Ashford University Computer Science Ethical Implications of AI in Organizations Paper

For this assignment, you must write a paper that discusses the ethical implications of AI in organizations and society at large. Consider their main challenges with respect to fairness and security and the result of exploiting the capabilities of AI for harm. Make sure you provide a definition of fairness and how it should be applied to machines that make decisions based on user experience, cultural, social, legal, and ethical tradeoffs.

You should provide examples of AI technologies in your paper that adhere to the previous considerations. For example, how would an AI system that makes approval recommendations for loans fairly treat different social groups and individuals? How will AI define the boundaries of a group and how will decide if a group is disadvantaged and special considerations should be made? How can the developers of AI systems ensure their systems are inclusive and “respectful” of diversity? How can potential conscious and unconscious human biases be eliminated from training sets and models so that predictions are reliable, and decisions are objective and fair?

Natural Language Processing
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The Organization
Zandan Inc. is a small technology company that deals with the development,
maintenance, and implementation of enterprise resource planning (ERP) financial applications.
The primary mission of the company is to ensure that small and medium business owners are
able to deliver effective and efficient services while ensuring the minimization of expenses and
maximization of profit. With the vast approach and adoption of AI in various markets, Zandan
Inc. can benefit from investing and implementing NLP in its departments to ensure a boost in
business operations and better delivery of products and services to customers. This report
provides a detailed description of NLP and its importance in a business.
Technical description of NLP
Natural Language Processing (NLP), is a branch of Artificial Intelligence (AI) in
computer science, that is involved in providing computers with the ability to comprehend
spoken words and written texts in the same way as humans can (Chowdhary & Chowdhary,
2020). This branch combines various frameworks such as deep learning, machine learning, and
statistical models with the computation linguistics models (Chowdhary & Chowdhary, 2020).
Ideally, these frameworks and technologies work together to enable the human-computer to
achieve a proper basis for processing the human language.
The human language is known to be filled with ambiguities that can make it difficult to
write software that can deliver the intended meaning of the text or the voice. As such, there are
several tasks assigned to the NLP that can make it easier for the computer to understand and
process the intended message from human texts and voices. One of the most basic tasks
associated with NLP is speech recognition (Chowdhary & Chowdhary, 2020). Ideally, speech
recognition is required in various web and mobile applications. As such, NLP can make it
easier to understand the speeches irrespective of the various challenging features such as
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slurring words, different accents, intonations, and even incorrect grammar. Speech tagging is
another crucial responsibility of the NLP. Ideally, speech tagging entails a basis whereby the
NLP identifies a part of the speech based on the use and context (Nadkarni, Ohno-Machado, &
Chapman, 2011). Besides, the NLP selects a range of words with multiple meanings and creates
a systematic analysis to process and determine the intense sense of the word context. The NLP
also performs sentiment analysis in an attempt to determine and understand the subjective
analysis in a text.
Resources required for its implementation
The implementation of NLP can be achieved through the existence of various resources.
One of the most essential resources is a development environment. For example, the existence
of a Python IDE such as Jupyter Notebook can help in the implementation of NLP processing.
A development environment is used in establishing and formulating the code that is used in the
sentimental analysis. The existence of data is another resource that is required for the
implementation of the NLP. Essentially, data is needed for the performance of the NLP
processing. This information can be human texts or speeches to ensure that the computer will
be able to process the human language.
Operational benefits for the various organizational departments
NLP presents a number of benefits to various organizational departments. One of the
departments that can benefit from the NLP is the sales department. Ideally, the sales manager
and the subordinates can use toe the NLP to examine the sentiments of the customers regarding
the company productions. With the analysis of these sentiments, the sales department can make
decisions that are more informed across their sales operations hence improving the sales and
marketing of products in the organization (Behera, Bala, Rana, & Irani, 2023). Ideally, the sales
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department can use NLP to analyze the available customer information hence boosting the
marketing and the sale experience in the organization.
Another department that can benefit from NLP is the finance department. Essentially,
the finance department, in the organization, can use sentiment analysis to analyze the data
regarding the financial performances of various businesses in the market. Essentially, the
finance department can be able to process these sentimental analyses and make informed
decisions hence reducing risks (Behera, Bala, Rana, & Irani, 2023). For example, the finance
department can use NLP to note and prevent any possible financial fraudulent operations that
may trigger liabilities in the company.
