IEEE 7010: A New Standard for Assessing the Well-being Implications of Artificial Intelligence
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Abstract (arXiv)
Artificial intelligence (AI) enabled products and services are becoming a staple of everyday life. While governments and businesses are eager to enjoy the benefits of AI innovations, the mixed impact of these autonomous and intelligent systems on human well-being has become a pressing issue. This article introduces one of the first international standards focused on the social and ethical implications of AI: The Institute of Electrical and Electronics Engineering (IEEE) Standard (Std) 7010-2020 Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well-being. Incorporating well-being factors throughout the lifecycle of AI is both challenging and urgent and IEEE 7010 provides key guidance for those who design, deploy, and procure these technologies. We begin by articulating the benefits of an approach for AI centered around well-being and the measurement of well-being data. Next, we provide an overview of IEEE 7010, including its key principles and how the standard relates to approaches and perspectives in place in the AI community. Finally, we indicate where future efforts are needed.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 May 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 5; 2025: 2.
11 papers.
- 2021What are you optimizing for? Aligning Recommender Systems with Human Valuescited by 28
- 2024Towards Ethical Personal AI Applications: Practical Considerations for AI Assistants with Long-Term Memorycited by 6
- 2024Improved Emotional Alignment of AI and Humans: Human Ratings of Emotions Expressed by Stable Diffusion v1, DALL-E 2, and DALL-E 3cited by 3
- 2025Embodied AI: Emerging Risks and Opportunities for Policy Actioncited by 3
- 2023Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systemscited by 2
- 2024Developing and Evaluating a Design Method for Positive Artificial Intelligencecited by 2
- 2023Positive AI: Key Challenges in Designing Artificial Intelligence for Wellbeingcited by 1
- 2022Needs and Artificial Intelligencecited by 0
- 2024User-Centric AI Analytics for Chronic Health Conditions Managementcited by 0
- 2024Towards Robust Training Datasets for Machine Learning with Ontologies: A Case Study for Emergency Road Vehicle Detectioncited by 0
- 2025HH4AI: A methodological Framework for AI Human Rights impact assessment under the EUAI ACTcited by 0
- 2025Embodied AI: Emerging Risks and Opportunities for Policy Action28 Aug 2025
- 2025HH4AI: A methodological Framework for AI Human Rights impact assessment under the EUAI ACT23 Mar 2025
- 2024Towards Ethical Personal AI Applications: Practical Considerations for AI Assistants with Long-Term Memory17 Sep 2024
- 2024Towards Robust Training Datasets for Machine Learning with Ontologies: A Case Study for Emergency Road Vehicle Detection21 Jun 2024
- 2024Improved Emotional Alignment of AI and Humans: Human Ratings of Emotions Expressed by Stable Diffusion v1, DALL-E 2, and DALL-E 328 May 2024
- 2024Developing and Evaluating a Design Method for Positive Artificial Intelligence2 Feb 2024
- 2024User-Centric AI Analytics for Chronic Health Conditions Management9 Jan 2024
- 2023Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systems22 Jun 2023
- 2023Positive AI: Key Challenges in Designing Artificial Intelligence for Wellbeing12 Apr 2023
- 2022Needs and Artificial Intelligence18 Feb 2022
- 2021What are you optimizing for? Aligning Recommender Systems with Human Values22 Jul 2021