Value-Aligned Human-AI Interaction

The anticipated large-scale deployment of AI systems in knowledge work will impact not only productivity and work quality but also workers’ values and workplace dynamics. I argue that how we design and deploy AI-infused technologies will shape people’s skills and competence, their sense of agency, collaboration with others, and even the meaning they derive from their work. I design human-AI interaction techniques that complement people and amplify their values in AI-assisted work. My research focuses on (1) understanding how people make AI-assisted decisions and (2) designing novel interaction paradigms, explanations, and systems that optimize human-centric outcomes (eg, human skills) and output-centric outcomes (eg, decision accuracy) in AI-assisted tasks. In this talk, I will present a suite of interaction techniques I have introduced to optimize AI-assisted decision-making. These include cognitive forcing interventions that reduce overreliance on AI, adaptive AI support that enables human-AI complementarity in decision accuracy, and contrastive explanations that improve both decision accuracy and users’ task-related skills.

Zana Buçinca is a PhD candidate in Computer Science at Harvard working at the intersection of human-AI interaction and responsible AI. Her research integrates cognitive and social science theories to design novel human-AI interaction techniques that complement workers and amplify their values in AI-assisted tasks. Her work has been recognized with the IBM PhD Fellowship, a Siebel Scholarship, and a Best Paper Award at IUI 2020. Zana has been named a Rising Star in AI by the University of Michigan, a Rising Star in Management Science & Engineering by Stanford, and one of the Top 10 Most Inspiring Women in STEM by UNDP Kosovo.