Corpus ID: 195801036

Interactive Machine Learning Heuristics

@inproceedings{Corbett2018InteractiveML,
  title={Interactive Machine Learning Heuristics},
  author={E. Corbett and Nathaniel Saul and Meg Pirrung},
  year={2018}
}
End-user interaction with machine learning based systems will result in new usability challenges for the field of human computer interaction. Machine learning algorithms are often complicated to the point of being literal black boxes, presenting a unique challenge in the context of interaction with and understanding by end-users. In order to address these challenges, the most relied upon usability inspection method, the heuristic evaluation, must be adapted for the unique end-user experiences… Expand
Wizard of Oz Prototyping for Machine Learning Experiences
  • J. Browne
  • Computer Science
  • CHI Extended Abstracts
  • 2019
TLDR
Wizard of Oz prototyping is suggested to help designers incorporate human-centered design processes into the development of machine learning experiences. Expand
Machine Learning from User Interaction for Visualization and Analytics: A Workshop-Generated Research Agenda
At IEEE VIS 2018, we organized the Machine Learning from User Interaction for Visualization and Analytics workshop. The goal of this workshop was to bring together researchers from across theExpand
Design Heuristics for Artificial Intelligence: Inspirational Design Stimuli for Supporting UX Designers in Generating AI-Powered Ideas
TLDR
Case studies suggest that AI design heuristics can be used as design stimuli in the early conceptual design phase to support practitioners in exploring a larger design space for the generation of AI-powered ideas. Expand

References

SHOWING 1-10 OF 35 REFERENCES
A Review of User Interface Design for Interactive Machine Learning
TLDR
A structural and behavioural model of a generalised IML system is proposed and a solution principles for building effective interfaces for IML are identified, identified strands of user interface research key to unlocking more efficient and productive non-expert interactive machine learning applications. Expand
Constructivist Design for Interactive Machine Learning
TLDR
It is argued that the objectives of interactive machine learning can be interpreted as constructivist, and it is shown how constructivist learning environments pose critical questions for the design of interactiveMachine learning systems. Expand
Evaluation of Interactive Machine Learning Systems
TLDR
This chapter argues for coupling two types of validation: algorithm-centered analysis, to study the computational behaviour of the system; and human-centered evaluation, to observe the utility and effectiveness of the application for end-users. Expand
Power to the People: The Role of Humans in Interactive Machine Learning
TLDR
It is argued that the design process for interactive machine learning systems should involve users at all stages: explorations that reveal human interaction patterns and inspire novel interaction methods, as well as refinement stages to tune details of the interface and choose among alternatives. Expand
Toward harnessing user feedback for machine learning
TLDR
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. Expand
Interacting meaningfully with machine learning systems: Three experiments
TLDR
Supporting rich interactions between users and machine learning systems is feasible for both user and machine, and shows the potential of rich human-computer collaboration via on-the-spot interactions as a promising direction for machineLearning systems and users to collaboratively share intelligence. Expand
Interacting with an inferred world: the challenge of machine learning for humane computer interaction
  • A. Blackwell
  • Computer Science
  • Aarhus Conference on Critical Alternatives
  • 2015
TLDR
The way in which this new generation of technology raises fresh challenges for the critical evaluation of interactive systems is explored and some proposed measures for the design of inference-based systems that are more open to humane design and use are proposed. Expand
Human-Centred Machine Learning
TLDR
A human-centered understanding of machine learning in human context can lead not only to more usable machine learning tools, but to new ways of framing learning computationally. Expand
Investigating How Experienced UX Designers Effectively Work with Machine Learning
TLDR
Designers appeared to be the most successful when they engaged in ongoing collaboration with data scientists to help envision what to make and when they embraced a data-centric culture. Expand
Heuristic evaluation of user interfaces
TLDR
Four experiments showed that individual evaluators were mostly quite bad at doing heuristic evaluations and that they only found between 20 and 51% of the usability problems in the interfaces they evaluated. Expand
...
1
2
3
4
...