CMU CoALA Lab
Co- {Augmentation, Learning, & AI}
Research areas
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?
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)?
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?
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 Interaction
co-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 Interaction
co-advisor: Nik Martelaro
Anna Kawakami
PhD Researcher in HCI + Human-AI Interaction
co-advisor: Haiyi Zhu
Tzu-Sheng Kuo
PhD Researcher in HCI + Human-AI Interaction
co-advisor: Haiyi Zhu
Charvi Rastogi
PhD Researcher in ML + Human-AI Interaction
co-advisor: Nihar Shah
Monica Chang
Design + HCI Researcher
co-mentor: Michael Madaio
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. 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 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 Researcher
co-mentors: Anna Kawakami, Logan Stapleton, Haiyi Zhu, & Alexandra Chouldechova
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]
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., 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?
If you are interested in collaborating or joining the group, please get in touch!