Besides, the human resource department in the organization can also benefit from the
insights offered by NLP. Employees are an important asset to an organization. Essentially, the
performance and productivity of employees play an essential role in the general performance
of the company (Chowdhary & Chowdhary, 2020). As such, there is a need to establish a proper
engagement strategy that can ensure that the working environment in a company is
accommodative and enables the employees to work towards maximum production. The human
resources department can use NLP to gain insights regarding how the employees feel in the
current environment; identify any risk of employee exit and even possible existing patterns that
can help in improving employee motivation.
The purchasing and supplies department can also benefit from the NLP framework.
Essentially, purchasing and supply chain is a crucial part of the organization that ensures that
the company can acquire any needed products and services while ensuring transactions
between the company and suppliers. Following the importance of this department, the NLP can
play an essential role in ensuring improvement in the operations that boost productivity level
(Chowdhary & Chowdhary, 2020). Ideally, the leaders and managers in the supplies and chain
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can implement the NLP to gain insights that can help in mitigating any possible risks that may
undermine the operations in the department. Besides, the purchasing and supply department
can use NLP to ensure that its operations are within the ethical and legal boundaries and any
potential breaches with the current and future stakeholders are prevented.
Challenges and issues that might arise from its implementation
Similar to most technologies, the implementation of the NLP is not without issues and
challenges. One of the challenges of NLP relates to irony and sarcasm. Ideally, irony and
sarcasm can cause problems for machine learning algorithms due to the usage of positive and
negative words that have opposite meanings. The existence of errors in speeches and texts is
another possible problem that can be encountered following the implementation of NLP. While
the autocorrect feature can be useful, it may be difficult to obtain the accurate intention or
meaning of the writer. Besides, the different accents and mispronunciations can also make it
difficult to obtain the accurate meaning of a text or speech when using NLP. Slang can also
raise problems during sentimental analysis as it can prevent the accurate identification and
interpretation of words and speeches.
The future potential of the technology as an organizational asset
NLP has good potential as an organizational asset in Zandan Inc. Ideally, this
technology can be implemented to help ensure that the products and services offered by the
company can reach the target consumers (Nadkarni, Ohno-Machado, & Chapman, 2011).
Besides, the NLP can play an essential role to ensure better operations in the organization by
enabling a proper understanding of both the employees and the customer’s needs (Chowdhary
& Chowdhary, 2020). For example, the company will be able to use NLP to identify any
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concerns that may risk a positive working environment or any issues that may contribute to the
loss of customers.
The potential of the technology will rely on a number of factors. One of the factors is
the availability of existing talent who understand the implementation and usage of the NLP in
different business processes. This factor can also help the company determine the amount of
training funds that would be required to ensure that the existing employees can be
understanding the NLP processing and implementation. Another factor that would determine
the existence of the framework as part of future technology is the acceptance and support from
the top management. Ideally, the rejection and lack of support from the management team in
the company would mean that the NLP would not be implemented and used in the processing
of information. As such, support from the top management team would play an essential role
in ensuring that NLP is a technological asset in the future.
Real-life examples of how other organizations have used NLP
One of the organizations that use NLP in the current technological environment is
Amazon. This company uses NLP to gain insights into the various contents of documents.
Essentially, this company has used NLP when creating new products. For example, Amazon
Comprehend is a product of NLP that allows consumers to search for products or scan
documentation. Microsoft is another organization that uses NLP for the delivery of quality
services and products to customers. The most identifiable NLP component in the company is
MS Azure. Ideally, Microsoft customers can use this engine to build intelligent applications
using REST APIs and client libraries.
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References
Behera, R. K., Bala, P. K., Rana, N. P., & Irani, Z. (2023). Responsible natural language
processing: A principlist framework for social benefits. Technological Forecasting
and Social Change, 188, 122306.
Chowdhary, K., & Chowdhary, K. R. (2020). Natural language processing. Fundamentals of
artificial intelligence, 603-649.
Nadkarni, P. M., Ohno-Machado, L., & Chapman, W. W. (2011). Natural language
processing: an introduction. Journal of the American Medical Informatics
Association, 18(5), 544-551.
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Neural Network application Guide
Student’s Name
Institutional affiliation
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Introduction
Neural networks are robust and adaptable AI mechanisms that simulate human neurons’
functioning in a digital form, allowing computers to learn and improve through experience.
These networks’ pattern recognition, data interpretation, and prediction capabilities have broad
applications for enterprises in many fields. RapidMiner, a powerful data science platform that
has made significant contributions to machine learning, has been chosen for this task. It
provides a straightforward interface and a wealth of features and options for implementing
sophisticated machine learning algorithms like neural networks. We will go deep into
RapidMiner’s capabilities by following a YouTube video on the implementation of neural
networks with the help of an example dataset provided by the program.
