Position: Towards Bidirectional Human-AI Alignment

• URL: http://arxiv.org/abs/2406.09264v4
• Date: Mon, 29 Sep 2025 06:11:24 GMT
• Title: Position: Towards Bidirectional Human-AI Alignment
• Authors: Hua Shen, Tiffany Knearem, Reshmi Ghosh, Kenan Alkiek, Kundan Krishna, Yachuan Liu, Ziqiao Ma, Savvas Petridis, Yi-Hao Peng, Li Qiwei, Sushrita Rakshit, Chenglei Si, Yutong Xie, Jeffrey P. Bigham, Frank Bentley, Joyce Chai, Zachary Lipton, Qiaozhu Mei, Rada Mihalcea, Michael Terry, Diyi Yang, Meredith Ringel Morris, Paul Resnick, David Jurgens,
• Abstract summary: We argue that the research community should explicitly define and critically reflect on "alignment" to account for the bidirectional and dynamic relationship between humans and AI.<n>We introduce the Bidirectional Human-AI Alignment framework, which not only incorporates traditional efforts to align AI with human values but also introduces the critical, underexplored dimension of aligning humans with AI.
• Score: 109.57781720848669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in general-purpose AI underscore the urgent need to align AI systems with human goals and values. Yet, the lack of a clear, shared understanding of what constitutes "alignment" limits meaningful progress and cross-disciplinary collaboration. In this position paper, we argue that the research community should explicitly define and critically reflect on "alignment" to account for the bidirectional and dynamic relationship between humans and AI. Through a systematic review of over 400 papers spanning HCI, NLP, ML, and more, we examine how alignment is currently defined and operationalized. Building on this analysis, we introduce the Bidirectional Human-AI Alignment framework, which not only incorporates traditional efforts to align AI with human values but also introduces the critical, underexplored dimension of aligning humans with AI -- supporting cognitive, behavioral, and societal adaptation to rapidly advancing AI technologies. Our findings reveal significant gaps in current literature, especially in long-term interaction design, human value modeling, and mutual understanding. We conclude with three central challenges and actionable recommendations to guide future research toward more nuanced, reciprocal, and human-AI alignment approaches.

