As the use of machine learning (ML) models in product development and data-driven decision-making processes became pervasive in many domains, people's focus on building a well-performing model has increasingly shifted to understanding how their model works. While scholarly interest in model interpretability has grown rapidly in research communities like HCI, ML, and beyond, little is known about how practitioners perceive and aim to provide interpretability in the context of their existing workflows. This lack of understanding of interpretability as practiced may prevent interpretability research from addressing important needs, or lead to unrealistic solutions. To bridge this gap, we conducted 22 semi-structured interviews with industry practitioners to understand how they conceive of and design for interpretability while they plan, build, and use their models. Based on a qualitative analysis of our results, we differentiate interpretability roles, processes, goals and strategies as they exist within organizations making heavy use of ML models. The characterization of interpretability work that emerges from our analysis suggests that model interpretability frequently involves cooperation and mental model comparison between people in different roles, often aimed at building trust not only between people and models but also between people within the organization. We present implications for design that discuss gaps between the interpretability challenges that practitioners face in their practice and approaches proposed in the literature, highlighting possible research directions that can better address real-world needs.

No abstract

When crowdsourcing the creation of machine learning datasets, statistical distributions that capture diverse answers can represent ambiguous data better than a single best answer. Unfortunately, collecting distributions is expensive because a large number of responses need to be collected to form a stable distribution. Despite this, the efficient collection of answer distributions-that is, ways to use less human effort to collect estimates of the eventual distribution that would be formed by a large group of responses-is an under-studied topic. In this paper, we demonstrate that this type of estimation is possible and characterize different elicitation approaches to guide the development of future systems. We investigate eight elicitation approaches along two dimensions: annotation granularity and estimation perspective. Annotation granularity is varied by annotating i) a single "best" label, ii) all relevant labels, iii) a ranking of all relevant labels, or iv) real-valued weights for all relevant labels. Estimation perspective is varied by prompting workers to either respond with their own answer or an estimate of the answer(s) that they expect other workers would provide. Our study collected ordinal annotations on the emotional valence of facial images from 1,960 crowd workers and found that, surprisingly, the most fine-grained elicitation methods were not the most accurate, despite workers spending more time to provide answers. Instead, the most efficient approach was to ask workers to choose all relevant classes that others would have selected. This resulted in a 21.4% reduction in the human time required to reach the same performance as the baseline (i.e., selecting a single answer with their own perspective). By analyzing cases in which finer-grained annotations degraded performance, we contribute to a better understanding of the trade-offs between answer elicitation approaches. Our work makes it more tractable to use answer distributions in large-scale tasks such as ML training, and aims to spark future work on techniques that can efficiently estimate answer distributions.

Microgrids can be considered as controllable units from the utility point of view because the entities of microgrids such as distributed energy resources and controllable loads can effectively control the amount of power consumption or generation. Therefore, microgrids can make various contracts with utility companies such as demand response program or ancillary services. Another advantage of microgrids is to integrate renewable energy resources to low-voltage distribution networks. Battery energy storage systems (BESSs) can effectively compensate the intermittent output of renewable energy resources. This paper presents intelligent control schemes for BESSs and autonomous energy management schemes of microgrids based on the concept of multi-agent systems. The proposed control scheme consists of two layers of decision-making procedures. In the bottom layer, intelligent agents decide the optimal operation strategies of individual microgrid entities such as BESSs, backup generators and loads. In the upper layer, the central microgrid coordinator (MGCC) coordinates multiple agents so that the overall microgrid can match the load reduction requested by the grid operator. The proposed control scheme is applied to Korea Power Exchange's Intelligent Demand Response Program.

No abstract