CMU CoALA Lab

Co- {Augmentation, Learning, & AI}

The CoALA Lab is an interdisciplinary research group within the Human–Computer Interaction Institute at Carnegie Mellon University. We study how humans and AI systems can augment each other’s abilities (co-augmentation) and learn from each other (co-learning) to support more effective and responsible human-AI collaborations. Through partnerships with practitioners and community stakeholders, we create new technologies to complement and bring out the best of human ability in fundamentally human endeavors such as social, creative, and care-based work.

Research areas

 
8b21c958-71ca-4a7a-b122-ff4e125425d7_rw_1200.png

Designing effective human–AI partnerships

Human and algorithmic decision-making each bring distinct advantages and drawbacks. How might we design systems to combine complementary strengths (and mitigate biases) of human and AI decision-makers?

noun_brainstorming_2056584+%281%29.jpg

Augmenting learning and decision-making

How can we help humans and AI systems augment each other’s abilities (co-augmentation) and learn from each other (co-learning)?

Large JPG-20140228_Trade 151_0046.jpg

Participatory design for AI and machine learning

Data-driven algorithmic systems increasingly influence every facet of our lives, yet most of us lack power to influence their designs. How might we meaningfully engage diverse, non-technical stakeholders in shaping algorithmic behavior?

pauschbridge1_lg.jpg

Fairness and conversation in human–AI systems

Today, critical decisions often result from a blend of human and machine judgment. How can we improve fairness in AI-assisted decision-making, and help humans and AI systems better assess, contest, and inform each other’s decisions?

 

Approach

  • We work with relevant stakeholders in context to co-design and build novel human–AI systems or to reimagine existing ones
  • We develop new co-design methods to support more meaningful stakeholder involvement at every stage of the process
  • We conduct field studies to understand the causal impacts of new technologies in real-world contexts

Values

  • Taking an iterative, de-risking approach to research. At each stage of a project, we identify the quickest research methods (e.g., interviews, observations, lo-fi prototypes, or minimal deployments) to get the insights we need to resolve the largest uncertainties. We regularly reflect and re-plan together.

  • Giving credit where it is due. We credit each other and other researchers/designers appropriately! For instance, this set of principles borrows from Glassman Lab @ Harvard SEAS and from the OH Lab @ CMU. This website’s header image and lab logo were designed by Natalia Oskiera and Mary Beth Kery, respectively!

  • Understanding the broader contexts in which we work, and addressing meaningful problems. Many of the issues we study in this lab (e.g., in human-AI interaction, futures of work, and algorithmic justice & fairness) are tied to deep-rooted systemic, societal problems. At times, our expertise in HCI will be best suited towards making meaningful progress on relatively small pieces of these problems. However, we will frequently reflect on how we might more effectively direct our efforts, through new interdisciplinary collaborations, research-practice and community partnerships, or problem reformulation.

  • Taking care of ourselves and each other. We prioritize getting enough sleep and attending to our mental and physical health, so that we can bring our best selves to our work (see “Lab Counterculture” by Jess Hammer, Alexandra To, & Erica P. Cruz).

  • Making tacit knowledge visible. We believe that each generation of researchers and designers should work to ensure that the next generation faces fewer unnecessary hurdles than they themselves did. With fewer hurdles in their way, more people will have more opportunities to advance our fields. In line with this belief, we value sharing tacit knowledge with newcomers to the lab, and shining a light on “hidden curricula”.

  • Supporting students’ goals, whatever those may be. We recognize that students at all levels may have a wide range of career goals. We support each other’s goals and help each other reach them!

  • Reflecting on our personal and community practices. This set of values will evolve over time. Every lab member can contribute to shaping the kind of lab they want to be a part of, and we will work together to make it happen!

People

If you are interested in collaborating or joining the group, please reach out!

Current CoALAs

Ken Holstein
Lab Director

Wesley Deng PhD Researcher in HCI + ML + Art + Human-AI Interactionco-advisor: Motahhare Eslami

Wesley Deng
PhD Researcher in HCI + ML + Art + Human-AI Interaction

co-advisor: Motahhare Eslami

Alicia DeVos PhD Researcher in HCI + Human-AI Interactionco-advisor: Motahhare Eslami

Alicia DeVos
PhD Researcher in HCI + Human-AI Interaction

co-advisor: Motahhare Eslami

Frederic Gmeiner PhD Researcher in HCI + Design + Human-AI Interactionco-advisor: Nik Martelaro (Augmenting Designer Capabilities Lab)

Frederic Gmeiner
PhD Researcher in HCI + Design + Human-AI Interaction

co-advisor: Nik Martelaro

Anna Kawakami PhD Researcher in HCI + Human-AI Interactionco-advisor: Haiyi Zhu (Social AI Group)

Anna Kawakami
PhD Researcher in HCI + Human-AI Interaction

co-advisor: Haiyi Zhu

 
Tzu-Sheng Kuo PhD Researcher in HCI + Human-AI Interactionco-advisor: Haiyi Zhu (Social AI Group)