Step by Step Tutorial
The first Step is to open the Rapid Minor Studio, and my version is 10.1 version as shown in
the screenshot below:
To start working on the application click the blank process, and then collect the data by using
the sample data set. To use the sample data set, click Samples then data, and since the tutorial
was using “iris” dataset that was the one that was used.
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Then drag the dataset into the workspace to look like the screenshot below:
To verify and check whether the data has been collected the output of the data set is connected
to the res of the model by dragging a line all the way to the “res” feature hen click the execution
button for the design to appear like the screenshot below:
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Then click the results button located at the top to view the table records at the data section and
you can also view the data type and other features like charts at the statistic section. Below is
the data records and charts per attribute screenshot:
Data records:
Charts:
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After the verification of the data collection, one should disconnect the dataset to the “res”. The
following is preprocessing but the sample data is already cleaned such that there are no
duplicate entries and null records, I moved to the next step which was the creation of the Neural
Network Model.
The first step in the creation of the network is the addition of the cross-validation operator (you
can find it by searching it on the operator search bar), which will be connected to the data set
as per the screenshot below:
The following is to double click the cross-validation operator such that it looks like the
screenshot below:
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The following is adding the Neural Net operator and drag it in the Training Section, and create
a connection from the “tra” to the “tra” of the Neural operator and another connection from the
“mod” of the Neural Net operator to the “mod” in the training section.
While on the testing region drag the Apply Model Operator and connect the neural “mod” to
the apply “mod” and then the “test” to the “unl” of the apply model. Such that the screenshot
should look like this:
To check the performance of the model will need to add the performance Classification by
connecting the “lab” of the apply model
Current Technologies and the Role of AI
Student’s Name
Institutional Affiliation
Introduction
Self-driving automobiles, or autonomous vehicles, represent the pinnacle of how
artificial intelligence (AI) is changing the transportation industry. They use a wide range of
high-tech resources, including artificial intelligence (AI), to analyze their surroundings and act
accordingly, as their name implies.
It is impossible to overestimate the importance of driverless cars in the modern world.
They have the potential to radically revolutionize many aspects of society, including
transportation, urban design, energy use, and accessibility. Increased safety, enhanced
economy, and maybe decreased traffic congestion and pollutants are possible benefits of
incorporating AI into these cars.
This article aims to present a comprehensive analysis of the current status of
autonomous vehicle technology, focusing on the part that AI plays and its effects on the
industry. The structure follows: First, we will examine the big picture of self-driving
automobiles and the related technology. Following this, the AI-enabled features of these
automobiles will be examined. Next, we’ll look at where the industry is now and present the
main players pushing towards autonomous vehicles. In the end, we will review the results and
discuss the potential of AI in driverless cars.
Overview of Autonomous Cars
Self-driving cars, also known as autonomous vehicles, are automobiles that can go from
one place to another without the assistance of a human driver by using a system of sensors,
algorithms, and machine learning. They are programmed to function as a human driver would
by sensing their surroundings, making judgments, and carrying them out. Accelerating,
decelerating, and directing, as well as reading and responding to road signals and avoiding
collisions with other cars and pedestrians, are all examples of such maneuvers. In the 1920s,
the idea of driverless automobiles was first presented in the Futurama exhibit at the 1939 New
York World’s Fair, marking the beginning of the long road to its eventual realization. But
autonomous car technology has taken off in the last several decades, with advances in
computing power, sensor technology, and AI (Dignum, 2021). In particular, a new age in
transportation was ushered in by the late-2000s exploration and investment in autonomous car
technology by businesses like Google and Tesla. Different degrees of autonomy, as defined by
the Society of Automotive Engineers (SAE), reflect different stages in the development of
autonomous cars. The levels are a continuous scale from 0 (no automation) to 5 (complete
automation). Level 2 and 3 autonomous cars are now the most common on the market. These
vehicles have some degree of autonomy in steering and acceleration/deceleration, but a human
driver must always be present to take over if necessary.
Building and maintaining autonomous cars requires substantial amounts of artificial
intelligence. Vehicles are able to detect their surroundings, form opinions based on that data,
and securely maneuver themselves thanks to this engine (Vaishya et al., 2020). With its builtin AI, the car is able to reflect on its past, modify its behavior in response to its surroundings,
and avoid or rectify problems as they arise. The system is able to do this because it uses
information from a variety of sensors, including cameras, LiDAR (Light Detection and
Ranging), and radar, to form a complete image of the area around the car and make sound
judgments.
