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Dynamic Convergence: Exploring AI's Impact on Social Science in Malaysia
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Abstract
The vibrant landscape of Malaysia serves as a dynamic backdrop for the convergence of artificial intelligence (AI) and social science, reshaping knowledge and inquiry amidst tradition and modernity. With Malaysia leading a technological renaissance driven by digital transformation and innovation, the ethical implications of AI-driven decision-making in public policy and governance emerge as pressing research problems. Addressing concerns such as algorithmic bias and discrimination requires interdisciplinary collaboration to ensure transparency, accountability, and fairness. Exploring questions regarding the distribution of welfare benefits and strategies for enhancing transparency and accountability in urban planning underscores the need for contextually relevant frameworks and stakeholder engagement. Through comprehensive research objectives, including investigating algorithmic biases and analyzing their social implications, scholars aim to contribute to a deeper understanding of the complex interplay between AI technology, social dynamics, and public policy. As Malaysia navigates the challenges and opportunities presented by AI deployment, prioritizing interdisciplinary collaborations, longitudinal studies, and capacity-building efforts will be essential for shaping a future that upholds principles of social justice and collective well-being.
Keywords:
Subject: Social Sciences - Sociology
Introduction
In the vibrant landscape of Malaysia, where tradition meets modernity and diversity thrives, the convergence of artificial intelligence (AI) and social science is reshaping the contours of knowledge and inquiry. As a professor deeply engaged in both disciplines, I am fascinated by the evolving dynamics and synergies emerging at this intersection, where cutting-edge technology meets the complexities of human behavior and societal systems.
Malaysia stands at the forefront of a technological renaissance, propelled by a vision of digital transformation and innovation. With a strategic focus on Industry 4.0 and smart nation initiatives, the country is harnessing the power of AI to drive economic growth, enhance public services, and empower its citizens. Initiatives such as the National AI Framework and investments in research and development underscore Malaysia’s commitment to leveraging AI as a catalyst for progress.
Research Problem
One of the pressing research problems at the intersection of AI and social science in Malaysia revolves around the ethical implications of AI-driven decision-making in public policy and governance. As AI technologies increasingly inform policy decisions in areas such as healthcare, education, and urban planning, there is a growing need to ensure transparency, accountability, and fairness in the decision-making process. Recent studies have highlighted concerns about algorithmic bias and discrimination in AI systems deployed by government agencies and public institutions. For instance, in the context of welfare distribution or criminal justice sentencing, AI algorithms trained on biased datasets may perpetuate existing inequalities and marginalize certain demographic groups.
Moreover, the lack of regulatory frameworks and ethical guidelines specific to AI applications in public policy exacerbates these concerns. Without clear guidelines on data privacy, algorithm transparency, and accountability mechanisms, there is a risk of unintended consequences and societal backlash against AI-driven decision-making processes. Addressing this research problem requires interdisciplinary collaboration between AI researchers, social scientists, policymakers, and civil society stakeholders. By integrating insights from social science disciplines such as ethics, sociology, and political science with AI-driven methodologies such as algorithm auditing and fairness testing, researchers can develop robust frameworks for ethical AI governance in Malaysia.
Furthermore, engaging with diverse stakeholders through participatory approaches, such as citizen consultations and stakeholder dialogues, can help ensure that AI-driven policies reflect the values, preferences, and needs of Malaysian society. In conclusion, the ethical implications of AI-driven decision-making in public policy and governance represent a critical research problem at the intersection of AI and social science in Malaysia. By addressing these challenges through interdisciplinary collaboration and ethical deliberation, researchers can pave the way for responsible AI deployment that fosters social inclusion, equity, and collective well-being in Malaysian society.
Research Question
How do algorithmic biases in AI systems affect the distribution of welfare benefits in Malaysia, and what are the implications for social equity and inclusion? This question addresses the specific concern of algorithmic bias in AI systems deployed for welfare distribution in Malaysia. By examining the ways in which biases manifest in these systems and analyzing their impact on the equitable distribution of benefits, researchers can uncover insights into potential disparities and marginalization of certain demographic groups. Furthermore, exploring the ethical implications of these biases can inform the development of more transparent and accountable AI-driven decision-making processes in public policy and governance.
