Integrating rich user feedback into intelligent user interfaces

The potential for machine learning systems to improve via a mutually beneficial exchange of information with users has yet to be explored in much detail. Previously, we found that users were willing to provide a generous amount of rich feedback to machine learning systems, and that the types of some of this rich feedback seem promising for assimilation by machine learning algorithms. Following up on those findings, we ran an experiment to assess the viability of incorporating real-time keyword-based feedback in initial training phases when data is limited. We found that rich feedback improved accuracy but an initial unstable period often caused large fluctuations in classifier behavior. Participants were able to give feedback by relying heavily on system communication in order to respond to changes. The results show that in order to benefit from the user’s knowledge, machine learning systems must be able to absorb keyword-based rich feedback in a graceful manner and provide clear explanations of their predictions

Interacting meaningfully with machine learning systems: Three experiments

Although machine learning is becoming commonly used in today's software, there has been little research into how end users might interact with machine learning systems, beyond communicating simple “right/wrong” judgments. If the users themselves could work hand-in-hand with machine learning systems, the users’ understanding and trust of the system could improve and the accuracy of learning systems could be improved as well. We conducted three experiments to understand the potential for rich interactions between users and machine learning systems. The first experiment was a think-aloud study that investigated users’ willingness to interact with machine learning reasoning, and what kinds of feedback users might give to machine learning systems. We then investigated the viability of introducing such feedback into machine learning systems, specifically, how to incorporate some of these types of user feedback into machine learning systems, and what their impact was on the accuracy of the system. Taken together, the results of our experiments show that supporting rich interactions between users and machine learning systems is feasible for both user and machine. This shows the potential of rich human–computer collaboration via on-the-spot interactions as a promising direction for machine learning systems and users to collaboratively share intelligence

Integrating rich user feedback into intelligent user interfaces

The potential for machine learning systems to improve via a mutually beneficial exchange of information with users has yet to be explored in much detail. Previously, we found that users were willing to provide a generous amount of rich feedback to machine learning systems, and that the types of some of this rich feedback seem promising for assimilation by machine learning algorithms. Following up on those findings, we ran an experiment to assess the viability of incorporating real-time keyword-based feedback in initial training phases when data is limited. We found that rich feedback improved accuracy but an initial unstable period often caused large fluctuations in classifier behavior. Participants were able to give feedback by relying heavily on system communication in order to respond to changes. The results show that in order to benefit from the user's knowledge, machine learning systems must be able to absorb keyword-based rich feedback in a graceful manner and provide clear explanations of their predictions.Keywords: Human information processing, User Interfaces: theory and methods, User feedback, Machine learning, User/Machine Systems, Information interfaces and presentation, Human factor

Toward harnessing user feedback for machine learning

There has been little research into how end users might be able to communicate advice to machine learning systems. If this resource--the users themselves--could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved and the users' understanding and trust of the system could improve as well. We conducted a think-aloud study to see how willing users were to provide feedback and to understand what kinds of feedback users could give. Users were shown explanations of machine learning predictions and asked to provide feedback to improve the predictions. We found that users had no difficulty providing generous amounts of feedback. The kinds of feedback ranged from suggestions for reweighting of features to proposals for new features, feature combinations, relational features, and wholesale changes to the learning algorithm. The results show that user feedback has the potential to significantly improve machine learning systems, but that learning algorithms need to be extended in several ways to be able to assimilate this feedback.Author Keywords: Machine learning, explanations, user feedback for learnin

End-user feature engineering in the presence of class imbalance

Intelligent user interfaces, such as recommender systems and email classifiers, use machine learning algorithms to customize their behavior to the preferences of an end user. Although these learning systems are somewhat reliable, they are not perfectly accurate. Traditionally, end users who need to correct these learning systems can only provide more labeled training data. In this paper, we focus on incorporating new features suggested by the end user into machine learning systems. To investigate the effects of user-generated features on accuracy we developed an auto- coding application that enables end users to assist a machine-learned program in coding a transcript by adding custom features. Our results show that adding user-generated features to the machine learning algorithm can result in modest improvements to its F1 score. Further improvements are possible if the algorithm accounts for class imbalance in the training data and deals with low-quality user-generated features that add noise to the learning algorithm. We show that addressing class imbalance improves performance to an extent but improving the quality of features brings about the most beneficial change. Finally, we discuss changes to the user interface that can help end users avoid the creation of low-quality features.Keywords: Feature Engineering, Class Imbalance, machine learning, artificial intelligence, end-user programming, HC

Interacting meaningfully with machine learning systems : three experiments

Although machine learning is becoming commonly used in today's software, there has been little research into how end users might interact with machine learning systems, beyond communicating simple "right/wrong" judgments. If the users themselves could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved and the users' understanding and trust of the system could improve as well. We conducted three experiments to begin to understand the potential for rich interactions between users and machine learning systems. The first experiment was a think-aloud study, aiming to see how willing users were to interact with and about machine learning reasoning, and to help us understand what kinds of feedback users might give to machine learning systems. Specifically, users were shown explanations of machine learning predictions and asked to provide feedback to improve the predictions. The results were that users' feedback was rich, complex, and widely varied, ranging from suggestions for reweighting of features to proposals for new features, feature combinations, relational features, and wholesale changes to the learning algorithm. We then investigated the viability of introducing such feedback into machine learning systems: specifically, how to incorporate some of these types of user feedback into machine learning systems, and impact on the accuracy of the system. Taken together, the results of our experiments show that supporting rich interactions between users and machine learning systems is feasible for both user and machine. This shows the potential of rich human-computer collaboration via on-the-spot interactions as a promising direction for machine learning systems to work more intelligently, hand-in-hand with the user

Learning Hierarchical Compositional Task Definitions through Online Situated Interactive Language Instruction

Artificial agents, from robots to personal assistants, have become competent workers in many settings and embodiments, but for the most part, they are limited to performing the capabilities and tasks with which they were initially programmed. Learning in these settings has predominately focused on learning to improve the agent’s performance on a task, and not on learning the actual definition of a task. The primary method for imbuing an agent with the task definition has been through programming by humans, who have detailed knowledge of the task, domain, and agent architecture. In contrast, humans quickly learn new tasks from scratch, often from instruction by another human. If we desire AI agents to be flexible and dynamically extendable, they will need to emulate these learning capabilities, and not be stuck with the limitation that task definitions must be acquired through programming. This dissertation explores the problem of how an Interactive Task Learning agent can learn the complete definition or formulation of novel tasks rapidly through online natural language instruction from a human instructor. Recent advances in natural language processing, memory systems, computer vision, spatial reasoning, robotics, and cognitive architectures make the time ripe to study how knowledge can be automatically acquired, represented, transferred, and operationalized. We present a learning approach embodied in an ITL agent that interactively learns the meaning of task concepts, the goals, actions, failure conditions, and task-specific terms, for 60 games and puzzles. In our approach, the agent learns hierarchical symbolic representations of task knowledge that enable it to transfer and compose knowledge, analyze and debug multiple interpretations, and communicate with the teacher to resolve ambiguity. Our results show that the agent can correctly generalize, disambiguate, and transfer concepts across variations of language descriptions and world representations, even with distractors present.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153434/1/jrkirk_1.pd