An autonomous vehicle relies on AI to perform tasks such as object recognition, route
planning, and decision making. It can detect and prevent collisions with barriers including other
cars, pedestrians, and bicycles by anticipating their actions. Artificial intelligence allows the
vehicle to interpret traffic signs, recognize and avoid potential dangers, and adhere to all
applicable regulations. With AI’s ability to learn and develop, the car’s algorithms might be
fine-tuned over time as the system gathers more information and experiences.
Technologies Involved in the Development of Autonomous Cars
The advancement of completely autonomous cars is dependent on the application of AI.
These autonomous cars utilize machine learning and deep learning algorithms to learn from
their experiences and make data-driven judgments. Autonomous cars use AI for more than just
decision-making; the technology is also used for perception, scenario prediction, and
instantaneous response.
Artificial intelligence creates an accurate and thorough picture of the environment
surrounding the vehicle by combining data from several sensors and computers. Major sensors
include LiDAR, radar, and AI-powered cameras. Using a pulsed laser and light, light detection
and ranging (LiDAR) may be used for remote sensing (Vaishya et al., 2020). Autonomous cars
use LiDAR to generate precise three-dimensional maps of their surrounds. Through the
reflection of millions of light pulses per second off the road, buildings, and other vehicles,
LiDAR is able to create a high-resolution map of the area.
Radio Detection and Ranging (radar) is a technique that utilizes radio waves to
determine speed and distance. Despite radar’s superior map-making capabilities, light detection
and ranging (LiDAR) has useful uses in low-visibility conditions. This set-up guarantees that
the automobile has a complete understanding of its environment. Vinuesa et al. (2020) note that
computer vision technologies are also essential for autonomous cars. The system use camera
technology to take snapshots, which are then analyzed by AI for the presence of human beings,
traffic signs, and other cars. Combining these two fields allows an autonomous vehicle to
interpret visual data much like a human driver.
While each of these innovations is beneficial on its own, the benefits of combining them
might be enormous. Autonomous cars may benefit from a more precise and complete picture
of their surroundings if they were equipped with sensors like LiDAR, radar, and computer
vision. Sensor fusion is necessary for the vehicle to operate at peak efficiency and without
incident (Vinuesa et al., 2020). This will make the car more resilient to sudden events and easier
to drive in congested areas. Artificial intelligence is also used to control and monitor the
collaboration of many technologies. An autonomous vehicle’s AI may draw conclusions about
its environment, make predictions about what could happen next, and choose the most
appropriate response thanks to sensor fusion data. They evolve throughout time, picking up on
context clues that allow them to refine their predictions and assessments.
Functions Supported by AI in Autonomous Vehicles
AI in autonomous cars enables several features to enhance rider security, productivity,
and comfort. AI’s primary role is in navigating its environment. AI systems can determine the
most efficient routes for the car by combining sensor data with complex algorithms. AI can
optimize trip time and passenger safety by continually assessing real-time data from GPS and
onboard sensors in reaction to changing traffic patterns, road closures, or accidents. The ability
of an autonomous vehicle to recognize and identify objects is essential to its ability to navigate
its environment safely (Yu & Helwig, 2022). When fed sensor data, AI can distinguish between
cars, pedestrians, bicycles, traffic signs, and barriers. Artificial intelligence can also calculate
the rate and course of moving objects, which may be used for risk assessment and preventative
measures.
AI is increasingly being used in the decision-making process. The analyzed sensor data
is used to make decisions such as speed regulation, lane changes, turns, and when to stop. These
judgments are made now, usually in tenths of a second or less, to keep the vehicle running
smoothly and safely. Importantly, the AI constantly improves its driving performance by
adjusting its judgements based on the data it has accumulated and its experiences. AI greatly
impacts the security and convenience of autonomous cars for their passengers (Yu & Helwig,
2022). The primary force behind these safety advances is the machine’s capacity to remain alert
at all times, respond swiftly to threats, and improve via the analysis of massive volumes of
data. The ability of AI to anticipate and adjust to changing traffic situations, together with its
pinpoint navigation and control, makes for a more relaxed and stress-free journey.
The future applications of AI in self-driving cars are wide-ranging. Better
comprehension and prediction of complicated traffic conditions may be possible with the help
of AI-developed prediction models. It might also pave the way for improved V2V and V2I
communication, improving traffic flow and allowing vehicles to make decisions together. In
addition, AI may be used to tailor the in-car experience to each passenger’s unique tastes and
requirements.
