Dr. O. Aly
Computer Science
The purpose of this discussion is to discuss artificial intelligence and whether it should be used as a tool to support or replace decision makers. The discussion begins with a brief history of artificial intelligence (AI), followed by the foundation of the AI, and the question about AI whether it should be used to support or replace decision makers.
The History of Artificial Intelligence
Artificial intelligence is defined as a computational technique allowing machines to perform cognitive functions such as acting or reacting to input, similar to the way humans do (Patrizio, 2018). The gestation of AI was between the year of 1943 and 1955. The work of Warren McCulloch and Walter Pins (1943) is regarded to be the first work of Artificial Intelligence (AI) (Russell & Norvig, 2016). Their work drew on three sources: knowledge of the underlying physiology and function of neurons in the brain, a formal analysis of propositional logic due, and Turing’s theory of computation (Russell & Norvig, 2016). Hebbian learning is the result of the work from Donald Hebb (1949) who demonstrated a simple updating a rule for modifying the connection strengths between neurons (Russell & Norvig, 2016). The Hebbian theory is still an influential model to this day (Russell & Norvig, 2016).
The birth of AI was in 1956, when John McCarthy, who was another influential figure in AI in Princeton, initiated a project for AI. AI witnessed early enthusiasm, and high expectation from 1952 until 1969 (Russell & Norvig, 2016). AI witnessed a dose of reality between 1966 and 1973. The knowledge-based systems as the key to power began in 1969 until 1979. In 1980 until the present time, the AI became an industry. From 1986 until today, the neural networks are returned. From 1987 until the present, AI adopts the scientific method. The emergence of intelligent agents is developed from 1995 until the present time. The large dataset became available from 2001 until the present. Recent works of AI suggest that the emphasis should be on data and not an algorithm to solve many problems (Russell & Norvig, 2016).
The Foundation of Artificial Intelligence
AI, ideally, takes the best possible action in a situation (Russell & Norvig, 2016). Building an agent that is intelligent is not an easy task and is described as problematic. There are eight foundations for building an intelligent agent. The early philosophers such as Aristotle (400 B.C.) made the AI conceivable by considering the ideas that the mind is in some ways like a machine, that it operates on knowledge encoded in some internal language, and that thought can be used to choose what actions to take (Russell & Norvig, 2016). The mathematics is another block for building an intelligent agent, where mathematician provides the tools to manipulate certain and uncertain statement, as well as probabilistic statements. Mathematics also set the groundwork for understanding computation and reasoning about algorithms (Russell & Norvig, 2016). The economics formalize the problem of making decisions that maximize the expected outcome of the decision makers (Russell & Norvig, 2016). The neuroscience discovered some facts about how the brain works and how it is similar to and different from computers. The computer engineering provided the ever-more-powerful machines that make AI applications possible. The control theory deals with designing devices that act optimally by feedback from the environment (Russell & Norvig, 2016). Understanding language requires an understanding of the subject matter and context, not just an understanding of the structure of sentences, which can cause a problem in AI (Russell & Norvig, 2016).
Can AI Support or Replace Decision-Maker?
AI has already entered various industries such as healthcare (navatiosolutions.com, 2018; UNAB, 2018). It has been used in managing medical records and other data. It has also been used for doing repetitive jobs such as analyzing tests, X-Rays, CT scans, and data entry (navatiosolutions.com, 2018). AI has been used to analyzing data, and reports to help select the correct individually customized treatment path (navatiosolutions.com, 2018). Patients can report their symptoms into an AI app which uses speech recognition to compare against a database of illness. AI acts as virtual nurses to help monitor the conditions of patients and follow up with treatments between doctor visits (navatiosolutions.com, 2018). AI has also been used to monitor the use of medication by a patient. Pharmaceutical has taken advantage of AI in creating drugs faster and cheaper. AI has been used for genetics and genomics for mutations and links to disease from information in DNA (navatiosolutions.com, 2018). AI has been used to sift through the data to highlight mistakes in treatments, workflow inefficiencies, and helps area healthcare systems avoid unnecessary patient hospitalization (navatiosolutions.com, 2018). Other examples of AI’s benefits include the autonomous transport system decreasing the number of accidents, the medical systems making quantum advances possible in health monitoring (UNAB, 2018).
