Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions

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·June 13, 2024

Summary

The systematic review investigates human-AI alignment, addressing the need for clarity and a more nuanced approach. It introduces the "Bidirectional Human-AI Alignment" framework, considering both AI systems aligning with human intentions and humans adapting to AI. Over 400 papers from 2019 to 2024, covering domains like HCI, NLP, and ML, are analyzed, discussing human values, interaction methods, and evaluation techniques. Key challenges include long-term alignment, managing AI risks, and ensuring human-centered design. The review proposes future research directions, emphasizing the dynamic nature of human objectives and the importance of adapting AI to evolving values. The papers collectively contribute to a comprehensive understanding of the topic, with a focus on interdisciplinary collaboration and the development of frameworks to guide AI development in a responsible and human-aligned manner.

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of bidirectional human-AI alignment, focusing on how to ensure that AI produces intended outcomes without undesirable side effects, aligning AI systems' objectives with those of humans, and adapting AI to changes in human values and societal evolution . This problem is not entirely new but has gained increasing attention due to the advancement of AI technologies and their integration into various aspects of society, raising concerns about potential risks and ethical implications . The paper contributes by providing a systematic review and proposing a framework to guide research efforts in this critical area of human-AI interaction and alignment .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to Bidirectional Human-AI Alignment. It focuses on exploring the alignment between humans and artificial intelligence systems, aiming to understand and improve the interaction, collaboration, and mutual understanding between the two entities . The research delves into various aspects such as Human-AI Collaboration, Interaction Mode, Integrate General Value, Assessment of Collaboration and Impact, and Human Value Representation to enhance the alignment between humans and AI . The study involves a systematic review to clarify the framework and future directions for achieving bidirectional alignment between humans and AI systems .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions" proposes several new ideas, methods, and models in the field of human-AI alignment . Some of the key contributions include:

  • Bidirectional Human-AI Alignment Framework: The paper introduces a bidirectional framework that focuses on aligning AI to humans and aligning humans to AI . This framework aims to ensure that AI produces intended outcomes determined by humans and helps individuals and society adjust to AI advancements cognitively and behaviorally.

  • Interaction Techniques and Evaluations: The paper discusses key findings related to human values, interaction techniques, and evaluations in the context of human-AI alignment .

  • Future Directions and Challenges: The authors envision three key challenges for future research directions and propose potential solutions . These challenges likely address the evolving landscape of human-AI interaction and alignment.

  • Collaborative and Social Computing Systems: The paper delves into human-centered computing aspects, including interactive systems, tools, collaborative, and social computing systems . This indicates a focus on enhancing the collaboration and interaction between humans and AI systems.

  • Empirical Studies in HCI: The research incorporates empirical studies in Human-Computer Interaction (HCI) to provide insights into the practical aspects of human-AI alignment . This empirical approach likely contributes to the development of effective alignment strategies.

  • Personalized AI Systems: The paper explores the concept of personalized AI systems, which could involve tailoring AI interactions to individual preferences and needs . This personalization aspect is crucial for enhancing user experience and alignment with AI technologies. The paper "Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions" introduces novel characteristics and advantages compared to previous methods in the field of human-AI alignment . Some key points include:

  • Interaction Techniques Across Various Domains: The paper highlights the differences in interaction techniques between AI-centered (NLP/ML) and human-centered (HCI) studies. NLP/ML studies primarily utilize numeric-based and natural language-based techniques, while HCI studies cover diverse graphical multi-modal interaction signals beyond text and images .

  • Different Interaction Techniques in Separate Stages: The research identifies the use of different interaction techniques in separate stages, especially in the NLP/ML fields. This includes leveraging implicit feedback in the learning stage and exploring various interaction modes such as multi-turn dialogue, text inputs/edits, and multi-modal visualization in the inference stage .

  • Expectations and Evaluations of Values: The paper delves into how expectations and evaluations of values may differ between humans and AI systems. It discusses the challenges in evaluating values like honesty and the need to calibrate human expectations of AI values through mechanistic interpretability .

  • Human Value Specification: The research emphasizes the importance of selecting suitable interaction techniques for humans to specify AI values for alignment. It explores the status quo of existing techniques in both human-centered and AI-centered research domains, highlighting the strengths and insights each domain offers .