Current State of the Autonomous Vehicle Industry
Over the last ten years, the autonomous vehicle sector has made great achievements
thanks to the development of AI, sensor technologies, and machine learning. Waymo, Tesla,
Uber, and many others are spending billions on R&D, steadily improving levels of autonomous
driving. A large number of vehicles are being used as test beds, and in certain places,
autonomous taxis and delivery vans are already in use.
According to the Society of Automotive Engineers (SAE) definition of autonomy, most
self-driving cars now operate at either Level 2 or 3. Despite the vehicle’s ability to steer and
accelerate/decelerate on its own, a human driver is still required at all times (Vinuesa et al.,
2020). The highly automated Level 4 and fully automated Level 5 are still in the research and
development phase.
Despite these improvements, the autonomous car sector still confronts several
significant obstacles. Although advancements in sensor technology and artificial intelligence
have been tremendous, more work is required. Among them is the creation of fail-safe
mechanisms to deal with possible faults and guarantee dependable operation across various
situations, including severe weather and difficult traffic scenarios. Regulatory issues also pose
substantial obstacles (Vinuesa et al., 2020). Local regulations on self-driving cars are constantly
shifting as governments try to make sense of this emerging technology. Companies have
challenges in adapting to the varying regulations in various markets due to the absence of a
common legal framework for autonomous cars.
The support of the public is also essential. Many individuals are still wary of giving up
control over a computer, despite studies showing rising interest in autonomous cars.
Widespread public adoption requires resolving concerns regarding safety, security, and privacy.
The “trolley problem” is a common metaphor for the ethical questions raised by AI decisionmaking in the event of an inevitable accident involving an autonomous vehicle. It is an ongoing
difficulty for engineers, ethicists, attorneys, and politicians to agree on how to write code for
autonomous cars to handle such scenarios.
However, researchers, policymakers, and concerned citizens are working tirelessly to
find solutions to these problems. It is not a matter of “if” but “when” these obstacles will be
solved, according to many experts in the field, allowing for the widespread use of autonomous
cars. Although significant strides have been achieved, the autonomous vehicle sector is still
developing. The advantages of autonomous cars — regarding safety, efficiency, and
accessibility — fuel continued attempts to tackle the industry’s complex technical, legislative,
and social problems. The future of the autonomous car business is bright as AI and associated
technologies continue to improve.
Organizations Involved in the Development of Autonomous Cars
The development of autonomous cars is being pushed further by many major companies
in the technology and automotive sectors.
Waymo, a division of Google’s parent firm Alphabet Inc., was an early innovator in this
field. Their self-driving taxis are now in use in several parts of the United States, and they have
logged millions of kilometres of autonomous driving on public roadways (Dignum, 2021). One
of the electric vehicle market leaders, Tesla, has included a technology dubbed “Autopilot” in
its vehicles. Although it is not entirely autonomous, this smart driver-assistance technology
shows the future for individual driverless cars.
Among the other major donors are Uber’s Advanced Technology Group, which is
working on autonomous driving technology, and GM’s subsidiary Cruise, creating a vehicle
named the “Cruise Origin.” Ford, BMW, and Mercedes-Benz are just a few more conventional
manufacturers pouring resources into autonomous driving (Dignum, 2021). These groups’
R&D efforts have achieved substantial advancements in autonomous vehicle technology. As a
result of their tireless efforts, artificial intelligence (AI), sensor technology, machine learning,
and the general public’s acceptance of autonomous cars have all advanced significantly.
Conclusion
In conclusion, the arrival of autonomous vehicles marks a revolutionary confluence of
artificial intelligence and transportation, with potentially far-reaching consequences for society
at large. Although there are still some bumps regarding technological excellence, regulatory
compliance, and public acceptability, progress is being made rapidly.
Machine learning, computer vision, and sensor fusion are just a few examples of the
artificial intelligence (AI) technologies that underlie autonomous cars and are boosting the
autonomous vehicle business and propelling a larger transformation across several industries.
As they are improved, the scope of applications for autonomous cars will grow, affecting more
than just the transportation sector.
Thinking about where we are now and where AI in autonomous cars can go, it’s clear
that we’re on the cusp of profound cultural upheaval. The development of truly autonomous
cars has great potential as technology, society, and politics continue to cope with the advent of
this new means of transportation.
References
Dignum, V. (2021). The role and challenges of education for responsible AI. London Review of
Education, 19(1), 1-11.
Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020). Artificial Intelligence (AI)
applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical
Research & Reviews, 14(4), 337-339.
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., … & Fuso Nerini,
F. (2020). The role of artificial intelligence in achieving the Sustainable Development
Goals. Nature communications, 11(1), 233.