What strategies can be implemented to enhance transparency and accountability in AI-driven decision-making processes for urban planning in Malaysia, considering the diverse socio-cultural landscape and stakeholder perspectives? This question focuses on the development of practical strategies to address the lack of transparency and accountability in AI-driven decision-making processes, particularly in the context of urban planning in Malaysia. By engaging with stakeholders from diverse socio-cultural backgrounds through participatory approaches, researchers can identify key challenges and opportunities for enhancing transparency and accountability. Additionally, by drawing on insights from social science disciplines such as sociology and political science, researchers can develop contextually relevant frameworks for ethical AI governance that reflect the values and preferences of Malaysian society.
Research Objective
The research objective is to investigate the presence and impact of algorithmic biases in AI systems used for welfare distribution in Malaysia and to assess their implications for social equity and inclusion. Specifically, the study aims to identify the types and sources of algorithmic biases present in AI systems deployed for welfare distribution in Malaysia, considering factors such as dataset composition, algorithm design, and decision-making processes.
Next, to examine how algorithmic biases contribute to disparities and marginalization of certain demographic groups in the distribution of welfare benefits, including but not limited to socioeconomic status, ethnicity, and geographic location.
Moreover, to analyze the ethical implications of algorithmic biases in AI-driven welfare distribution systems, exploring issues of fairness, transparency, and accountability in public policy and governance.
By achieving these objectives, the research aims to contribute to a deeper understanding of the complex interplay between AI technology, social dynamics, and public policy, ultimately informing the development of more equitable and inclusive welfare distribution systems in Malaysia.
Literature Review
Critical theories provide a foundational framework for understanding the intricate dynamics of AI deployment in the workplace, particularly in relation to power structures, inequality, and social justice implications. Drawing on critical perspectives such as critical theory, feminist theory, and critical race theory, researchers analyze how AI technologies both perpetuate and challenge existing socio-economic disparities within organizations. These theories offer a lens through which to interrogate the underlying power dynamics inherent in AI-driven decision-making processes, shedding light on issues of representation, access, and distribution of resources among different groups within the workforce.
Foundational concepts from social science literature further enrich our understanding of the impact of AI on employees and organizational behavior. Classical theories such as Max Weber’s bureaucracy theory and Karl Marx’s theory of alienated labor provide frameworks for comprehending how AI influences job design, work processes, and overall employee well-being. Weber’s bureaucratic model, for instance, elucidates how AI-driven automation may streamline organizational processes but also potentially lead to hierarchical structures that stifle innovation and autonomy among workers. Marx’s theory of alienated labor, on the other hand, highlights the potential for AI technologies to exacerbate feelings of detachment and disconnection among workers, particularly in contexts where human creativity and agency are undervalued.
Past studies have delved into various dimensions of AI’s impact on employees, ranging from job displacement to skill requirements and job satisfaction. In Malaysia, research has explored how AI technologies, including automation and machine learning, reshape job roles across different sectors such as manufacturing, services, and finance. These studies have highlighted the complex interplay between technological advancement and workforce dynamics, illustrating both the opportunities and challenges that AI presents for employees in terms of job security, skill development, and career advancement.
Employee resistance and adaptation are critical aspects of understanding how workers respond to the implementation of AI technologies in the workplace. Studies have documented employees’ reactions, which range from resistance to adaptation and negotiation strategies. In Malaysia, research has examined how workers perceive AI technologies, their concerns about job security, and their strategies for coping with technological change. This research underscores the importance of considering employee perspectives and experiences in the design and implementation of AI systems to ensure successful adoption and mitigate potential negative consequences.
Ethical considerations are central to discussions surrounding AI and employee dynamics, emphasizing the importance of human-centered design, transparency, and accountability. Past studies have examined ethical dilemmas arising from AI deployment in employment contexts, such as privacy concerns, algorithmic bias, and the ethical implications of AI-mediated decision-making. In Malaysia, there is a growing recognition of the need to prioritize ethical considerations in AI deployment, particularly concerning employee rights, safety, and well-being.
Policy and regulatory frameworks play a crucial role in shaping the ethical and social implications of AI for employees. Studies in Malaysia have assessed existing policy frameworks related to AI deployment in the workplace and proposed recommendations for ensuring worker rights, safety, and well-being in the era of AI-driven automation. However, there is still a need for more comprehensive and enforceable regulations to safeguard employee interests and ensure responsible AI deployment.
