887 citations · 1.3k across the 10 of their papers we have counts for
10 papers
Users Mispredict Their Own Preferences for AI Writing Assistance
Vivian Lai, Zana Buçinca, Nil-Jana Akpinar +5
Proactive AI writing assistants need to predict when users want drafting help, yet we lack empirical understanding of what drives preferences. Through a factorial vignette study wi…
Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills
Zana Buçinca, Siddharth Swaroop, Amanda E. Paluch +2
People's decision-making abilities often fail to improve or may even erode when they rely on AI for decision-support, even when the AI provides informative explanations. We argue t…
Learning Interpretable Fair Representations
Tianhao Wang, Zana Buçinca, Zilin Ma
Numerous approaches have been recently proposed for learning fair representations that mitigate unfair outcomes in prediction tasks. A key motivation for these methods is that the…
Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning
Zana Buçinca, Siddharth Swaroop, Amanda E. Paluch +2
Imagine if AI decision-support tools not only complemented our ability to make accurate decisions, but also improved our skills, boosted collaboration, and elevated the joy we deri…
AHA!: Facilitating AI Impact Assessment by Generating Examples of Harms
Zana Buçinca, Chau Minh Pham, Maurice Jakesch +3
While demands for change and accountability for harmful AI consequences mount, foreseeing the downstream effects of deploying AI systems remains a challenging task. We developed AH…
Accuracy-Time Tradeoffs in AI-Assisted Decision Making under Time Pressure
Siddharth Swaroop, Zana Buçinca, Krzysztof Z. Gajos +1
In settings where users both need high accuracy and are time-pressured, such as doctors working in emergency rooms, we want to provide AI assistance that both increases decision ac…
How Different Groups Prioritize Ethical Values for Responsible AI
Maurice Jakesch, Zana Buçinca, Saleema Amershi +1
Private companies, public sector organizations, and academic groups have outlined ethical values they consider important for responsible artificial intelligence technologies. While…
To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making
Zana Buçinca, Maja Barbara Malaya, Krzysztof Z. Gajos
People supported by AI-powered decision support tools frequently overrely on the AI: they accept an AI's suggestion even when that suggestion is wrong. Adding explanations to the A…
AffectON: Incorporating Affect Into Dialog Generation
Zana Bucinca, Yucel Yemez, Engin Erzin +1
Due to its expressivity, natural language is paramount for explicit and implicit affective state communication among humans. The same linguistic inquiry (e.g., How are you?) might…
Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems
Zana Buçinca, Phoebe Lin, Krzysztof Z. Gajos +1
Explainable artificially intelligent (XAI) systems form part of sociotechnical systems, e.g., human+AI teams tasked with making decisions. Yet, current XAI systems are rarely evalu…