Rise of Machine Agency: A Framework for Studying the Psychology of Human-AI Interaction (HAII)

By Shyam Sundar. J. Comput. Mediat. Commun., 2020

712 citations recorded by the source corpus.

Advances in personalization algorithms and other applications of machine learning have vastly enhanced the ease and convenience of our media and communication experiences, but they have also raised significant concerns about privacy, transparency of technologies and human control over their operations. Going forth, reconciling such tensions between machine agency and human agency will be important in the era of artificial intelligence (AI), as machines get more agentic and media experiences become increasingly determined by algorithms. Theory and research should be geared toward a deeper understanding of the human experience of algorithms in general and the psychology of Human–AI interaction (HAII) in particular. This article proposes some directions by applying the dual-process framework of the Theory of Interactive Media Effects (TIME) for studying the symbolic and enabling effects of the affordances of AI-driven media on user perceptions and experiences.

Subfields: Safety Research, Sociology and Political Science, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/5936b8dcaa3f57c1202e2e75870d4eeb83eb2d21

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Safety Research

1. Donghee Shin
2. Wei Xu
3. Marvin J. Dainoff
4. Zaifeng Gao
5. Liezhong Ge

In Sociology and Political Science

1. Donghee Shin
2. Wei Xu
3. Marvin J. Dainoff
4. Zaifeng Gao
5. Liezhong Ge

In Human-Computer Interaction

1. Donghee Shin
2. Wei Xu
3. Marvin J. Dainoff
4. Zaifeng Gao
5. Liezhong Ge

Citations in the corpus

• Transitioning to Human Interaction with AI Systems: New Challenges and Opportunities for HCI Professionals to Enable Human-Centered AI (2021, methodology): “This work was mainly carried out in the computer science community, the HCI community should support for defining the paradigms, metaphors, and experimental verification to address the unique issues in AI systems as we did in the personal computer era (Fu et al., 2014; Sundar, 2020).”
• User Perceptions of Algorithmic Decisions in the Personalized AI System:Perceptual Evaluation of Fairness, Accountability, Transparency, and Explainability (2020, background): “Theoretically, clarifying the cognitive process of trust has made meaningful contributions to the ongoing discussion of human-AI interaction (Sundar, 2020).”
• Agentic AI: Autonomous Intelligence for Complex Goals—A Comprehensive Survey (2025, neutral): “Theoretical research into topics such as multi-agent coordination, decentralized decision-making, and long-term goal management could provide new insights into enhancing AI autonomy while maintaining control [78].”
• Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century (2021, background): “33) and it is further noted that, as a result of rising machine agency [54], we need a clear set of guidelines and codes to regulate human-AI interaction [55] in an explicit and transparent manner [56].”