Description
You should have gathered and read information from a number of sources related to your topic. State you topic and problem you are addressing in your dissertation. What trends are you seeing in the research related to your topic? What gap in evidence have you identified so far? NOTE: the topic is Exploring Ethical Considerations in AI Decision-Making AlgorithmsI need 200 words for the discussion, in APA format & references are important too, mention all the problems you see with this topic in your research related. I need 3-4 reference which are not mentioned in the below attachment.Below is the brief description on what i have worked on the earlier weeks for this dissertation.
Unformatted Attachment Preview
Title: Exploring Ethical Considerations in AI Decision-Making Algorithms
Proposed Topic: The investigation of the ethical problems AI decision-making
algorithms can cause is the aim of the suggested dissertation study. The ethical
concerns arising from AI systems must be tackled as they evolve and find broad use
across many industries. The study aims to expand the knowledge base in this field by
looking into dilemmas, biases, and probable societal impacts of AI algorithms
(Rodgers at el., 2023).
Methodology:
A
mixed-methods
research
design
integrating
quantitative and qualitative methods will be employed in the study. Among the results
of an artificial intelligence (AI) decision-making assessment, the quantitative part will
identify patterns, biases, and trends, which involves an extensive dataset analysis. The
in-depth interviews and case studies will be the qualitative part of the study, looking
at the views and real-life stories of people who have encountered AI algorithms. In
addition, a pragmatic technique will be applied to suggest feasible solutions and
recommendations for ethical issues involved in AI systems. The study will also be
concerned with the dynamic nature of AI technology, considering how ethical
considerations change over time employing a longitudinal approach(Nassar and
Kamal, 2021).
Population & Sample Selection: The study population of the research
are victims of AI decision-making algorithms, whether they are researchers,
professionals, or others from various industries. We will sample to ensure the
representation of all the demographic parameters by pulling from different
backgrounds. Resources like The U.S. Bureau of Labor Statistics will be utilized to
approximate the research participants’ population size. For AI job applicant search,
groups of people on social media, professional networks, and institutions
acknowledged for their impact in the AI field will be leveraged.
Since AI technologies are of global importance, it is necessary to bring
international bibliographic records, including material from established regions and
emerging ones, to result in a sample with diversity. Legislators’ and AI developers’
views will also be considered to gain a more comprehensive understanding of the
moral environment.
Theoretical
Framework
and
Background:
Utilitarianism
and
deontology are the main aspects that my study’s theoretical lens will focus on. The
ethical impacts of AI decision-making will encompass these concepts. In the
utilitarianism approach, we will consider how AI algorithms will affect society as a
whole, taking responsibility, transparency and fairness into account. The
morphologies of moral duties and principles innate in the development and
implementation of AI will be governed by deontology that appreciates the importance
of adherence to morality and individual freedom. In addition to the ethnic
environment, the study will also explore multidisciplinary perspectives, such as views
from sociology, psychology and philosophy. Also, the new ethical framework
designed explicitly for AI technology will be studied in the present research(Gerlick
and Liozu, 2020).
Link to Program Goals and Courses: By emphasizing the role of ethics
in AI development, this study supports the program’s ambition to train graduates who
can ethically engage in technology development. The investigation applies the
knowledge developed in these courses to real-hacking IT issues; hence, it is closely
related to courses in ethics, artificial intelligence, and research methodologies.
Furthermore, by linking the investigation with sociology, psychology and philosophy
subjects, the study aspires to enhance multidisciplinary cooperation and a total
understanding of the ethics of Artificial Intelligence.
. A project will be conducted to examine ethical considerations when
implementing a curriculum for building AI as an education component that aligns
with the program’s overall goals.
Practical Implications and Future Research: The conclusions will be
practicable for industry experts, legislators, and AI developers. For Responsible AI,
guidelines and ethical standards will be offered. The research will also include
recommendations for further studies on issues such as the direction of the
development of AI ethics, how culture influences ethical issues and the creation of
tools for real-time ethical evaluation of AI algorithms(Barn, 2020).
The suggested thesis topic on ethical considerations in AI decisionmaking algorithms will uniquely contribute to the recently booming domain of
artificial intelligence. The comprehensive strategy enables collaboration across
disciplines and corresponds with the program’s aims, considering diverse populations,
approaches, and theoretical perspectives. Also, the study provides the groundwork for
future debates and progress on getting AI technology morally sound.
Timeline and Milestones: A comprehensive timeline that maps specific
dates for each research phase, from literature review, data collecting, analysis and
thesis writing, will be developed. Feedback sessions and regular progress reports
provided by the advisor will ensure timely completion.