These characteristics and advancements in the paper provide a comprehensive analysis of interaction techniques, value evaluation, and human-AI alignment strategies, contributing to the development of more effective and nuanced approaches in the field of human-AI alignment.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of bidirectional human-AI alignment. Noteworthy researchers who have contributed to this topic include Carrie J Cai, Samantha Winter, David Steiner, Lauren Wilcox, Michael Terry, Xin-Qiang Cai, Yu-Jie Zhang, Chao-Kai Chiang, Masashi Sugiyama, Chengzhi Cao, Yinghao Fu, Sheng Xu, Ruimao Zhang, Shuang Li, Micah Carroll, Davis Foote, Anand Siththaranjan, Stuart Russell, Anca Dragan, among others . The key to the solution mentioned in the paper involves enhancing human-AI collaboration through logic-guided reasoning, supporting high-uncertainty decisions through AI and logic-style explanations, and exploring the utility of learning about humans for human-AI coordination .


How were the experiments in the paper designed?

The experiments in the paper were designed through a systematic literature review process based on the PRISMA guideline. The workflow involved several key steps:

  1. Identification and Screening with Keywords: The initial stage involved identifying papers published in AI-related domain venues from a specific timeframe. A total of 34,213 papers were identified through keyword searches .
  2. Assessing Eligibility with Criteria: Papers were further screened based on specific criteria, including keywords related to human-AI alignment. A total of 2,136 papers were included in the screening stage .
  3. Paper Coding and Analysis: The final corpus of 411 papers was selected for qualitative coding and analysis to develop the bidirectional human-AI alignment framework .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is a corpus of 411 papers that were analyzed in detail using qualitative coding . The codebook was developed through qualitative coding for each paper by identifying relevant sentences that could answer specific research questions . However, the information provided does not specify whether the code used for the analysis is open source or not.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that require verification. The systematic literature review process followed a rigorous methodology based on the PRISMA guideline, ensuring a comprehensive selection and refinement of relevant papers . The study identified a significant number of records through keyword filtering and screening, ultimately arriving at a final corpus of 411 papers for qualitative coding and analysis .

Furthermore, the paper discusses potential future solutions that aim to enhance human-AI collaboration by developing validation mechanisms for interpreting and verifying AI outputs. These solutions include interactive interfaces for requesting justifications from AI, integrating tools for verifying AI truthfulness, and designing interfaces for group validation of AI outputs . These strategies contribute to addressing the scientific hypotheses by proposing practical approaches to ensure the accuracy and reliability of AI systems.

Moreover, the research direction of empowering humans to understand and control AI's instrumental goals to safeguard co-adaptation between humans and AI is crucial for aligning human-AI interactions . By focusing on developing interactive strategies to enhance human capabilities through learning from advanced AI, the study aims to assess how individuals and society adapt to AI advancements, guiding the future evolution of AI . These aspects of the paper provide valuable insights into the alignment of human values, behaviors, and capabilities with AI advancements.


What are the contributions of this paper?

The paper "Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions" has several key contributions made by the authors and advisors involved in the project :

  • The authors conducted a systematic literature review based on the PRISMA guideline to develop a bidirectional human-AI alignment framework, focusing on dimensions such as developing AI with general values, customizing AI for individuals/groups, AI evaluation, interaction modes to specify values for AI, perceiving and understanding AI, critical thinking about AI, human collaborating with diverse AI roles, and assessing AI impacts on humans and society.
  • Savvas Petridis, Yi-Hao Peng, Li Qiwei, Sushrita Rakshit, Chenglei Si, Yutong Xie, and advisors like Jeffrey P. Bigham and Frank Bentley contributed to various aspects of the project, including filtering papers, coding, ideating frameworks, writing sections, and participating in paper revision and proofreading.
  • The paper addresses research questions related to human-AI alignment, explores the impact of AI assistance on incidental learning, discusses methods to reduce harms in language models, and delves into topics like extractive QA, bandit learning from user feedback, collaborative qualitative analysis, and dialogue response ranking training with human feedback data.
  • The authors also analyzed the relative priority of values in human-AI alignment, emphasizing the importance of understanding the ordered system of human values and the potential trade-offs between different values in guiding actions and behaviors.
  • The paper provides insights into the design space of human-AI interaction in text summarization, the effects of machine learning literacy interventions on laypeople's reliance on ML models, and the exploration of human-AI collaborative decision-making processes.
  • Additionally, the authors discuss the development of an automatic planning system for interactive decision-making tasks with large language models, investigate user-GPT interactions, and highlight the utility of explainable AI in human-machine teaming scenarios.
  • The contributions of the paper extend to mapping and mitigating misaligned models, measuring trade-offs between rewards and ethical behavior in benchmarks, and introducing AI training methodologies for healthcare applications.

What work can be continued in depth?