Looking ahead, emerging trends such as the gig economy, remote work, and AI-enabled platforms present new challenges and opportunities for employees. Future research directions in Malaysia may explore the intersection of AI with these emerging trends, considering their implications for job quality, labor rights, and social inequality. By addressing these challenges and opportunities, researchers can contribute to a deeper understanding of the complex dynamics of AI and employee relations, ultimately informing policies and practices that promote equitable and inclusive workplaces in the AI-driven era.
Recommendation & Future Research Agenda
To recommend solutions to the research questions posed and to suggest future research directions, several strategies can be proposed. Firstly, to address the ethical implications of AI-driven decision-making in public policy and governance, interdisciplinary collaboration is essential. Researchers, policymakers, AI experts, and civil society stakeholders should collaborate to develop transparent and accountable frameworks for AI governance. This collaboration should involve the integration of insights from social science disciplines such as ethics, sociology, and political science with AI-driven methodologies like algorithm auditing and fairness testing. Additionally, engaging with diverse stakeholders through participatory approaches such as citizen consultations and stakeholder dialogues can ensure that AI-driven policies reflect the values and preferences of Malaysian society.
Regarding the research question concerning algorithmic biases in AI systems affecting the distribution of welfare benefits in Malaysia, solutions can be multifaceted. Researchers should conduct comprehensive audits of AI systems to identify and mitigate algorithmic biases. This involves examining dataset composition, algorithm design, and decision-making processes to ensure fairness and equity in welfare distribution. Furthermore, policymakers should implement regulatory frameworks and ethical guidelines specific to AI applications in public policy, emphasizing data privacy, algorithm transparency, and accountability mechanisms. Stakeholder engagement and public awareness campaigns can also foster trust and transparency in AI-driven decision-making processes.
For the research question focusing on strategies to enhance transparency and accountability in AI-driven decision-making processes for urban planning in Malaysia, contextually relevant frameworks are crucial. Researchers should collaborate with urban planners, community representatives, and policymakers to identify key challenges and opportunities for enhancing transparency and accountability. Participatory approaches such as co-design workshops and community consultations can facilitate the development of inclusive and responsive AI governance frameworks. Moreover, drawing on insights from social science disciplines such as sociology and political science can inform the design of governance structures that reflect the socio-cultural diversity and stakeholder perspectives inherent in urban planning processes.
Looking ahead, future research agendas in Malaysia should prioritize several areas. Firstly, longitudinal studies can track the implementation and impact of AI-driven policies and interventions over time, assessing their effectiveness and equity implications. Secondly, research should explore the intersection of AI with emerging trends such as the gig economy, remote work, and AI-enabled platforms, considering their implications for job quality, labor rights, and social inequality. Additionally, interdisciplinary research initiatives should investigate the ethical, legal, and societal implications of AI deployment across diverse sectors, including healthcare, education, and environmental sustainability. Finally, efforts to build capacity in AI governance, ethics, and policy-making should be prioritized to ensure that Malaysia is well-equipped to navigate the complexities of the AI-driven future while upholding principles of social inclusion, equity, and collective well-being.
Conclusion
In conclusion, the convergence of artificial intelligence (AI) and social science in Malaysia presents both opportunities and challenges for shaping the future of governance, public policy, and societal well-being. The pressing research problem of ethical implications stemming from AI-driven decision-making underscores the need for interdisciplinary collaboration and ethical deliberation to ensure transparency, accountability, and fairness. By addressing concerns such as algorithmic bias and discrimination, researchers can pave the way for responsible AI deployment that fosters social inclusion and equity. Moreover, the exploration of practical strategies to enhance transparency and accountability in AI-driven decision-making processes, particularly in urban planning, highlights the importance of contextually relevant frameworks and stakeholder engagement. Moving forward, future research agendas should prioritize longitudinal studies, interdisciplinary collaborations, and capacity-building efforts to navigate the complexities of the AI-driven future while upholding principles of social justice and collective well-being in Malaysian society.
