# Positive AI: Key Challenges in Designing Artificial Intelligence for Wellbeing

arXiv: https://arxiv.org/abs/2304.12241 · HTML: https://mlsanity.com/paper/2304.12241/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 135 references that are in the map.
- First posted on arXiv 12 Apr 2023.

## Builds on (most cited first, 19 of 19)

- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models](https://mlsanity.com/paper/2201.03544/) (2022)
- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [Incomplete Contracting and AI Alignment](https://mlsanity.com/paper/1804.04268/) (2018)
- [The Problem with Metrics is a Fundamental Problem for AI](https://mlsanity.com/paper/2002.08512/) (2020)
- [Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics](https://mlsanity.com/paper/2202.08792/) (2022)
- [Hard Choices in Artificial Intelligence](https://mlsanity.com/paper/2106.11022/) (2021)
- [Building Human Values into Recommender Systems: An Interdisciplinary Synthesis](https://mlsanity.com/paper/2207.10192/) (2022)
- [A Human Rights-Based Approach to Responsible AI](https://mlsanity.com/paper/2210.02667/) (2022)
- [Ethics as a service: a pragmatic operationalisation of AI Ethics](https://mlsanity.com/paper/2102.09364/) (2021)
- [AI Ethics Principles in Practice: Perspectives of Designers and Developers](https://mlsanity.com/paper/2112.07467/) (2021)
- [IEEE 7010: A New Standard for Assessing the Well-being Implications of Artificial Intelligence](https://mlsanity.com/paper/2005.06620/) (2020)
- [Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation](https://mlsanity.com/paper/2004.09980/) (2020)
- [Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes](https://mlsanity.com/paper/2303.03367/) (2023)
- [Enhanced well-being assessment as basis for the practical implementation of ethical and rights-based normative principles for AI](https://mlsanity.com/paper/2007.14826/) (2020)

## Built on by (most cited first, 1 of 1)

- [Developing and Evaluating a Design Method for Positive Artificial Intelligence](https://mlsanity.com/paper/2402.01499/) (2024)

## Abstract (arXiv)

Artificial Intelligence (AI) is a double-edged sword: on one hand, AI promises to provide great advances that could benefit humanity, but on the other hand, AI poses substantial (even existential) risks. With advancements happening daily, many people are increasingly worried about AI's impact on their lives. To ensure AI progresses beneficially, some researchers have proposed "wellbeing" as a key objective to govern AI. This article addresses key challenges in designing AI for wellbeing. We group these challenges into issues of modeling wellbeing in context, assessing wellbeing in context, designing interventions to improve wellbeing, and maintaining AI alignment with wellbeing over time. The identification of these challenges provides a scope for efforts to help ensure that AI developments are aligned with human wellbeing.