Yu, C., & Helwig, E. J. (2022). The role of AI technology in prediction, diagnosis and treatment
of colorectal cancer. Artificial Intelligence Review, 1-21.
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Analysis of the Current Status of Research and Practice on Intelligent Agents’
Technologies
Student’s Name
Institution Affiliation
Date
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Introduction
Technological advancements have always served as catalysts for societal change, mostly
positive but also negative. Even in ancient times, simple technological advancements improved
the efficiency and effectiveness of various tasks. Unlike in these times, the technological
advancements introduced into today’s society are far more advanced and have much broader
applications. Intelligent agents are one such technological advancement (Elshan et al., 2022).
The main goal underlying the ongoing development and advancement of intelligent agents’
technologies is creating autonomous systems capable of perceiving, reasoning, and acting in
dynamic and complicated environments (Elshan et al., 2022). These agents have been subjected
to extensive research and practice, with major advancements in recent years. As such, this paper
will analyze the status of research and practice on intelligent agents’ technologies.
I. Definitions and Terminology
Understanding what intelligent agents’ technologies are, including the various
terminologies associated with this technological phenomenon, is critical. This will provide
invaluable foundational knowledge, making it easy to comprehend the analysis and discussion in
the subsequent sections of the paper. Intelligent agents’ technologies are computer systems or
programs designed to perceive the environment, make reasonable assumptions and deductions
about it, and then take actions to attain specific goals (Elshan et al., 2022). Intelligent agents
simulate human-like cognitive capabilities, like decision-making, learning, and problem-solving
(Elshan et al., 2022). These agents leverage various technologies to support their intelligent
behavior. They often employ artificial intelligence techniques like natural language processing,
robotics, machine learning, computer vision, and expert systems to undertake their tasks (Elshan
et al., 2022).
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II. Theoretical and Practical Perspectives
Intelligent agents’ technologies have wide theoretical and practical perspectives. In
addition, these two perspectives are interlinked, with theoretical perspectives influencing
practical perspectives and vice versa (Gams et al., 2019). Essentially, this means that theoretical
research on intelligent agents’ technologies expands people’s comprehension of intelligence,
cognition, and emergent behavior (Gams et al., 2019). Furthermore, practical applications of
intelligent agents’ technologies are informed by this theoretical research, pushing the boundaries
of what these agents can fulfill within real-world concepts (Gams et al., 2019). These practical
perspectives then inform further theoretical research into intelligent agents’ technologies. Even
though the two perspectives are intertwined, they will be discussed separately below.
(a). Theoretical Perspectives
These agents are heavily dependent on artificial intelligence. AI generally focuses on the
development of algorithms and models that mimic human intelligence and cognition (Gams et
al., 2019). AI-based theoretical research focuses on areas such as natural language processing,
robotics, machine learning, computer vision, and expert systems (Gams et al., 2019). These AI
techniques allow intelligent agents to learn from the data fed to them, understand it, and
subsequently produce human-like language, interpret visual data, and make informed decisions.
Cognitive science is another core theoretical perspective associated with these agents
(Dellermann et al., 2019). Comprehending how the human mind operates has always been a core
aspect of developing these agents. Such study is typically localized within the cognitive science
field. This field generally offers valuable insights into areas like memory, learning, reasoning,
perception, as well as problem-solving (Dellermann et al., 2019). As such, theoretical
perspectives informed by cognitive science help design intelligent agents’ technologies
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replicating human-like cognitive processes and conduct (Dellermann et al., 2019). This makes
the agents more effective at interacting with people and completing assigned tasks. The
theoretical foundation underpinning these agents is predominantly informed by agent-based
modeling. This interdisciplinary collaboration is a byproduct of combining elements of computer
science, sociology, economics, and complex systems theory (Dellermann et al., 2019). Agentbased models mimic interactions between individual agents to comprehend emergent phenomena
as well as complicated systems. Finally, intelligent agents’ technologies leverage decision theory
to inform their rational choices in dynamic and uncertain environments (Dellermann et al.,
2019). This allows the agents to consider various probabilities, goals, and utilities, optimizing
their actions to maximize the anticipated outcomes. Decision theory also informs how the agents
deal with challenges, such as trade-offs between conflicting goals and risk management.