References
- Grossmann, I.; Feinberg, M.; Parker, D.C.; Christakis, N.A.; Tetlock, P.E.; Cunningham, W.A. AI and the transformation of social science research. Science 2023, 380, 1108–1109. [Google Scholar] [CrossRef] [PubMed]
- Hagerty, A.; Rubinov, I. Global AI ethics: a review of the social impacts and ethical implications of artificial intelligence. arXiv 2019, arXiv:1907.07892. [Google Scholar]
- Irving, G.; Askell, A. AI Safety Needs Social Scientists. Distill 2019, 4, e14. [Google Scholar] [CrossRef]
- Miller, T. Explanation in artificial intelligence: Insights from the social sciences. Artif. Intell. 2018, 267, 1–38. [Google Scholar] [CrossRef]
- Morshidi, A.B.; Satar, N. S. M.; Azizan, A. A. D. A.; Idris, R.Z.; Idris, R.; Radzi, M. S. M.; Sarjono, F. A Bibliometric Analysis of Artificial Intelligence and Human Resource Management Studies. In Exploring the Intersection of AI and Human Resources Management; IGI Global, 2024; pp. 85-117.
- Morshidi, A.B.; Yussof, K. Y. S. B. K. M.,; Idris, R.Z.; Idris, R.; Abas, A. Online work adjustment in the sustainable development context during COVID-19 pandemic: a systematic literature review. Int. J. Work. Innov. 2022, 3, 269–288. [Google Scholar] [CrossRef]
- Morshidi, A.H.; Yussof, K.Y.S.K.M.; Shah, J.M. Predictors and Moderator of Telework Adjustment among Labour Union Members During Covid-19 Pandemic: A Conceptual Paper. Int. J. Acad. Res. Bus. Soc. Sci. 2021, 11, 217–266. [Google Scholar] [CrossRef]
- Morshidi, A.; Zakaria, N.S.; Idris, R.Z.; Ridzuan, M. I. M.; Yusoff, S.M. Generative Artificial Intelligence and Risk at Work: An Inevitable Consequence? Asian Journal of Research in Education and Social Sciences 2023, 5, 329–343. [Google Scholar]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Submitted:
24 April 2024
Posted:
24 April 2024
You are already at the latest version
Alerts
This version is not peer-reviewed
Submitted:
24 April 2024
Posted:
24 April 2024
You are already at the latest version
Alerts
Abstract
The vibrant landscape of Malaysia serves as a dynamic backdrop for the convergence of artificial intelligence (AI) and social science, reshaping knowledge and inquiry amidst tradition and modernity. With Malaysia leading a technological renaissance driven by digital transformation and innovation, the ethical implications of AI-driven decision-making in public policy and governance emerge as pressing research problems. Addressing concerns such as algorithmic bias and discrimination requires interdisciplinary collaboration to ensure transparency, accountability, and fairness. Exploring questions regarding the distribution of welfare benefits and strategies for enhancing transparency and accountability in urban planning underscores the need for contextually relevant frameworks and stakeholder engagement. Through comprehensive research objectives, including investigating algorithmic biases and analyzing their social implications, scholars aim to contribute to a deeper understanding of the complex interplay between AI technology, social dynamics, and public policy. As Malaysia navigates the challenges and opportunities presented by AI deployment, prioritizing interdisciplinary collaborations, longitudinal studies, and capacity-building efforts will be essential for shaping a future that upholds principles of social justice and collective well-being.
Keywords:
Subject: Social Sciences - Sociology
Introduction
In the vibrant landscape of Malaysia, where tradition meets modernity and diversity thrives, the convergence of artificial intelligence (AI) and social science is reshaping the contours of knowledge and inquiry. As a professor deeply engaged in both disciplines, I am fascinated by the evolving dynamics and synergies emerging at this intersection, where cutting-edge technology meets the complexities of human behavior and societal systems.
Malaysia stands at the forefront of a technological renaissance, propelled by a vision of digital transformation and innovation. With a strategic focus on Industry 4.0 and smart nation initiatives, the country is harnessing the power of AI to drive economic growth, enhance public services, and empower its citizens. Initiatives such as the National AI Framework and investments in research and development underscore Malaysia’s commitment to leveraging AI as a catalyst for progress.
