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Decision Making01:20

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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An integrative decision-making framework to guide policies on regulating ChatGPT usage.

Umar Ali Bukar1, Md Shohel Sayeed1, Siti Fatimah Abdul Razak1

  • 1Centre for Intelligent Cloud Computing (CICC), Faculty of Information Science & Technology, Multimedia University, Melaka, Malaysia.

Peerj. Computer Science
|March 5, 2024
PubMed
Summary

Policymakers can use a risk, reward, and resilience framework to guide decisions on generative artificial intelligence (AI) tools like ChatGPT. This framework helps navigate the complexities of AI adoption, particularly in higher education, balancing benefits against potential drawbacks.

Keywords:
ChatGPTDecision makingEthicsGenerative AIHigher educationPolicy makingResilienceRewardRiskSystematic review

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Area of Science:

  • Artificial Intelligence Ethics
  • Policy Making Frameworks
  • Generative AI Governance

Background:

  • Generative artificial intelligence (AI) tools, exemplified by ChatGPT, necessitate policy development due to increasing human-AI interaction.
  • Policymakers face the challenge of regulating advanced AI technologies, balancing innovation with potential risks.
  • The widespread adoption of AI tools like ChatGPT in various sectors, including higher education, requires a structured approach to governance.

Purpose of the Study:

  • To propose a policy-making framework for generative artificial intelligence (AI) based on risk, reward, and resilience.
  • To provide policymakers with a decision-making primer for navigating AI-related challenges.
  • To apply the proposed framework to the specific context of ChatGPT in higher education.

Main Methods:

  • Systematic literature search using carefully selected keywords, excluding non-English content, conference articles, book chapters, and editorials.
  • Filtering of published research based on relevance to ChatGPT ethics, resulting in 41 articles.
  • Deduction and classification of key elements related to ChatGPT concerns and motivations into risk, reward, and resilience categories.

Main Results:

  • The study identified interconnections between risk and reward, such as efficiency gains versus plagiarism risks with ChatGPT use.
  • Opportunities like accessing information present rewards but also risks of misinformation and copyright issues.
  • Developing AI resilience tools can enhance academic integrity but may create vulnerabilities like the digital divide and job losses.

Conclusions:

  • The risk, reward, and resilience framework offers a comprehensive and flexible model for AI policy decision-making.
  • This framework assists policymakers and higher education institutions in navigating the complexities and trade-offs associated with generative AI tools like ChatGPT.
  • The study highlights the theoretical and practical implications of AI governance for the future, including second-order effects of legislation.