(b). Practical Perspectives
Intelligent agents have a broad array of practical perspectives. One of them is robotics
(Dellermann et al., 2019). Robotics often leverage these agents to allow autonomous systems to
perceive and interact with their physical environment. This practical application relies on various
technologies, including sensor fusion, computer vision, control systems, and motion planning
(Dellermann et al., 2019). These autonomous systems undertake various tasks, including
surveillance, human aid, manufacturing, and exploration. Despite this, there are some practical
challenges in robotics. These include issues with locomotion, perception, making decisions
during times of uncertainty, and manipulation (Dellermann et al., 2019). Virtual assistants are
another practical perspective associated with intelligent agents’ technologies. These agents
support various virtual assistants such as Google Assistant, Siri, and Alexa (Dellermann et al.,
2019). These agents employ machine learning and natural language processing to comprehend
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and accurately respond to user questions, undertake tasks, and share pertinent information.
Practical considerations of intelligent agents within this area include language comprehension,
speech recognition, context awareness, as well as integration with other services (Dellermann et
al., 2019). Autonomous vehicles are another practical perspective. These agents play a vital role
in the operation of self-driving vehicles. They rely on perception algorithms, control
mechanisms, sensors, and planning systems to move around within complicated and dynamic
environments (Dellermann et al., 2019). Despite these advances, practical challenges are
undermining the use of these agents in this area. These challenges include poor decision-making
when faced with dynamic situations, real-time perception, as well as guaranteeing reliability and
passenger safety (Dellermann et al., 2019). Recommender systems are also another practical
perspective associated with intelligent agents’ technologies. These agents play a vital role in the
operation of recommender systems. These systems offer individualized recommendations in
various areas like social media, e-commerce, and streaming (Dellermann et al., 2019). The
intelligent agents will analyze a user’s preferences, historical data, and contextual data to come
up with suggestions relevant to the user. Other practical aspects of this area include contentoriented filtering, incorporating feedback from users, and collaborative filtering (Dellermann et
al., 2019). Multi-agent systems are another practical perspective of intelligent agents’
technologies. Practical applications within this area frequently involve multiple intelligent agents
interacting and collaborating. Despite these advances, practical challenges are undermining the
use of these agents in this area. These challenges include coordination, communication,
negotiation, as well as guaranteeing overall system efficiency (Dellermann et al., 2019). These
practical and theoretical perspectives showcase the wide range of technologies and applications
linked to intelligent agents. Furthermore, as AI-based research and development advances,
6
people should expect further theoretical and practical innovations and advancements in
intelligent agents’ technologies.
III. Technical Characteristics and Supporting Technologies
As mentioned earlier in the paper, intelligent agents are software systems designed to
make decisions or complete tasks for users or organizations. These agents often leverage various
technical attributes and supporting technologies to operationalize their intelligent conduct
(Martynov et al., 2019). Autonomy is one of these technical attributes. These agents can operate
independently, taking actions and making decisions without ongoing human intervention
(Martynov et al., 2019). They have some level of autonomy, especially when it comes to goal
attainment and solving problems. Learning and adapting is another key technical attribute
(Martynov et al., 2019). These agents are designed to gather knowledge and enhance their
performance as time passes via learning and adaptation. They analyze information, identify
hidden patterns in the data, and then adjust their prior behavior based on feedback, changing
environments, and experience (Martynov et al., 2019). Several supporting technologies are
leveraged to bring out these attributes, including deep learning, machine learning, reinforcement
learning, genetic algorithms, and neural networks (Martynov et al., 2019).
Knowledge representation and reasoning is another key technical attribute. Intelligent
agents rely on formal models for knowledge representation and manipulation. Due to this,
intelligent agents can interact with the knowledge to facilitate decision-making, problem-solving,
and explaining their actions (Martynov et al., 2019). Several supporting technologies are
leveraged to bring out these attributes, including semantic networks, inference engines,
ontologies, logic programming, and knowledge graphs (Martynov et al., 2019). Natural language
processing (NLP) is another core technical characteristic. This attribute allows intelligent agents
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to comprehend and reproduce human language, facilitating better interactions with users via
natural language interfaces (Martynov et al., 2019). Furthermore, these attributes support the
agents’ processing and interpretation of speech or text to extract their meaning, language
translation, and dialogues. Several supporting technologies are leveraged to bring out these
attributes, including natural language comprehension, machine translation, speech recognition,
named entity recognition, as well as sentiment analysis (Martynov et al., 2019).
Perception and sensing are also key technical attributes. These agents rely on various
sensors to perceive and interpret their environments (Martynov et al., 2019). Furthermore, these
attributes allow the agents to gather and process information from multiple sources like
microphones, sensors, and cameras to obtain and enhance situational awareness. Several
supporting technologies are leveraged to bring out these attributes, including sensor integration,
computer vision, signal processing, and speech recognition (Martynov et al., 2019). Planning and
decision-making are another set of core technical attributes. These attributes allow intelligent
agents to develop plans or sequenced actions to attain specific goals (Martynov et al., 2019).