Research Problem
One of the pressing research problems at the intersection of AI and social science in Malaysia revolves around the ethical implications of AI-driven decision-making in public policy and governance. As AI technologies increasingly inform policy decisions in areas such as healthcare, education, and urban planning, there is a growing need to ensure transparency, accountability, and fairness in the decision-making process. Recent studies have highlighted concerns about algorithmic bias and discrimination in AI systems deployed by government agencies and public institutions. For instance, in the context of welfare distribution or criminal justice sentencing, AI algorithms trained on biased datasets may perpetuate existing inequalities and marginalize certain demographic groups.
Moreover, the lack of regulatory frameworks and ethical guidelines specific to AI applications in public policy exacerbates these concerns. Without clear guidelines on data privacy, algorithm transparency, and accountability mechanisms, there is a risk of unintended consequences and societal backlash against AI-driven decision-making processes. Addressing this research problem requires interdisciplinary collaboration between AI researchers, social scientists, policymakers, and civil society stakeholders. By integrating insights from social science disciplines such as ethics, sociology, and political science with AI-driven methodologies such as algorithm auditing and fairness testing, researchers can develop robust frameworks for ethical AI governance in Malaysia.
Furthermore, engaging with diverse stakeholders through participatory approaches, such as citizen consultations and stakeholder dialogues, can help ensure that AI-driven policies reflect the values, preferences, and needs of Malaysian society. In conclusion, the ethical implications of AI-driven decision-making in public policy and governance represent a critical research problem at the intersection of AI and social science in Malaysia. By addressing these challenges through interdisciplinary collaboration and ethical deliberation, researchers can pave the way for responsible AI deployment that fosters social inclusion, equity, and collective well-being in Malaysian society.
Research Question
How do algorithmic biases in AI systems affect the distribution of welfare benefits in Malaysia, and what are the implications for social equity and inclusion? This question addresses the specific concern of algorithmic bias in AI systems deployed for welfare distribution in Malaysia. By examining the ways in which biases manifest in these systems and analyzing their impact on the equitable distribution of benefits, researchers can uncover insights into potential disparities and marginalization of certain demographic groups. Furthermore, exploring the ethical implications of these biases can inform the development of more transparent and accountable AI-driven decision-making processes in public policy and governance.
What strategies can be implemented to enhance transparency and accountability in AI-driven decision-making processes for urban planning in Malaysia, considering the diverse socio-cultural landscape and stakeholder perspectives? This question focuses on the development of practical strategies to address the lack of transparency and accountability in AI-driven decision-making processes, particularly in the context of urban planning in Malaysia. By engaging with stakeholders from diverse socio-cultural backgrounds through participatory approaches, researchers can identify key challenges and opportunities for enhancing transparency and accountability. Additionally, by drawing on insights from social science disciplines such as sociology and political science, researchers can develop contextually relevant frameworks for ethical AI governance that reflect the values and preferences of Malaysian society.
Research Objective
The research objective is to investigate the presence and impact of algorithmic biases in AI systems used for welfare distribution in Malaysia and to assess their implications for social equity and inclusion. Specifically, the study aims to identify the types and sources of algorithmic biases present in AI systems deployed for welfare distribution in Malaysia, considering factors such as dataset composition, algorithm design, and decision-making processes.
Next, to examine how algorithmic biases contribute to disparities and marginalization of certain demographic groups in the distribution of welfare benefits, including but not limited to socioeconomic status, ethnicity, and geographic location.
Moreover, to analyze the ethical implications of algorithmic biases in AI-driven welfare distribution systems, exploring issues of fairness, transparency, and accountability in public policy and governance.
By achieving these objectives, the research aims to contribute to a deeper understanding of the complex interplay between AI technology, social dynamics, and public policy, ultimately informing the development of more equitable and inclusive welfare distribution systems in Malaysia.
Literature Review
Critical theories provide a foundational framework for understanding the intricate dynamics of AI deployment in the workplace, particularly in relation to power structures, inequality, and social justice implications. Drawing on critical perspectives such as critical theory, feminist theory, and critical race theory, researchers analyze how AI technologies both perpetuate and challenge existing socio-economic disparities within organizations. These theories offer a lens through which to interrogate the underlying power dynamics inherent in AI-driven decision-making processes, shedding light on issues of representation, access, and distribution of resources among different groups within the workforce.
