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The effect of AI on individual autonomy and understanding

✓ Verified against 15 peer-reviewed sources

The effect of artificial intelligence (AI) on individual autonomy and understanding is a complex topic addressed in several of the provided scientific papers. Below is a structured analysis based on the available literature:

1. Perceived Loss of Autonomy and Personal Control

The paper by Chen et al. [1] highlights that autonomous technologies (ATs), including AI systems, can reduce human autonomy by taking over decision-making processes. The authors developed a scale to measure the perceived loss of control when using AI-driven systems, emphasizing that while AI may free individuals from manual tasks, it simultaneously diminishes their sense of autonomy. This aligns with broader concerns about AI's role in human-machine interaction, where users may feel less in control as AI systems make decisions on their behalf [4].

2. Autonomy in Human-Robot Interaction

Beer et al. [4] discuss the concept of "Levels of Robot Autonomy (LORA)" in human-robot interaction (HRI). They note that as robots become more autonomous, the nature of human interaction shifts, potentially reducing human autonomy in decision-making. For example, fully autonomous systems may override human input, further eroding individual autonomy. This is particularly relevant in contexts where AI systems operate independently, such as in healthcare or education [12, 15].

3. Manipulation and Influence in AI Systems

Susser et al. [2] explore the concept of "online manipulation," where AI systems may subtly influence human decision-making. This raises ethical concerns about AI's role in shaping autonomy, as individuals may unknowingly be guided by AI-driven recommendations or algorithms. Such manipulation could undermine personal autonomy by altering behavior without explicit consent [6].

4. Impact on Critical Thinking and Understanding

Several papers discuss the potential effects of AI on human understanding. For instance, Michel-Villarreal et al. [9] highlight that generative AI tools like ChatGPT may impact academic integrity and critical thinking by providing ready-made answers. Similarly, Chan and Hu [7] found that students perceive AI as a tool that could reduce the depth of learning by offering superficial explanations. This suggests that while AI may enhance efficiency, it may also diminish the development of independent thought and deep understanding [8, 10].

5. Ethical Considerations in AI Ethics

Hagendorff [13] critiques the ethical guidelines surrounding AI, arguing that many frameworks fail to address the broader implications of AI on human autonomy. He suggests that ethical discussions should prioritize the preservation of individual autonomy, particularly in contexts where AI systems may override human judgment. This aligns with concerns raised in other papers about the potential for AI to erode personal control [1, 4].

Conclusion

The provided papers collectively suggest that AI has a dual effect on individual autonomy and understanding:

  • Reduction of Autonomy: AI systems can diminish personal control by making decisions on behalf of users, particularly in autonomous technologies and human-robot interactions [1, 4].
  • Impact on Understanding: AI may facilitate efficiency but risk shallowing human learning and critical thinking, as seen in educational applications [7, 9].
  • Ethical Concerns: There is a need for ethical frameworks that explicitly address the preservation of autonomy in AI-driven systems [13].

However, the papers do not provide a comprehensive quantitative analysis of these effects across all domains. Further research is needed to fully assess the long-term implications of AI on individual autonomy and understanding.

Sources

Xiaofan Chen, Emma Slade, Xiaojun Wang et al. · Psychology and Marketing (2006) · 481 citations · OpenAlex
Daniel Susser, Beate Roessler, Helen Nissenbaum · Internet Policy Review (2019) · 423 citations · OpenAlex
Yogesh K. Dwivedi, Nir Kshetri, Laurie Hughes et al. · International Journal of Information Management (2023) · 3676 citations · OpenAlex
Jenay M. Beer, Arthur D. Fisk, Wendy A. Rogers · Journal of Human-Robot Interaction (2014) · 589 citations · OpenAlex
W. Holmes, Kaśka Porayska‐Pomsta, Ken Holstein et al. · International Journal of Artificial Intelligence in Education (2021) · 1037 citations · OpenAlex
Theo Araujo, Natali Helberger, Sanne Kruikemeier et al. · AI & Society (2020) · 952 citations · OpenAlex
Cecilia Ka Yuk Chan, Wenjie Hu · International Journal of Educational Technology in Higher Education (2023) · 1760 citations · OpenAlex
Tufan Adıgüzel, Mehmet Haldun Kaya, Fatih Kürşat Cansu · Contemporary Educational Technology (2023) · 851 citations · OpenAlex
Rosario Michel‐Villarreal, Eliseo Luis Vilalta-perdomo, David Ernesto Salinas-Navarro et al. · Education Sciences (2023) · 741 citations · OpenAlex
Thomas K. F. Chiu · Interactive Learning Environments (2023) · 682 citations · OpenAlex
Milad Mirbabaie, Felix Brünker, Nicholas Frick et al. · Electronic Markets (2021) · 287 citations · OpenAlex
Shuroug A. Alowais, Sahar S. Alghamdi, Nada Alsuhebany et al. · BMC Medical Education (2023) · 2791 citations · OpenAlex
Thilo Hagendorff · Minds and Machines (2020) · 1621 citations · OpenAlex
Soumyadeb Chowdhury, Prasanta Kumar Dey, Sian Joel-Edgar et al. · Human Resource Management Review (2022) · 805 citations · OpenAlex
Ahmed Al Kuwaiti, Khalid Nazer, Abdullah H. Alreedy et al. · Journal of Personalized Medicine (2023) · 801 citations · OpenAlex

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This article was generated by MultiModelMagic Science AI by synthesizing the peer-reviewed sources listed above, and validated by a second model for accuracy. It is for informational purposes only and is not medical, legal, or professional advice. Always consult a qualified professional.