Ethical Concerns and Mitigation Strategies in AI-Driven Language Models

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@article{1710825,
      author = {Rishabh Agrawal, Himanshu Kumar},
      title = {Ethical Concerns and Mitigation Strategies in AI-Driven Language Models},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {11},
      pages = {842-855},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710825.pdf},
      abstract = {Rapid development and widespread application of AI-driven language models, particularly large language models (LLMs) like GPT-4 and subsequent variants, have revolutionized human-machine communication by enabling unprecedented natural language processing and generation capacities. This development is followed by essential ethical concerns that must be addressed promptly to promote responsible use. This study is focused on salient ethical challenges of AI-driven language models, including bias and discrimination within training datasets, misinformation and deep fake generation, intellectual property rights, privacy intrusion, and accountability gaps. These models have the capacity to reproduce or even amplify societal stereotypes, thereby generating biased outputs that disenfranchise vulnerable groups and propagate misinformation at scale. The generation of very realistic yet fake content endangers social trust and democratic institutions. Furthermore, the big data that trains these models may be intruding on user privacy, while non-transparent decision-making raises questions of transparency and governance. The paper synthesizes current literature and stakeholder interviews to outline the significance of these ethical concerns in academic, industrial, and societal terms. Correspondingly, the study proposes a multi-dimensional mitigation framework consisting of developing unambiguous and enforceable guidelines for AI utilization, integration of AI literacy and ethics education across sectors, implementation of bias identification and rectification processes, and enhanced regulatory oversight to foster responsibility. Stakeholder engagement and policy continuous updating are central to keeping pace with technological evolution, it is emphasized. By confronting such ethical issues proactively, the field can promote equitable, trustworthy, and socially beneficial AI technologies. This article contributes to a growing conversation on responsible AI governance and guides the ethical use of AI-driven language models in diverse domains.},
      keywords = {AI Ethics, Language Models, Bias, Transparency, Accountability, Mitigation Strategies},
      month = {May},
      doi = {https://doi.org/10.64388/IREV7I11-1710825}
  }