Past Editions

Past Editions

Third Edition on Designing at Scale with Human-AI Collaboration

We can harness unprecedented amounts of data using AI, creating opportunities to tackle major societal problems in numerous domains, such as health, well-being, and mobility. To make AI useful, we need to find new ways to combine the creative power of humans with the analytical capabilities of computers. While designing solutions and developing systems for social good, a key challenge lies in finding out how to help designers, experts, and societal stakeholders work together with AI to prepare, realize and evaluate design interventions. How can we reduce design complexity for large-scale social interventions?

Check out the recorded talks from the third edition of TAFF here!

Ben Shneiderman
University of Maryland

Chenhao Tan
University of Chicago

Juho Kim
School of Computing at KAIST

Aaron Halfaker
Microsoft Research

Nithya Sambasivan

Trivik Verma
Delft University of Technology

Tim Kraska

Vanessa Murdock

Mounia Lalmas
Head of Tech Research at Personalization at Spotify

Judith Redi
Head of Data Science at Miro

Second Edition on Responsible Use of Data

The adoption of artificial intelligence, data science, data analytics, among other techniques is predominant in many contexts and domains: often used to help us decide which items to buy, what music to listen to, and in high-stakes domains such as education, healthcare provision or criminal justice, among others. The performance of such AI systems depends both on the learning algorithms, as well as the data used for their training and evaluation. The role of the algorithms is well studied. In contrast, research that focuses on the data used in AI systems is not commonplace. Data, however, is always at their core, being a crucial component for advancing and assessing the technological field. We took a multi-disciplinary view and explored further lessons learned from success stories and examples in which the irresponsible use of data can create and foster inequality and inequity, perpetuate bias and prejudice, or produce unlawful or unethical outcomes. We discussed and drew certain guidelines to make the use of data a responsible practice.

Check out the recorded talks from the second edition of TAFF here!

Jahna Otterbacher
Cyprus Center for Algorithmic Transparency (CyCAT) at the OUC

Luke Stark
University of Western Ontario

Lora Aroyo
Google Research

Q. Vera Liao
IBM T.J. Watson Research Center

Elena Simperl
King's College London

Catherine D'Ignazio
MIT, Data + Feminism Lab

Solon Barocas
Cornell University, Microsoft

Alessandro Piscopo
BBC, Datalab

Krishnaram Kenthapadi
Amazon AWS AI

Seda Gürses
Delft University of Technology

The unprecedented rise in the adoption of artificial intelligence techniques in many contexts is concomitant with shortcomings of such technology with respect to robustness, interpretability, usability, and trustworthiness. Crowd computing offers a viable means to engage a large number of human participants in data related tasks and in user studies. In the context of overcoming the computational and interactional challenges facing the current generation of AI systems, recent work has shown how crowd computing can be leveraged to either debug noisy training data in machine learning systems, understand which machine learning models are more congruent to human understanding in particular tasks, or to advance our understanding of how AI systems can influence human behavior.

Check out the recorded talks from the first edition of TAFF here!

Matthew Lease
UT Austin, Amazon

Gianluca Demartini
University of Queensland

Mihaela Vorvoreanu

Shamsi Iqbal

Panos Ipeirotis
New York University

Michael Bernstein
Stanford University

Nithya Sambasivan

Olga Megorskaya

Simo Hosio
University of Oulu

Edith Law
University of Waterloo