The UNAB think tank (UNAB, 2018) has raised valid questions among which the singularity of human and AI and whether the human and AI can become integrated. AI control of the human with no regard to the human value is causing fears towards AI technology (UNAB, 2018). The other questions include the following (UNAB, 2018):
- “What if AI was wholly monitoring human behavior, without human participation?
- Who or what will be engaged in the related decision-making process?
- To what extent would individuals accept AI despite the consequences?
- Will the human factor as we know it disappears completely?”
These questions are valid questions to fully adopt AI technology and integrate it fully into the human lives. (James, 2018) has raised another valid question “Can We Trust AI?” Despite the benefits of AI especially in the healthcare industry, these systems can still make mistakes, caused by limited training, or unknown bias in the algorithm due to lack of understanding of the neural network models operation (James, 2018). Several high profile instances of machines have demonstrated bias, which caused by wrong training dataset, and a malicious attacker who hacks into the training dataset to make it bias (IBM, n.d.).
Ethics issues come along with AI technology adoption (James, 2018; UNAB, 2018). IBM has suggested instilling human values and morality into AI systems (IBM, n.d.). However, there is no single ethical system for AI (IBM, n.d.). Transparency seems to be a key in trusting AI (IBM, n.d.; James, 2018). People need to know how the AI system arrives at a particular conclusion and make a decision or a recommendation (IBM, n.d.; James, 2018).
Conclusion
This discussion has addressed the artificial intelligence and its key dimension in human life. It has contributed to various industries including healthcare and pharmaceutical and proven to provide value in certain areas. However, it is also proven to make mistakes and demonstrated bias due to wrong training data set or malicious attacks. There is a fear about integrating AI technology fully into human lives with no regard to human’s participation and human’s values. Integrating values and ethics is not an easy task.
From the researcher point of view, AI should not be used for making decisions that are related to human values and ethics. Human lives have many dimensions that are not always black and white. There are some areas where human integrity, principles, values, and ethics play a role. In the court, there is always a statement of “benefit of the doubt.” Can AI decision be based on the “benefit of the doubt” rule in the court? Another aspect of AI, from the researcher’s point of view, is: who develops AI? The AI technology is developed by humans. Are humans trying to get rid of humans and put AI in a superior role? AI technology has its role and its dimension in certain fields but not in all fields and domains where a human can move and interact with other humans with integrity and values. Let AI technology take place and make decisions in areas where it is proven to be most useful to human such as promoting sales and marketing, automating certain processes to increase efficiency, and productivity, etc. Let the human takes place and makes decisions in areas where it is proven to be most useful to human lives promoting ethics, values, integrity, and principles. “Computers are becoming great assistants to us however they still need our thought to make good decisions” (Chibuk, 2018).
References
Chibuk, J. D. (2018). Four Building Blocks for a General AI.
IBM. (n.d.). Building Trust in AI. Retrieved from https://www.ibm.com/watson/advantage-reports/future-of-artificial-intelligence/building-trust-in-ai.html.
James, R. (2018). Can We Trust AI? Retrieved from https://www.electronicdesign.com/industrial-automation/can-we-trust-ai.
navatiosolutions.com. (2018). 10 Common Applications of Artificial Intelligence in Healthcare. Retrieved from https://novatiosolutions.com/10-common-applications-artificial-intelligence-healthcare/.
Patrizio, A. (2018). Big Data vs. Artificial Intelligence.
Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach: Malaysia; Pearson Education Limited.
UNAB. (2018). Human Decision Thoughts On AI. Retrieved from http://unesdoc.unesco.org/images/0026/002615/261563E.pdf, United Nations Educational, Scientificn and Cultrual Organization.
Think and Knowledge Tank 