AI assistance in decision-making has become popular, yet people's inappropriate reliance on AI often leads to unsatisfactory human-AI collaboration performance. In this paper, through three pre-registered, randomized human subject experiments, we explore whether and how the provision of {second opinions} may affect decision-makers' behavior and performance in AI-assisted decision-making. We find that if both the AI model's decision recommendation and a second opinion are always presented together, decision-makers reduce their over-reliance on AI while increase their under-reliance on AI, regardless whether the second opinion is generated by a peer or another AI model. However, if decision-makers have the control to decide when to solicit a peer's second opinion, we find that their active solicitations of second opinions have the potential to mitigate over-reliance on AI without inducing increased under-reliance in some cases. We conclude by discussing the implications of our findings for promoting effective human-AI collaborations in decision-making.

Automated Machine Learning (AutoML) is a rapidly growing set of technologies that automate the model development pipeline by searching model space and generating candidate models. A critical, final step of AutoML is human selection of a final model from dozens of candidates. In current AutoML systems, selection is supported only by performance metrics. Prior work has shown that in practice, people evaluate ML models based on additional criteria, such as the way a model makes predictions. Comparison may happen at multiple levels, from types of errors, to feature importance, to how the model makes predictions of specific instances. We developed \tool{} to support interactive model comparison for AutoML by integrating multiple Explainable AI (XAI) and visualization techniques. We conducted a user study in which we both evaluated the system and used it as a technology probe to understand how users perform model comparison in an AutoML system. We discuss design implications for utilizing XAI techniques for model comparison and supporting the unique needs of data scientists in comparing AutoML models.

Artificial Intelligence (AI) can now automate the algorithm selection, feature engineering, and hyperparameter tuning steps in a machine learning workflow. Commonly known as AutoML or AutoAI, these technologies aim to relieve data scientists from the tedious manual work. However, today's AutoAI systems often present only limited to no information about the process of how they select and generate model results. Thus, users often do not understand the process, neither they trust the outputs. In this short paper, we build an experimental system AutoAIViz that aims to visualize AutoAI's model generation process to increase users' level of understanding and trust in AutoAI systems. Through a user study with 10 professional data scientists, we find that the proposed system helps participants to complete the data science tasks, and increases their perceptions of understanding and trust in the AutoAI system.

Dakuo Wang is a Research Scientist at IBM Research AI, Cambridge, Massachusetts. His research lies in the intersection between human-computer interaction (HCI) and artificial intelligence (AI). He is now leading a team of researchers, engineers, and designers to conduct research and design user experience for IBM AutoAI, a solution to automate the end-to-end machine learning pipeline. From studying how users work with various AI systems such as automated machine learning (AutoML/AutoAI), chatbots, and clinical decision support systems (CDSS), he proposes "Human-AI Collaboration" as a new framework to examine and design AI systems to work together with humans. Before joining IBM Research, Dakuo Wang got his Ph.D. and M.S. in Information and Computer Science from the University of California Irvine, a Diplôme d'Ingénieur (M.S.) in Information System from École Centrale d'Électronique Paris, and a B.S. in Computer Science from Beijing University of Technology.

In recent years, data-driven decision making has become critical to the success of corporations. There are many benefits of using technology for data-driven practices including the optimization of production and manufacturing, reductions in customer attrition, reductions in data redundancy, increased profitability, and the creation of competitive advantage. So data science has become popular as organizations embrace data-driven decision-making approaches. Data scientists need a wide range of skills including mathematics and statistics, machine learning and artificial intelligence (AI), databases and cloud computing, and data visualization. However, it is difficult to recruit enough data scientists, particularly with sufficient domain knowledge, such as banking, healthcare, human resources, manufacturing, and telco, for the tasks to be performed and decisions to be made.

The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One application domain is data science. New techniques in automating the creation of AI, known as AutoAI or AutoML, aim to automate the work practices of data scientists. AutoAI systems are capable of autonomously ingesting and pre-processing data, engineering new features, and creating and scoring models based on a target objectives (e.g. accuracy or run-time efficiency). Though not yet widely adopted, we are interested in understanding how AutoAI will impact the practice of data science. We conducted interviews with 20 data scientists who work at a large, multinational technology company and practice data science in various business settings. Our goal is to understand their current work practices and how these practices might change with AutoAI. Reactions were mixed: while informants expressed concerns about the trend of automating their jobs, they also strongly felt it was inevitable. Despite these concerns, they remained optimistic about their future job security due to a view that the future of data science work will be a collaboration between humans and AI systems, in which both automation and human expertise are indispensable.