The Impact of Machine Learning on Society: An Analysis of Current Trends and Future Implications

• URL: http://arxiv.org/abs/2404.10204v1
• Date: Tue, 16 Apr 2024 01:10:09 GMT
• Title: The Impact of Machine Learning on Society: An Analysis of Current Trends and Future Implications
• Authors: Md Kamrul Hossain Siam, Manidipa Bhattacharjee, Shakik Mahmud, Md. Saem Sarkar, Md. Masud Rana,
• Abstract summary: This research aimed to conduct a comprehensive analysis of the current and future impact of Machine learning on society. The research included a thorough literature review, case studies, and surveys to gather data on the economic impact of ML, ethical and privacy implications, and public perceptions of the technology. The findings of this research revealed that the majority of respondents have a moderate level of familiarity with the concept of ML, believe that it has the potential to benefit society, and think that society should prioritize the development and use of ML.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Machine learning (ML) is a rapidly evolving field of technology that has the potential to greatly impact society in a variety of ways. However, there are also concerns about the potential negative effects of ML on society, such as job displacement and privacy issues. This research aimed to conduct a comprehensive analysis of the current and future impact of ML on society. The research included a thorough literature review, case studies, and surveys to gather data on the economic impact of ML, ethical and privacy implications, and public perceptions of the technology. The survey was conducted on 150 respondents from different areas. The case studies conducted were on the impact of ML on healthcare, finance, transportation, and manufacturing. The findings of this research revealed that the majority of respondents have a moderate level of familiarity with the concept of ML, believe that it has the potential to benefit society, and think that society should prioritize the development and use of ML. Based on these findings, it was recommended that more research is conducted on the impact of ML on society, stronger regulations and laws to protect the privacy and rights of individuals when it comes to ML should be developed, transparency and accountability in ML decision-making processes should be increased, and public education and awareness about ML should be enhanced.

Related papers

• On the Societal Impact of Machine Learning [8.200229795326445]
  This thesis investigates the societal impact of machine learning (ML)<n>ML increasingly informs consequential decisions and recommendations, significantly affecting many aspects of our lives.<n>As these data-driven systems are often developed without explicit fairness considerations, they carry the risk of discriminatory effects.
  arXiv  Detail & Related papers  (2025-10-27T17:59:48Z)
• Persuasion with Large Language Models: a Survey [49.86930318312291]
  Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
  arXiv  Detail & Related papers  (2024-11-11T10:05:52Z)
• Decoding Large-Language Models: A Systematic Overview of Socio-Technical Impacts, Constraints, and Emerging Questions [1.1970409518725493]
  The article highlights the application areas that could have a positive impact on society along with the ethical considerations. It includes responsible development considerations, algorithmic improvements, ethical challenges, and societal implications.
  arXiv  Detail & Related papers  (2024-09-25T14:36:30Z)
• LLM-PBE: Assessing Data Privacy in Large Language Models [111.58198436835036]
  Large Language Models (LLMs) have become integral to numerous domains, significantly advancing applications in data management, mining, and analysis. Despite the critical nature of this issue, there has been no existing literature to offer a comprehensive assessment of data privacy risks in LLMs. Our paper introduces LLM-PBE, a toolkit crafted specifically for the systematic evaluation of data privacy risks in LLMs.
  arXiv  Detail & Related papers  (2024-08-23T01:37:29Z)
• A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law [65.87885628115946]
  Large language models (LLMs) are revolutionizing the landscapes of finance, healthcare, and law. We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies. We critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems.
  arXiv  Detail & Related papers  (2024-05-02T22:43:02Z)
• Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally [50.92232179802755]
  AdvML for Social Good (AdvML4G) is an emerging field that repurposes the AdvML bug to invent pro-social applications. This paper provides the first comprehensive review of the emerging field of AdvML4G.
  arXiv  Detail & Related papers  (2023-10-05T15:49:04Z)
• Influence of External Information on Large Language Models Mirrors Social Cognitive Patterns [51.622612759892775]
  Social cognitive theory explains how people learn and acquire knowledge through observing others. Recent years have witnessed the rapid development of large language models (LLMs) LLMs, as AI agents, can observe external information, which shapes their cognition and behaviors.
  arXiv  Detail & Related papers  (2023-05-08T16:10:18Z)
• Addressing contingency in algorithmic (mis)information classification: Toward a responsible machine learning agenda [0.9659642285903421]
  Data scientists need to take a stance on the objectivity, authoritativeness and legitimacy of the sources of truth" used for model training and testing. Despite (and due to) their reported high accuracy and performance, ML-driven moderation systems have the potential to shape online public debate and create downstream negative impacts such as undue censorship and the reinforcing of false beliefs.
  arXiv  Detail & Related papers  (2022-10-05T17:34:51Z)
• Machine learning in the social and health sciences [0.8681909776958184]
  This paper provides a meta-mapping of research questions in the social and health sciences to appropriate machine learning approaches. We map the established classification into description, prediction, and causal inference to common research goals.
  arXiv  Detail & Related papers  (2021-06-20T15:48:45Z)
• On the Morality of Artificial Intelligence [154.69452301122175]
  We propose conceptual and practical principles and guidelines for Machine Learning research and deployment. We insist on concrete actions that can be taken by practitioners to pursue a more ethical and moral practice of ML aimed at using AI for social good.
  arXiv  Detail & Related papers  (2019-12-26T23:06:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.