Financial Considerations: A tentative estimate of the funds required
will be offered, encompassing costs for research data collection, payments to the
participants, travel to the field (if necessary), software/tools, and dissemination
activities.
Limitations and Delimitations: The study will highlight potential
restrictions, among others, being sample size limitations and the ever-changing nature
of AI technologies. The study will have delimitations that will define what is studied.
Problems
1. Bias and Fairness:
Problem: AI algorithms often exhibit biases, reflecting the biases present in training
data. This can lead to unfair treatment of certain groups or individuals.
Literature Focus: Investigate existing instances of biased AI decisions, explore
methods for detecting and mitigating bias, and propose strategies for ensuring fairness
in AI algorithms.
2. Transparency and Explainability:
Problem: Many AI algorithms, especially deep learning models, operate as “black
boxes,” making it challenging to understand how they arrive at specific decisions.
Literature Focus: Examine the importance of transparency in AI decision-making,
review current methods for making AI algorithms more explainable, and propose
guidelines for ensuring transparency in AI systems.
3. Accountability and Responsibility:
Problem: Determining accountability when AI systems make unethical decisions is
complex, especially when multiple entities are involved in the development and
deployment process.
Literature Focus: Explore ethical frameworks for assigning responsibility in AI
decision-making, analyze legal precedents, and propose guidelines for holding
individuals or organizations accountable for AI outcomes.
4. Privacy Concerns:
Problem: AI algorithms often process large amounts of personal data, raising concerns
about privacy infringement and potential misuse.
Literature Focus: Investigate privacy issues associated with AI decision-making,
review existing regulations and ethical guidelines, and propose measures to safeguard
user privacy in AI systems.
5. Cross-Cultural Ethical Variations:
Problem: Ethical considerations in AI may vary across cultures, leading to challenges
in developing universally applicable ethical guidelines.
Literature Focus: Examine cultural differences in ethical perspectives on AI, explore
the impact on decision-making, and propose strategies for developing culturally
sensitive AI ethics.
6. Lack of Standardized Ethical Frameworks:
Problem: The absence of universally accepted ethical frameworks for AI decisionmaking hinders the development of consistent guidelines and best practices.
Literature Focus: Review existing ethical frameworks for AI, assess their strengths
and weaknesses, and propose a standardized framework or guidelines for ethical AI
development.
7. Dynamic Nature of AI Technology:
Problem: The rapid evolution of AI technology makes it challenging to establish static
ethical guidelines that can adapt to emerging challenges.
Literature Focus: Explore the dynamic nature of AI, review the literature on adaptive
ethical frameworks, and propose strategies for updating ethical guidelines in response
to technological advancements.
8. User Trust and Perception:
Problem: Lack of user trust in AI decision-making may hinder the widespread
adoption of AI technologies.
Literature Focus: Investigate factors influencing user trust in AI systems, explore
methods for building trust, and propose strategies to enhance user perception of AI
ethics.
9. Security Risks:
Problem: Ethical considerations extend to the security of AI systems, as
vulnerabilities can be exploited to manipulate decision outcomes.
Literature Focus: Explore the intersection of AI ethics and cybersecurity, identify
potential security risks, and propose measures to ensure the integrity and security of
AI decision-making processes.
10. Social and Economic Impacts:
Problem: AI decision-making can have profound social and economic implications,
including job displacement and societal inequalities.
Literature Focus: Examine the social and economic impacts of AI, investigate ways to
address negative consequences, and propose ethical guidelines to mitigate adverse
effects.
References
Rodgers, W., Murray, J. M., Stefanidis, A., Degbey, W. Y., & Tarba, S. Y. (2023). An
artificial intelligence algorithmic approach to ethical decision-making in human
resource management processes. Human Resource Management Review, 33(1),
100925.
Nassar, A., & Kamal, M. (2021). Ethical dilemmas in AI-powered decision-making: a
deep dive into significant data-driven ethical considerations. International
Journal of Responsible Artificial Intelligence, 11(8), 1-11.
Gerlick, J. A., & Liozu, S. M. (2020). Ethical and legal considerations of artificial
intelligence and algorithmic decision-making in personalized pricing. Journal of
Revenue and Pricing Management, 19, 85-98.
Marabelli, M., Newell, S., & Handunge, V. (2021). The lifecycle of algorithmic
decision-making systems: Organizational choices and ethical challenges. The
Journal of Strategic Information Systems, 30(3), 101683.
Barn, B. S. (2020). Mapping the public debate on ethical concerns: algorithms in
mainstream media. Journal of Information, Communication and Ethics in
Society, 18(1), 124-139.
Purchase answer to see full
attachment