Related papers

• Human-AI Interaction Alignment: Designing, Evaluating, and Evolving Value-Centered AI For Reciprocal Human-AI Futures [27.995784716141767]
  The rapid integration of generative AI into everyday life underscores the need to move beyond unidirectional alignment models.<n>This workshop focuses on bidirectional human-AI alignment, a dynamic, reciprocal process where humans and AI co-adapt through interaction, evaluation, and value-centered design.
  arXiv  Detail & Related papers  (2025-12-25T07:45:38Z)
• Understanding the Process of Human-AI Value Alignment [1.6799377888527687]
  Value alignment in computer science research is often used to refer to the process of aligning artificial intelligence with humans, but the way the phrase is used often lacks precision.<n>We conduct a systematic literature review to advance the understanding of value alignment in artificial intelligence.
  arXiv  Detail & Related papers  (2025-09-17T09:39:38Z)
• Exploring Societal Concerns and Perceptions of AI: A Thematic Analysis through the Lens of Problem-Seeking [0.0]
  This study introduces a novel conceptual framework distinguishing problem-seeking from problem-solving to clarify the unique features of human intelligence in contrast to AI.<n>The framework emphasizes that while AI excels at efficiency and optimization, it lacks the orientation derived from grounding and the embodiment flexibility intrinsic to human cognition.
  arXiv  Detail & Related papers  (2025-05-29T18:24:34Z)
• Research on Superalignment Should Advance Now with Parallel Optimization of Competence and Conformity [30.24208064228573]
  We argue that superalignment is achievable and research on it should advance immediately.<n>This work sheds light on a practical approach for developing the value-aligned next-generation AI.
  arXiv  Detail & Related papers  (2025-03-08T04:10:11Z)
• The Road to Artificial SuperIntelligence: A Comprehensive Survey of Superalignment [33.27140396561271]
  The emergence of large language models (LLMs) has sparked the possibility of about Artificial Superintelligence (ASI) Superalignment aims to address two primary goals -- scalability in supervision to provide high-quality guidance signals and robust governance to ensure alignment with human values. Specifically, we explore the concept of ASI, the challenges it poses, and the limitations of current alignment paradigms in addressing the superalignment problem.
  arXiv  Detail & Related papers  (2024-12-21T03:51:04Z)
• Aligning Generalisation Between Humans and Machines [74.120848518198]
  Recent advances in AI have resulted in technology that can support humans in scientific discovery and decision support but may also disrupt democracies and target individuals. The responsible use of AI increasingly shows the need for human-AI teaming. A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise.
  arXiv  Detail & Related papers  (2024-11-23T18:36:07Z)
• ValueCompass: A Framework of Fundamental Values for Human-AI Alignment [15.35489011078817]
  We introduce Value, a framework of fundamental values, grounded in psychological theory and a systematic review. We apply Value to measure the value alignment of humans and language models (LMs) across four real-world vignettes. Our findings uncover risky misalignment between humans and LMs, such as LMs agreeing with values like "Choose Own Goals", which are largely disagreed by humans.
  arXiv  Detail & Related papers  (2024-09-15T02:13:03Z)
• Beyond Prompts: Learning from Human Communication for Enhanced AI Intent Alignment [30.93897332124916]
  We study human strategies for intent specification in human-human communication. This study aims to advance toward a human-centered AI system by bringing together human communication strategies for the design of AI systems.
  arXiv  Detail & Related papers  (2024-05-09T11:10:29Z)
• Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review [6.013543974938446]
  Leveraging Artificial Intelligence in decision support systems has disproportionately focused on technological advancements. A human-centered perspective attempts to alleviate this concern by designing AI solutions for seamless integration with existing processes.
  arXiv  Detail & Related papers  (2023-10-30T17:46:38Z)
• AI Alignment: A Comprehensive Survey [70.35693485015659]
  AI alignment aims to make AI systems behave in line with human intentions and values. We identify four principles as the key objectives of AI alignment: Robustness, Interpretability, Controllability, and Ethicality. We decompose current alignment research into two key components: forward alignment and backward alignment.
  arXiv  Detail & Related papers  (2023-10-30T15:52:15Z)
• Next Steps for Human-Centered Generative AI: A Technical Perspective [107.74614586614224]
  We propose next-steps for Human-centered Generative AI (HGAI) By identifying these next-steps, we intend to draw interdisciplinary research teams to pursue a coherent set of emergent ideas in HGAI.
  arXiv  Detail & Related papers  (2023-06-27T19:54:30Z)
• Human-AI Coevolution [48.74579595505374]
  Coevolution AI is a process in which humans and AI algorithms continuously influence each other. This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science.
  arXiv  Detail & Related papers  (2023-06-23T18:10:54Z)
• Artificial intelligence in government: Concepts, standards, and a unified framework [0.0]
  Recent advances in artificial intelligence (AI) hold the promise of transforming government. It is critical that new AI systems behave in alignment with the normative expectations of society.
  arXiv  Detail & Related papers  (2022-10-31T10:57:20Z)
• Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation [61.77881142275982]
  This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them. We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives. We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
  arXiv  Detail & Related papers  (2022-06-08T12:32:08Z)
• On some Foundational Aspects of Human-Centered Artificial Intelligence [52.03866242565846]
  There is no clear definition of what is meant by Human Centered Artificial Intelligence. This paper introduces the term HCAI agent to refer to any physical or software computational agent equipped with AI components. We see the notion of HCAI agent, together with its components and functions, as a way to bridge the technical and non-technical discussions on human-centered AI.
  arXiv  Detail & Related papers  (2021-12-29T09:58:59Z)

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.