Future research in the field of human-AI alignment can be continued in depth by focusing on several key areas identified in the systematic review:

  • Specification Game: This involves integrating fully specified human values into aligning AI and eliciting nuanced and contextual human values during diverse interactions .
  • Dynamic Co-evolution of Alignment: Understanding and navigating the dynamic interplay among human values, societal evolution, and AI progression. This includes co-evolving AI with changes in humans and society, ensuring alignment goals remain consistent with human values as AI systems evolve .
  • Safeguarding Coadaptation: Decomposing AI final goals into interpretable and controllable instrumental actions, and empowering humans to identify and intervene in AI instrumental and final strategies in collaboration. This ensures the alignment between humans and AI is maintained .

Introduction
Background
Emergence of human-AI alignment as a critical issue
Importance of clarity and nuanced approach
Objective
To provide a comprehensive overview
Develop the Bidirectional Human-AI Alignment framework
Address key challenges and future research directions
Methodology
Data Collection
Timeframe: 2019-2024
Domains: HCI, NLP, ML
Inclusion criteria and search strategy
Data Preprocessing
Paper selection process
Content extraction and synthesis
Analysis
Human values and their role in alignment
Interaction methods between humans and AI
Evaluation techniques for human-AI alignment
Key Findings
Long-term Alignment
Ensuring consistency over time
Addressing drift and unforeseen consequences
Managing AI Risks
Risk assessment and mitigation strategies
Ethical considerations in AI development
Human-Centered Design
Importance of user experience and adaptability
Evolving human objectives and AI adaptation
Challenges and Future Research
Dynamic nature of human objectives
Adapting AI to changing societal values
Interdisciplinary collaboration for alignment
Frameworks for responsible AI development
Conclusion
Summary of key insights
The Bidirectional Human-AI Alignment framework's significance
Call to action for researchers and practitioners
Basic info
papers
computation and language
human-computer interaction
artificial intelligence
Advanced features
Insights
What domains does the analysis of over 400 papers cover?
What framework does the review introduce for human-AI alignment?
What are some key challenges mentioned in the review regarding human-AI alignment?
What is the primary focus of the systematic review in question?

Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions

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·June 13, 2024

Summary

The systematic review investigates human-AI alignment, addressing the need for clarity and a more nuanced approach. It introduces the "Bidirectional Human-AI Alignment" framework, considering both AI systems aligning with human intentions and humans adapting to AI. Over 400 papers from 2019 to 2024, covering domains like HCI, NLP, and ML, are analyzed, discussing human values, interaction methods, and evaluation techniques. Key challenges include long-term alignment, managing AI risks, and ensuring human-centered design. The review proposes future research directions, emphasizing the dynamic nature of human objectives and the importance of adapting AI to evolving values. The papers collectively contribute to a comprehensive understanding of the topic, with a focus on interdisciplinary collaboration and the development of frameworks to guide AI development in a responsible and human-aligned manner.
Mind map
Evolving human objectives and AI adaptation
Importance of user experience and adaptability
Ethical considerations in AI development
Risk assessment and mitigation strategies
Addressing drift and unforeseen consequences
Ensuring consistency over time
Evaluation techniques for human-AI alignment
Interaction methods between humans and AI
Human values and their role in alignment
Content extraction and synthesis
Paper selection process
Inclusion criteria and search strategy
Domains: HCI, NLP, ML
Timeframe: 2019-2024
Address key challenges and future research directions
Develop the Bidirectional Human-AI Alignment framework
To provide a comprehensive overview
Importance of clarity and nuanced approach
Emergence of human-AI alignment as a critical issue
Call to action for researchers and practitioners
The Bidirectional Human-AI Alignment framework's significance
Summary of key insights
Frameworks for responsible AI development
Interdisciplinary collaboration for alignment
Adapting AI to changing societal values
Dynamic nature of human objectives
Human-Centered Design
Managing AI Risks
Long-term Alignment
Analysis
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Challenges and Future Research
Key Findings
Methodology
Introduction
Outline
Introduction
Background
Emergence of human-AI alignment as a critical issue
Importance of clarity and nuanced approach
Objective
To provide a comprehensive overview
Develop the Bidirectional Human-AI Alignment framework
Address key challenges and future research directions
Methodology
Data Collection
Timeframe: 2019-2024
Domains: HCI, NLP, ML
Inclusion criteria and search strategy
Data Preprocessing
Paper selection process
Content extraction and synthesis
Analysis
Human values and their role in alignment
Interaction methods between humans and AI
Evaluation techniques for human-AI alignment
Key Findings
Long-term Alignment
Ensuring consistency over time
Addressing drift and unforeseen consequences
Managing AI Risks
Risk assessment and mitigation strategies
Ethical considerations in AI development
Human-Centered Design
Importance of user experience and adaptability
Evolving human objectives and AI adaptation
Challenges and Future Research
Dynamic nature of human objectives
Adapting AI to changing societal values
Interdisciplinary collaboration for alignment
Frameworks for responsible AI development
Conclusion
Summary of key insights
The Bidirectional Human-AI Alignment framework's significance
Call to action for researchers and practitioners

