DeepAI AI Chat
Log In Sign Up

Learning by Design: Structuring and Documenting the Human Choices in Machine Learning Development

by   Simon Enni, et al.

The influence of machine learning (ML) is quickly spreading, and a number of recent technological innovations have applied ML as a central technology. However, ML development still requires a substantial amount of human expertise to be successful. The deliberation and expert judgment applied during ML development cannot be revisited or scrutinized if not properly documented, and this hinders the further adoption of ML technologies–especially in safety critical situations. In this paper, we present a method consisting of eight design questions, that outline the deliberation and normative choices going into creating a ML model. Our method affords several benefits, such as supporting critical assessment through methodological transparency, aiding in model debugging, and anchoring model explanations by committing to a pre hoc expectation of the model's behavior. We believe that our method can help ML practitioners structure and justify their choices and assumptions when developing ML models, and that it can help bridge a gap between those inside and outside the ML field in understanding how and why ML models are designed and developed the way they are.


page 1

page 2

page 3

page 4


Toward Certification of Machine-Learning Systems for Low Criticality Airborne Applications

The exceptional progress in the field of machine learning (ML) in recent...

Is the Rush to Machine Learning Jeopardizing Safety? Results of a Survey

Machine learning (ML) is finding its way into safety-critical systems (S...

Symphony: Composing Interactive Interfaces for Machine Learning

Interfaces for machine learning (ML), information and visualizations abo...

Tiny, always-on and fragile: Bias propagation through design choices in on-device machine learning workflows

Billions of distributed, heterogeneous and resource constrained smart co...

Model Stability with Continuous Data Updates

In this paper, we study the "stability" of machine learning (ML) models ...

Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry

Design research is important for understanding and interrogating how eme...