

Regulatory Frameworks ◽

Practice Of Medicine

Artificial intelligence (AI) is changing healthcare and the practice of medicine as data-driven science and machine-learning technologies, in particular, are contributing to a variety of medical and clinical tasks. Such advancements have also raised many questions, especially about public trust. As a response to these concerns there has been a concentrated effort from public bodies, policy-makers and technology companies leading the way in AI to address what is identified as a "public trust deficit". This paper argues that a focus on trust as the basis upon which a relationship between this new technology and the public is built is, at best, ineffective, at worst, inappropriate or even dangerous, as it diverts attention from what is actually needed to actively warrant trust. Instead of agonising about how to facilitate trust, a type of relationship which can leave those trusting vulnerable and exposed, we argue that efforts should be focused on the difficult and dynamic process of ensuring reliance underwritten by strong legal and regulatory frameworks. From there, trust could emerge but not merely as a means to an end. Instead, as something to work in practice towards; that is, the deserved result of an ongoing ethical relationship where there is the appropriate, enforceable and reliable regulatory infrastructure in place for problems, challenges and power asymmetries to be continuously accounted for and appropriately redressed.

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AIoT Application for Food Recommendation based on Emotion Analysis

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-928 ◽

2021 ◽

pp. 44-53

Author(s):

Shivangi Ruhela ◽

Pragati Chaudhary ◽

Rishija Shrivas ◽

Deepti Chopra

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Decision Making ◽

Data Exchange ◽

The Internet ◽

Human Intervention ◽

User Interactions ◽

Emotion Analysis ◽

Efficiency Performance ◽

Analyze Data

Artificial Intelligence(AI) and Internet of Things(IoT) are popular domains in Computer Science. AIoT converges AI and IoT, thereby applying AI into IoT. When ‘things’ are programmed and connected to the Internet, IoT comes into place. But when these IoT systems, can analyze data and have decision-making potential without human intervention, AIoT is achieved. AI powers IoT through Decision-Making and Machine Learning, IoT powers AI through data exchange and connectivity. With the AI’s brain and IoT’s body, the systems can have shot-up efficiency, performance and learning from user interactions. Some studies show that, by 2022, AIoT devices such as drones to save rainforests or fully automated cars, would be ruling the computer industries. The paper discusses AIoT at a greater depth, focuses on few case studies of AIoT for better understanding on practical levels, and lastly, proposes an idea for a model which suggests food through emotion analysis.

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