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of bidirectional human-AI alignment, focusing on how to ensure that AI produces intended outcomes without undesirable side effects, aligning AI systems' objectives with those of humans, and adapting AI to changes in human values and societal evolution . This problem is not entirely new but has gained increasing attention due to the advancement of AI technologies and their integration into various aspects of society, raising concerns about potential risks and ethical implications . The paper contributes by providing a systematic review and proposing a framework to guide research efforts in this critical area of human-AI interaction and alignment .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to Bidirectional Human-AI Alignment. It focuses on exploring the alignment between humans and artificial intelligence systems, aiming to understand and improve the interaction, collaboration, and mutual understanding between the two entities . The research delves into various aspects such as Human-AI Collaboration, Interaction Mode, Integrate General Value, Assessment of Collaboration and Impact, and Human Value Representation to enhance the alignment between humans and AI . The study involves a systematic review to clarify the framework and future directions for achieving bidirectional alignment between humans and AI systems .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions" proposes several new ideas, methods, and models in the field of human-AI alignment . Some of the key contributions include:

  • Bidirectional Human-AI Alignment Framework: The paper introduces a bidirectional framework that focuses on aligning AI to humans and aligning humans to AI . This framework aims to ensure that AI produces intended outcomes determined by humans and helps individuals and society adjust to AI advancements cognitively and behaviorally.

  • Interaction Techniques and Evaluations: The paper discusses key findings related to human values, interaction techniques, and evaluations in the context of human-AI alignment .

  • Future Directions and Challenges: The authors envision three key challenges for future research directions and propose potential solutions . These challenges likely address the evolving landscape of human-AI interaction and alignment.

  • Collaborative and Social Computing Systems: The paper delves into human-centered computing aspects, including interactive systems, tools, collaborative, and social computing systems . This indicates a focus on enhancing the collaboration and interaction between humans and AI systems.

  • Empirical Studies in HCI: The research incorporates empirical studies in Human-Computer Interaction (HCI) to provide insights into the practical aspects of human-AI alignment . This empirical approach likely contributes to the development of effective alignment strategies.

  • Personalized AI Systems: The paper explores the concept of personalized AI systems, which could involve tailoring AI interactions to individual preferences and needs . This personalization aspect is crucial for enhancing user experience and alignment with AI technologies. The paper "Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions" introduces novel characteristics and advantages compared to previous methods in the field of human-AI alignment . Some key points include:

  • Interaction Techniques Across Various Domains: The paper highlights the differences in interaction techniques between AI-centered (NLP/ML) and human-centered (HCI) studies. NLP/ML studies primarily utilize numeric-based and natural language-based techniques, while HCI studies cover diverse graphical multi-modal interaction signals beyond text and images .

  • Different Interaction Techniques in Separate Stages: The research identifies the use of different interaction techniques in separate stages, especially in the NLP/ML fields. This includes leveraging implicit feedback in the learning stage and exploring various interaction modes such as multi-turn dialogue, text inputs/edits, and multi-modal visualization in the inference stage .

  • Expectations and Evaluations of Values: The paper delves into how expectations and evaluations of values may differ between humans and AI systems. It discusses the challenges in evaluating values like honesty and the need to calibrate human expectations of AI values through mechanistic interpretability .

  • Human Value Specification: The research emphasizes the importance of selecting suitable interaction techniques for humans to specify AI values for alignment. It explores the status quo of existing techniques in both human-centered and AI-centered research domains, highlighting the strengths and insights each domain offers .

These characteristics and advancements in the paper provide a comprehensive analysis of interaction techniques, value evaluation, and human-AI alignment strategies, contributing to the development of more effective and nuanced approaches in the field of human-AI alignment.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of bidirectional human-AI alignment. Noteworthy researchers who have contributed to this topic include Carrie J Cai, Samantha Winter, David Steiner, Lauren Wilcox, Michael Terry, Xin-Qiang Cai, Yu-Jie Zhang, Chao-Kai Chiang, Masashi Sugiyama, Chengzhi Cao, Yinghao Fu, Sheng Xu, Ruimao Zhang, Shuang Li, Micah Carroll, Davis Foote, Anand Siththaranjan, Stuart Russell, Anca Dragan, among others . The key to the solution mentioned in the paper involves enhancing human-AI collaboration through logic-guided reasoning, supporting high-uncertainty decisions through AI and logic-style explanations, and exploring the utility of learning about humans for human-AI coordination .