Foundational concepts from social science literature further enrich our understanding of the impact of AI on employees and organizational behavior. Classical theories such as Max Weber’s bureaucracy theory and Karl Marx’s theory of alienated labor provide frameworks for comprehending how AI influences job design, work processes, and overall employee well-being. Weber’s bureaucratic model, for instance, elucidates how AI-driven automation may streamline organizational processes but also potentially lead to hierarchical structures that stifle innovation and autonomy among workers. Marx’s theory of alienated labor, on the other hand, highlights the potential for AI technologies to exacerbate feelings of detachment and disconnection among workers, particularly in contexts where human creativity and agency are undervalued.
Past studies have delved into various dimensions of AI’s impact on employees, ranging from job displacement to skill requirements and job satisfaction. In Malaysia, research has explored how AI technologies, including automation and machine learning, reshape job roles across different sectors such as manufacturing, services, and finance. These studies have highlighted the complex interplay between technological advancement and workforce dynamics, illustrating both the opportunities and challenges that AI presents for employees in terms of job security, skill development, and career advancement.
Employee resistance and adaptation are critical aspects of understanding how workers respond to the implementation of AI technologies in the workplace. Studies have documented employees’ reactions, which range from resistance to adaptation and negotiation strategies. In Malaysia, research has examined how workers perceive AI technologies, their concerns about job security, and their strategies for coping with technological change. This research underscores the importance of considering employee perspectives and experiences in the design and implementation of AI systems to ensure successful adoption and mitigate potential negative consequences.
Ethical considerations are central to discussions surrounding AI and employee dynamics, emphasizing the importance of human-centered design, transparency, and accountability. Past studies have examined ethical dilemmas arising from AI deployment in employment contexts, such as privacy concerns, algorithmic bias, and the ethical implications of AI-mediated decision-making. In Malaysia, there is a growing recognition of the need to prioritize ethical considerations in AI deployment, particularly concerning employee rights, safety, and well-being.
Policy and regulatory frameworks play a crucial role in shaping the ethical and social implications of AI for employees. Studies in Malaysia have assessed existing policy frameworks related to AI deployment in the workplace and proposed recommendations for ensuring worker rights, safety, and well-being in the era of AI-driven automation. However, there is still a need for more comprehensive and enforceable regulations to safeguard employee interests and ensure responsible AI deployment.
Looking ahead, emerging trends such as the gig economy, remote work, and AI-enabled platforms present new challenges and opportunities for employees. Future research directions in Malaysia may explore the intersection of AI with these emerging trends, considering their implications for job quality, labor rights, and social inequality. By addressing these challenges and opportunities, researchers can contribute to a deeper understanding of the complex dynamics of AI and employee relations, ultimately informing policies and practices that promote equitable and inclusive workplaces in the AI-driven era.
Recommendation & Future Research Agenda
To recommend solutions to the research questions posed and to suggest future research directions, several strategies can be proposed. Firstly, to address the ethical implications of AI-driven decision-making in public policy and governance, interdisciplinary collaboration is essential. Researchers, policymakers, AI experts, and civil society stakeholders should collaborate to develop transparent and accountable frameworks for AI governance. This collaboration should involve the integration of insights from social science disciplines such as ethics, sociology, and political science with AI-driven methodologies like algorithm auditing and fairness testing. Additionally, engaging with diverse stakeholders through participatory approaches such as citizen consultations and stakeholder dialogues can ensure that AI-driven policies reflect the values and preferences of Malaysian society.
Regarding the research question concerning algorithmic biases in AI systems affecting the distribution of welfare benefits in Malaysia, solutions can be multifaceted. Researchers should conduct comprehensive audits of AI systems to identify and mitigate algorithmic biases. This involves examining dataset composition, algorithm design, and decision-making processes to ensure fairness and equity in welfare distribution. Furthermore, policymakers should implement regulatory frameworks and ethical guidelines specific to AI applications in public policy, emphasizing data privacy, algorithm transparency, and accountability mechanisms. Stakeholder engagement and public awareness campaigns can also foster trust and transparency in AI-driven decision-making processes.
