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

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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Transparency and Fairness in Machine Learning Applications

10.37419/jpl.v4.i5.2 ◽

2018 ◽

Vol 4 (5) ◽

pp. 443-463

Author(s):

Jim Shook ◽

Robyn Smith ◽

Alex Antonio

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Reading Comprehension ◽

New Technology ◽

Human Intervention ◽

Daily Work ◽

Comprehension Test ◽

The Future ◽

Machine Learning Applications ◽

Self Driving Cars

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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Ethics Education of Human Factors Engineers for Responsible AI Development

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181321651038 ◽

2021 ◽

Vol 65 (1) ◽

pp. 1034-1038

Author(s):

Esa M. Rantanen ◽

John D. Lee ◽

Katherine Darveau ◽

Dave B. Miller ◽

James Intriligator ◽

...

Keyword(s):

Artificial Intelligence ◽

Human Factors ◽

Data Science ◽

Ethics Education ◽

Panel Discussion ◽

Technical Skills ◽

The Past ◽

Human Factors And Ergonomics ◽

Educational Challenges ◽

Societal Impacts

This panel discussion is third in a series examining the educational challenges facing future human factors and ergonomics professionals. The past two panels have focused on training of technical skills in data science, machine learning, and artiﬁcial intelligence to human factors students. This panel discussion expands on these topics and argues for a need of new and broader training curricula that include ethics for responsible development of AI-based systems that will touch lives of everybody and have widespread societal impacts.

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Digitalism and Jobs of the Future

10.4018/978-1-7998-8169-8.ch001 ◽

2022 ◽

pp. 1-20

Author(s):

Gamze Sart ◽

Orkun Yildiz

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Data Science ◽

Strong Relationship ◽

New Order ◽

Occupational Groups ◽

The Future ◽

And Robotics ◽

Learning Data

There has been a strong relationship between digitalism and the future of jobs. Reports by OECD and WEF examined the jobs in the coming decades, and the findings show that there is a completely new order in the professions that we are not familiar with. In addition, how the impacts of artificial intelligence (AI), machine learning, data science, and robotics have affected labour, the market is analyzed. The findings in the reports clearly would affect the careers of the next generations. With the post-pandemic developments and the rapid advancement of technology in many areas worldwide, digitalization has gained significant momentum. This situation manifested itself in professions and workforce. However, it is obvious that in the coming years, with digitalization, many occupational groups and accordingly, differences in skills will be seen. While some occupational groups disappear completely, it is seen that some new occupational groups will emerge and, some will transform.

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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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Enabling Machine Learning Applications in Data Science

10.1007/978-981-33-6129-4 ◽

2021 ◽

Keyword(s):

Machine Learning ◽

Data Science ◽

Machine Learning Applications

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A Comparison of Rule-Based and Machine Learning Models for Classification of Human Factors Aviation Safety Event Reports

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641034 ◽

2020 ◽

Vol 64 (1) ◽

pp. 129-133

Author(s):

Katherine Darveau ◽

Daniel Hannon ◽

Chad Foster

Keyword(s):

Machine Learning ◽

Human Factors ◽

Human Error ◽

Data Science ◽

Aircraft Engine ◽

Rule Based ◽

Root Cause ◽

Textual Data ◽

Safety Event

There is growing interest in the study and practice of applying data science (DS) and machine learning (ML) to automate decision making in safety-critical industries. As an alternative or augmentation to human review, there are opportunities to explore these methods for classifying aviation operational events by root cause. This study seeks to apply a thoughtful approach to design, compare, and combine rule-based and ML techniques to classify events caused by human error in aircraft/engine assembly, maintenance or operation. Event reports contain a combination of continuous parameters, unstructured text entries, and categorical selections. A Human Factors approach to classifier development prioritizes the evaluation of distinct data features and entry methods to improve modeling. Findings, including the performance of tested models, led to recommendations for the design of textual data collection systems and classification approaches.

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