How were the experiments in the paper designed?

The experiments in the paper were designed through a systematic literature review process based on the PRISMA guideline. The workflow involved several key steps:

  1. Identification and Screening with Keywords: The initial stage involved identifying papers published in AI-related domain venues from a specific timeframe. A total of 34,213 papers were identified through keyword searches .
  2. Assessing Eligibility with Criteria: Papers were further screened based on specific criteria, including keywords related to human-AI alignment. A total of 2,136 papers were included in the screening stage .
  3. Paper Coding and Analysis: The final corpus of 411 papers was selected for qualitative coding and analysis to develop the bidirectional human-AI alignment framework .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is a corpus of 411 papers that were analyzed in detail using qualitative coding . The codebook was developed through qualitative coding for each paper by identifying relevant sentences that could answer specific research questions . However, the information provided does not specify whether the code used for the analysis is open source or not.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that require verification. The systematic literature review process followed a rigorous methodology based on the PRISMA guideline, ensuring a comprehensive selection and refinement of relevant papers . The study identified a significant number of records through keyword filtering and screening, ultimately arriving at a final corpus of 411 papers for qualitative coding and analysis .

Furthermore, the paper discusses potential future solutions that aim to enhance human-AI collaboration by developing validation mechanisms for interpreting and verifying AI outputs. These solutions include interactive interfaces for requesting justifications from AI, integrating tools for verifying AI truthfulness, and designing interfaces for group validation of AI outputs . These strategies contribute to addressing the scientific hypotheses by proposing practical approaches to ensure the accuracy and reliability of AI systems.

Moreover, the research direction of empowering humans to understand and control AI's instrumental goals to safeguard co-adaptation between humans and AI is crucial for aligning human-AI interactions . By focusing on developing interactive strategies to enhance human capabilities through learning from advanced AI, the study aims to assess how individuals and society adapt to AI advancements, guiding the future evolution of AI . These aspects of the paper provide valuable insights into the alignment of human values, behaviors, and capabilities with AI advancements.


What are the contributions of this paper?

The paper "Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions" has several key contributions made by the authors and advisors involved in the project :

  • The authors conducted a systematic literature review based on the PRISMA guideline to develop a bidirectional human-AI alignment framework, focusing on dimensions such as developing AI with general values, customizing AI for individuals/groups, AI evaluation, interaction modes to specify values for AI, perceiving and understanding AI, critical thinking about AI, human collaborating with diverse AI roles, and assessing AI impacts on humans and society.
  • Savvas Petridis, Yi-Hao Peng, Li Qiwei, Sushrita Rakshit, Chenglei Si, Yutong Xie, and advisors like Jeffrey P. Bigham and Frank Bentley contributed to various aspects of the project, including filtering papers, coding, ideating frameworks, writing sections, and participating in paper revision and proofreading.
  • The paper addresses research questions related to human-AI alignment, explores the impact of AI assistance on incidental learning, discusses methods to reduce harms in language models, and delves into topics like extractive QA, bandit learning from user feedback, collaborative qualitative analysis, and dialogue response ranking training with human feedback data.
  • The authors also analyzed the relative priority of values in human-AI alignment, emphasizing the importance of understanding the ordered system of human values and the potential trade-offs between different values in guiding actions and behaviors.
  • The paper provides insights into the design space of human-AI interaction in text summarization, the effects of machine learning literacy interventions on laypeople's reliance on ML models, and the exploration of human-AI collaborative decision-making processes.
  • Additionally, the authors discuss the development of an automatic planning system for interactive decision-making tasks with large language models, investigate user-GPT interactions, and highlight the utility of explainable AI in human-machine teaming scenarios.
  • The contributions of the paper extend to mapping and mitigating misaligned models, measuring trade-offs between rewards and ethical behavior in benchmarks, and introducing AI training methodologies for healthcare applications.

What work can be continued in depth?

Future research in the field of human-AI alignment can be continued in depth by focusing on several key areas identified in the systematic review:

  • Specification Game: This involves integrating fully specified human values into aligning AI and eliciting nuanced and contextual human values during diverse interactions .
  • Dynamic Co-evolution of Alignment: Understanding and navigating the dynamic interplay among human values, societal evolution, and AI progression. This includes co-evolving AI with changes in humans and society, ensuring alignment goals remain consistent with human values as AI systems evolve .
  • Safeguarding Coadaptation: Decomposing AI final goals into interpretable and controllable instrumental actions, and empowering humans to identify and intervene in AI instrumental and final strategies in collaboration. This ensures the alignment between humans and AI is maintained .
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