keynote

Does My AI Help or Hurt? Exploring Human-AI Complementarity

Author:

Kori InkpenAuthors Info & Claims

UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

Page 2

https://doi.org/10.1145/3340631.3395384

Published: 13 July 2020 Publication History

Get Access

Abstract

In a world where the use of AI is growing and evolving, where will we be in 5 years? 10 years? 20 years? What role will AI play in our society, and how will humans and AI interact? While there will undoubtedly be scenarios where AI systems will be able to outperform humans, there will also continue to be instances where humans will be a critical part of the process. As researchers explore improvements to AI systems, we also need to explore the interplay between humans and AI, and continue to evolve our understanding of how humans and AI systems can work together, effectively harnessing the benefits of both systems [3].

Designing effective interaction between the human and the AI systems is critical for future use of Human-AI systems [1]. Merely building an AI system that blindly sends recommendations to users has been shown in some cases to decrease human performance [2]. Different models can also have differential impact on user's trust of the model, adherence to the recommendation, and can impact bias in decision making tasks. This talk will highlight important directions for Human-AI research.

References

[1]

Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul Bennett, Kori Inkpen, Jaime Teevan, Ruth Kikin-Gil, and Eric Horvitz. 2019. Guidelines for human-AI interaction. In Proceedings of the 2019 Conference on Human Factors in Computing Systems (CHI 2019). ACM.

Digital Library

Google Scholar

[2]

Ben Green and Yiling Chen. 2019. Disparate interactions: An algorithm-in-the-loop analysis of fairness in risk assessments. In Conference on Fairness, Accountability, and Transparency. 90--99.

Digital Library

Google Scholar

[3]

Ece Kamar. 2016. Directions in Hybrid Intelligence: Complementing AI Systems with Human Intelligence. In Twenty-Fifth International Joint Conference on Artificial Intelligence. 4070--4073.

Google Scholar

[4]

Keri Mallari, Kori Inkpen, Paul Johns, Sarah Tan, Divya Ramesh, and Ece Kamar. 2020. Do I Look Like a Criminal? Examining how Race Presentation Impacts Human Judgement of Recidivism. In Proceedings of the 2020 Conference on Human Factors in Computing Systems (CHI 2020). ACM.

Digital Library

Google Scholar

[5]

Andi Peng, Besmira Nushi, Emre Kiciman, Kori Inkpen, Siddharth Suri, and Ece Kamar. 2019. What you see is what you get? The impact of representation criteria on human bias in hiring. In AAAI Conference on Human Computation and Crowdsourcing. 125--134.

Crossref

Google Scholar

Cited By

View all

Cabitza FCampagner ARonzio LCameli MMandoli GPastore MSconfienza LFolgado DBarandas MGamboa H(2023)Rams, hounds and white boxes: Investigating human–AI collaboration protocols in medical diagnosisArtificial Intelligence in Medicine10.1016/j.artmed.2023.102506138(102506)Online publication date: Apr-2023
https://doi.org/10.1016/j.artmed.2023.102506
Sankaran SZhang CAarts HMarkopoulos P(2021)Exploring Peoples' Perception of Autonomy and Reactance in Everyday AI InteractionsFrontiers in Psychology10.3389/fpsyg.2021.71307412Online publication date: 29-Sep-2021
https://doi.org/10.3389/fpsyg.2021.713074
Ueda TOgishi T(2021)Sustainable Platform for Environmental Changes in Smart City2021 6th International Conference on Smart and Sustainable Technologies (SpliTech)10.23919/SpliTech52315.2021.9566381(1-6)Online publication date: 8-Sep-2021
https://doi.org/10.23919/SpliTech52315.2021.9566381
Show More Cited By

Recommendations

"An Ideal Human": Expectations of AI Teammates in Human-AI Teaming
CSCW

Driven by state-of-the-art AI technologies, human-AI collaboration has become an important area in computer-supported teamwork research. While human-AI collaboration has been investigated in various domains, more research is needed to explore human ...
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction
CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Despite the proliferation of explainable AI (XAI) methods, little is understood about end-users’ explainability needs and behaviors around XAI explanations. To address this gap and contribute to understanding how explainability can support human-AI ...
Trust in an AI versus a Human teammate: The effects of teammate identity and performance on Human-AI cooperation
Abstract
Recent advances in artificial intelligence (AI) enable researchers to create more powerful AI agents that are becoming competent teammates for humans. However, human distrust of AI is a critical factor that may impede human-AI ...
Highlights
- Humans accept their AI teammate's decision less often when they are deceived about the identity of the AI as another human.

Comments

Information & Contributors

Information

Published In

UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

July 2020

426 pages

ISBN:9781450368612

DOI:10.1145/3340631

Editors:
Tsvi Kuflik
University of Haifa, Israel
,
Ilaria Torre
University of Genoa, Italy
,
Robin Burke
University of Colorado, Boulder, USA
,
Cristina Gena
University of Turin, Italy

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2020

Check for updates

Author Tags

Qualifiers

Keynote

Conference

UMAP '20

Sponsor:

UMAP '20: 28th ACM Conference on User Modeling, Adaptation and Personalization

July 14 - 17, 2020

Genoa, Italy

Acceptance Rates

Overall Acceptance Rate 162 of 633 submissions, 26%

Upcoming Conference

UMAP '25

Sponsor:
sigchi
sigchi

33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

New York City , NY , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
742
Total Downloads

Downloads (Last 12 months)58
Downloads (Last 6 weeks)5

Reflects downloads up to 21 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Cabitza FCampagner ARonzio LCameli MMandoli GPastore MSconfienza LFolgado DBarandas MGamboa H(2023)Rams, hounds and white boxes: Investigating human–AI collaboration protocols in medical diagnosisArtificial Intelligence in Medicine10.1016/j.artmed.2023.102506138(102506)Online publication date: Apr-2023
https://doi.org/10.1016/j.artmed.2023.102506
Sankaran SZhang CAarts HMarkopoulos P(2021)Exploring Peoples' Perception of Autonomy and Reactance in Everyday AI InteractionsFrontiers in Psychology10.3389/fpsyg.2021.71307412Online publication date: 29-Sep-2021
https://doi.org/10.3389/fpsyg.2021.713074
Ueda TOgishi T(2021)Sustainable Platform for Environmental Changes in Smart City2021 6th International Conference on Smart and Sustainable Technologies (SpliTech)10.23919/SpliTech52315.2021.9566381(1-6)Online publication date: 8-Sep-2021
https://doi.org/10.23919/SpliTech52315.2021.9566381
Sankaran SMarkopoulos P(2021)”It’s like a puppet master”: User Perceptions of Personal Autonomy when Interacting with Intelligent TechnologiesProceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450613.3456820(108-118)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450613.3456820

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Recommendations

"An Ideal Human": Expectations of AI Teammates in Human-AI Teaming

"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction

Trust in an AI versus a Human teammate: The effects of teammate identity and performance on Human-AI cooperation