Designing Trustworthy AI: A Human-Machine Teaming Framework to Guide Development
Not on the map. Why?
Abstract (arXiv)
Artificial intelligence (AI) holds great promise to empower us with knowledge and augment our effectiveness. We can -- and must -- ensure that we keep humans safe and in control, particularly with regard to government and public sector applications that affect broad populations. How can AI development teams harness the power of AI systems and design them to be valuable to humans? Diverse teams are needed to build trustworthy artificial intelligent systems, and those teams need to coalesce around a shared set of ethics. There are many discussions in the AI field about ethics and trust, but there are few frameworks available for people to use as guidance when creating these systems. The Human-Machine Teaming (HMT) Framework for Designing Ethical AI Experiences described in this paper, when used with a set of technical ethics, will guide AI development teams to create AI systems that are accountable, de-risked, respectful, secure, honest, and usable. To support the team's efforts, activities to understand people's needs and concerns will be introduced along with the themes to support the team's efforts. For example, usability testing can help determine if the audience understands how the AI system works and complies with the HMT Framework. The HMT Framework is based on reviews of existing ethical codes and best practices in human-computer interaction and software development. Human-machine teams are strongest when human users can trust AI systems to behave as expected, safely, securely, and understandably. Using the HMT Framework to design trustworthy AI systems will provide support to teams in identifying potential issues ahead of time and making great experiences for humans.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Oct 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 2; 2025: 1.
8 papers.
- 2023A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspectivecited by 33
- 2022A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunitiescited by 8
- 2023Human-centered trust framework: An HCI perspectivecited by 2
- 2024Integrating ESG and AI: A Comprehensive Responsible AI Assessment Frameworkcited by 2
- 2023Challenges and Trends in User Trust Discourse in AIcited by 1
- 2021Requirements for Open Political Information: Transparency Beyond Open Datacited by 0
- 2024Achieving Responsible AI through ESG: Insights and Recommendations from Industry Engagementcited by 0
- 2025Advancing Human-Machine Teaming: Concepts, Challenges, and Applicationscited by 0
- 2025Advancing Human-Machine Teaming: Concepts, Challenges, and Applications16 Mar 2025
- 2024Achieving Responsible AI through ESG: Insights and Recommendations from Industry Engagement30 Aug 2024
- 2024Integrating ESG and AI: A Comprehensive Responsible AI Assessment Framework2 Aug 2024
- 2023Challenges and Trends in User Trust Discourse in AI5 May 2023
- 2023Human-centered trust framework: An HCI perspective5 May 2023
- 2023A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective18 Apr 2023
- 2022A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities17 Oct 2022
- 2021Requirements for Open Political Information: Transparency Beyond Open Data6 Dec 2021