# An Ethical Framework for Guiding the Development of Affectively-Aware Artificial Intelligence

arXiv: https://arxiv.org/abs/2107.13734 · HTML: https://mlsanity.com/paper/2107.13734/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 55 references that are in the map.
- First posted on arXiv 29 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Modeling emotion in complex stories: the Stanford Emotional Narratives Dataset](https://mlsanity.com/paper/1912.05008/) (2019)
- [Applying Probabilistic Programming to Affective Computing](https://mlsanity.com/paper/1903.06445/) (2019)
- [Smile for the Camera: Privacy and Policy Implications of Emotion AI](https://mlsanity.com/paper/1709.00396/) (2017)

## Built on by (most cited first, 4 of 4)

- [Human-like Affective Cognition in Foundation Models](https://mlsanity.com/paper/2409.11733/) (2024)
- [Using Knowledge-Embedded Attention to Augment Pre-trained Language Models for Fine-Grained Emotion Recognition](https://mlsanity.com/paper/2108.00194/) (2021)
- [Affective Conversational Agents: Understanding Expectations and Personal Influences](https://mlsanity.com/paper/2310.12459/) (2023)
- [Documenting use cases in the affective computing domain using Unified Modeling Language](https://mlsanity.com/paper/2209.09666/) (2022)

## Abstract (arXiv)

The recent rapid advancements in artificial intelligence research and deployment have sparked more discussion about the potential ramifications of socially- and emotionally-intelligent AI. The question is not if research can produce such affectively-aware AI, but when it will. What will it mean for society when machines -- and the corporations and governments they serve -- can "read" people's minds and emotions? What should developers and operators of such AI do, and what should they not do? The goal of this article is to pre-empt some of the potential implications of these developments, and propose a set of guidelines for evaluating the (moral and) ethical consequences of affectively-aware AI, in order to guide researchers, industry professionals, and policy-makers. We propose a multi-stakeholder analysis framework that separates the ethical responsibilities of AI Developers vis-`a-vis the entities that deploy such AI -- which we term Operators. Our analysis produces two pillars that clarify the responsibilities of each of these stakeholders: Provable Beneficence, which rests on proving the effectiveness of the AI, and Responsible Stewardship, which governs responsible collection, use, and storage of data and the decisions made from such data. We end with recommendations for researchers, developers, operators, as well as regulators and law-makers.