For the research question focusing on strategies to enhance transparency and accountability in AI-driven decision-making processes for urban planning in Malaysia, contextually relevant frameworks are crucial. Researchers should collaborate with urban planners, community representatives, and policymakers to identify key challenges and opportunities for enhancing transparency and accountability. Participatory approaches such as co-design workshops and community consultations can facilitate the development of inclusive and responsive AI governance frameworks. Moreover, drawing on insights from social science disciplines such as sociology and political science can inform the design of governance structures that reflect the socio-cultural diversity and stakeholder perspectives inherent in urban planning processes.
Looking ahead, future research agendas in Malaysia should prioritize several areas. Firstly, longitudinal studies can track the implementation and impact of AI-driven policies and interventions over time, assessing their effectiveness and equity implications. Secondly, research should explore the intersection of AI with emerging trends such as the gig economy, remote work, and AI-enabled platforms, considering their implications for job quality, labor rights, and social inequality. Additionally, interdisciplinary research initiatives should investigate the ethical, legal, and societal implications of AI deployment across diverse sectors, including healthcare, education, and environmental sustainability. Finally, efforts to build capacity in AI governance, ethics, and policy-making should be prioritized to ensure that Malaysia is well-equipped to navigate the complexities of the AI-driven future while upholding principles of social inclusion, equity, and collective well-being.
Conclusion
In conclusion, the convergence of artificial intelligence (AI) and social science in Malaysia presents both opportunities and challenges for shaping the future of governance, public policy, and societal well-being. The pressing research problem of ethical implications stemming from AI-driven decision-making underscores the need for interdisciplinary collaboration and ethical deliberation to ensure transparency, accountability, and fairness. By addressing concerns such as algorithmic bias and discrimination, researchers can pave the way for responsible AI deployment that fosters social inclusion and equity. Moreover, the exploration of practical strategies to enhance transparency and accountability in AI-driven decision-making processes, particularly in urban planning, highlights the importance of contextually relevant frameworks and stakeholder engagement. Moving forward, future research agendas should prioritize longitudinal studies, interdisciplinary collaborations, and capacity-building efforts to navigate the complexities of the AI-driven future while upholding principles of social justice and collective well-being in Malaysian society.
References
- Grossmann, I.; Feinberg, M.; Parker, D.C.; Christakis, N.A.; Tetlock, P.E.; Cunningham, W.A. AI and the transformation of social science research. Science 2023, 380, 1108–1109. [Google Scholar] [CrossRef] [PubMed]
- Hagerty, A.; Rubinov, I. Global AI ethics: a review of the social impacts and ethical implications of artificial intelligence. arXiv 2019, arXiv:1907.07892. [Google Scholar]
- Irving, G.; Askell, A. AI Safety Needs Social Scientists. Distill 2019, 4, e14. [Google Scholar] [CrossRef]
- Miller, T. Explanation in artificial intelligence: Insights from the social sciences. Artif. Intell. 2018, 267, 1–38. [Google Scholar] [CrossRef]
- Morshidi, A.B.; Satar, N. S. M.; Azizan, A. A. D. A.; Idris, R.Z.; Idris, R.; Radzi, M. S. M.; Sarjono, F. A Bibliometric Analysis of Artificial Intelligence and Human Resource Management Studies. In Exploring the Intersection of AI and Human Resources Management; IGI Global, 2024; pp. 85-117.
- Morshidi, A.B.; Yussof, K. Y. S. B. K. M.,; Idris, R.Z.; Idris, R.; Abas, A. Online work adjustment in the sustainable development context during COVID-19 pandemic: a systematic literature review. Int. J. Work. Innov. 2022, 3, 269–288. [Google Scholar] [CrossRef]
- Morshidi, A.H.; Yussof, K.Y.S.K.M.; Shah, J.M. Predictors and Moderator of Telework Adjustment among Labour Union Members During Covid-19 Pandemic: A Conceptual Paper. Int. J. Acad. Res. Bus. Soc. Sci. 2021, 11, 217–266. [Google Scholar] [CrossRef]
- Morshidi, A.; Zakaria, N.S.; Idris, R.Z.; Ridzuan, M. I. M.; Yusoff, S.M. Generative Artificial Intelligence and Risk at Work: An Inevitable Consequence? Asian Journal of Research in Education and Social Sciences 2023, 5, 329–343. [Google Scholar]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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