Tzu-Sheng Kuo
PhD Researcher in HCI + Human-AI Interaction

co-advisor: Haiyi Zhu

Charvi Rastogi PhD Researcher in ML + Human-AI Interactionco-advisor: Nihar Shah

Charvi Rastogi
PhD Researcher in ML + Human-AI Interaction

co-advisor: Nihar Shah

Monica Chang Design + HCI Researcherco-mentor:  Michael Madaio

Monica Chang
Design + HCI Researcher

co-mentor: Michael Madaio

Yang Cheng HCI + Cognitive Science + Design Researcherco-mentor: Maria De-Arteaga

Yang Cheng
HCI + Cognitive Science + Design Researcher

co-mentors: Anna Kawakami & Haiyi Zhu

Harnoor Dhingra
AI + HCI Researcher

co-mentors: Wesley Deng & Motahhare Eslami

 
Bill Guo HCI + CS + Arch. Researcherco-mentors: Wesley Deng, Alicia DeVos, Motahhare Eslami

Bill Guo
HCI + CS + Arch. Researcher

co-mentors: Wesley Deng, Alicia DeVos, Motahhare Eslami, Jason Hong, & Hong Shen

Andrew Sim
Design + HCI Researcher

co-mentors: Wesley Deng & Motahhare Eslami

Sophia Timko
Design + HCI Researcher

co-mentors: Frederic Gmeiner, Nik Martelaro, & Lining Yao

Lakshmi Tumati Statistics + ML + HCI Researcherco-mentor: Maria De-Arteaga

Lakshmi Tumati
Statistics + ML + HCI Researcher

co-mentor: Maria De-Arteaga

Linda Xue
HCI + Design Researcher

co-mentors: Frederic Gmeiner, Nik Martelaro, & Lining Yao

 

Jieyu Zhou
Computational Design + HCI Researcher

co-mentors: Wesley Deng & Motahhare Eslami

Diana Qing Visiting HCI + Human-AI Interaction Researcherco-mentors: Logan Stapleton, Haiyi Zhu, & Alexandra Chouldechova

Diana Qing
Visiting HCI + Human-AI Interaction Researcher

co-mentors: Anna Kawakami, Logan Stapleton, Haiyi Zhu, & Alexandra Chouldechova

Nick Su Visiting Informatics + HCI Researcher

Nick Su
Visiting Informatics + HCI Researcher

co-mentor: Motahhare Eslami

 
 
 

Alumni

Undergraduate & Postbacc

Erica Chiang
Alison Hu
Meijie Hu
Karen Kim
Ankita Kundu
Matthew Ok
Lauren Park
Freesoul ‘Sol’ El Shabazz-Thompson
Donghoon Shin
Meghna Sudhakar
Elena Swecker
Mera Tegene
Candace Williams
Zac Yu

Masters

Yu-Jan Chang
Connie Chau
David Contreras
Aditi Dhabalia
Anushri Gupta
Gena Hong
Janice Lyu
Ahana Mukhopadhyay
Yunmin Oh
Will Rutter
Harkiran Kaur Saluja
Anita Sun
Yuchen Yao

PhD & Postdoctoral

Vanessa Echeverria
LuEttaMae Lawrence

Highlighted work

Holstein, K., Wortman Vaughan, J., Daumé III, H., Dudik, M., & Wallach, H. (2019). Improving fairness in machine learning systems: What do industry practitioners need? In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI’19) (pp. 1-16). ACM. [ACM]  [supp. materials]   [pdf]

Shen, H.*, DeVos, A.* , Eslami, M.**, & Holstein, K.** (2021). Everyday algorithm auditing: Understanding the power of regular users in surfacing harmful algorithmic behaviors. In Proceedings of the ACM on Human-Computer Interaction, 3(CSCW). [ACM]  [pdf]


Holstein, K., McLaren, B. M., & Aleven, V. (2018). Student learning benefits of a mixed-reality teacher awareness tool in AI-enhanced classrooms. In International Conference on Artificial intelligence in Education (AIED’18) (pp. 154-168). Springer, Cham. [pdf]

Holstein, K.*, Harpstead, E.*, Gulotta, R., & Forlizzi, J. (2020). Replay Enactments: Exploring possible futures through historical data. In Proceedings of the 2020 ACM Designing Interactive Systems Conference (DIS’20) (pp. 1607-1618). ACM. [ACM] [pdf]

Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher–AI complementarity. Journal of Learning Analytics (JLA), 6(2), 27-52. SoLAR. Presented at the Workshop on Participatory Approaches to Machine Learning @ ICML’20. [pdf]

Interested in working with us?

 

Funky hat party with members of the CoALA Lab and Social AI Group at CMU HCII!
(Fall 2021)

 
 

If you are interested in collaborating or joining the group, please get in touch!

Name