Furthermore, the attributes enable the agents to assess alternative choices, take account of
constraints, as well as optimize their decision-making. Several supporting technologies are
leveraged to bring out these attributes, including decision theory, automated planning, and
optimization algorithms (Martynov et al., 2019).
Collaborating and coordination are also core technical attributes. These attributes
facilitate the interaction and collaboration witnessed between the agents and other agents or
people to attain collective goals (Martynov et al., 2019). The attributes allow agents to
communicate, negotiate, and coordinate their activities to complete complicated tasks. Several
supporting technologies are leveraged to bring out these attributes, including coordinating
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mechanisms, multi-agent systems, and communication behavior (Martynov et al., 2019). Lastly,
intelligent agents are increasingly expected to demonstrate ethical conduct that conforms to
societal norms (Hancock et al., 2020). Essentially, they are expected to be accountable,
transparent, as well as capable of providing explanations to users for their actions (Hancock et
al., 2020). Several supporting technologies can be leveraged to bring out these attributes,
including transparency tools, ethical guidelines, and explainable artificial intelligence (Hancock
et al., 2020). A key point to note is that these technical attributes and their accompanying
supporting technologies are interlinked. Due to this, they frequently combine to establish more
advanced and capable, intelligent agent systems (Martynov et al., 2019). Furthermore, the
specific combination and implementation of these technologies might differ depending on the
requirements and application of an intelligent agent.
IV. Real-World Application of Intelligent Agents’ Technologies in Business
As intimated earlier, intelligent agents’ technologies are computer systems or programs
designed to perceive the environment, make reasonable assumptions and deductions about it, and
then take actions to attain specific goals. Ubiquitous household products like Alexa, Siri, and
robot vacuums that can move around rooms by themselves are just common illustrations of the
intelligent agents embraced by people in today’s society (Soni et al., 2019). Entering into the
consumer arena was just the first step. Integrating intelligence agents into the business world
provides numerous benefits for organizations (Soni et al., 2019). One area where successful
integration has occurred is customer service. More and more organizations are relying on these
agents to provide individualized customer experiences to their customers (Soni et al., 2019).
These agents allow organizations to present contextual offers to customers without a lot of
complex data science modeling. They ensure customers’ physical interactions with brands are
9
more personalized and enjoyable, increasing customer loyalty and the likelihood of repeat
business (Soni et al., 2019). Organizations are also relying on intelligent agents to enhance their
compliance. These agents can monitor all decisions they make, which can be leveraged as a
source of continual learning and provide a digital audit trail (Soni et al., 2019). This compliance
approach is often more objective than human decision-making. Intelligent Agents can also have
huge impacts on operational efficiency, imitating human decision-makers with greater efficiency
and integrating with various other forms of technology (Soni et al., 2019).
V. Future Prospects for Businesses and Science
Even though intelligent agents’ technologies are offering numerous benefits to individual
users and organizations, their continued use and development are being overshadowed by the
ethical issues associated with this technological phenomenon. For instance, no well-defined rules
and regulations exist to resolve the ethical and legal issues arising from using AI in healthcare
settings (Naik et al., 2022). Due to this, as intelligent technologies continue advancing, ethical
and legal issues such as the need for algorithmic transparency, privacy, and protection of all the
beneficiaries involved, as well as the cybersecurity of associated vulnerabilities, should be
addressed (Naik et al., 2022). These ethical and legal concerns are not solely localized to the
healthcare sector, considering they affect any organization leveraging these agents irrespective of
their industry. These agents will accelerate the research and discovery process within the
scientific field, facilitating improved data-driven insights and supporting complicated
simulations (Zhang & Lu, 2021). For instance, intelligent agents will be able to analyze large
amounts of biomedical information, such as findings from clinical trials and genetic data (Zhang
& Lu, 2021).
Conclusion
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Most people were apprehensive about the integration of AI into their lives. Despite these
fears, people are surrounded by autonomous, intelligent technologies. Intelligent agents’
technologies’ research and development has predominantly focused on creating autonomous
systems capable of perceiving, reasoning, and acting in dynamic and complicated environments.
All in all, the future of intelligent agents’ technologies holds great promise for science and
business. As these technologies advance, intelligent agents will become more sophisticated,
allowing organizations to leverage the power of information and automate their processes.
Despite this, finding ways to address the ethical and legal issues affecting their use is critical to
guaranteeing the beneficial and responsible deployment of intelligent agents’ technologies.
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