# AutoDOViz: Human-Centered Automation for Decision Optimization

arXiv: https://arxiv.org/abs/2302.09688 · HTML: https://mlsanity.com/paper/2302.09688/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 66 references that are in the map.
- First posted on arXiv 19 Feb 2023.

## Builds on (most cited first, 25 of 25)

- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [Auto-Keras: An Efficient Neural Architecture Search System](https://mlsanity.com/paper/1806.10282/) (2018)
- [Visualizing and Understanding Atari Agents](https://mlsanity.com/paper/1711.00138/) (2017)
- [Explainable Reinforcement Learning: A Survey](https://mlsanity.com/paper/2005.06247/) (2020)
- [Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers](https://mlsanity.com/paper/1801.06889/) (2018)
- [Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI](https://mlsanity.com/paper/1909.02309/) (2019)
- [Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science](https://mlsanity.com/paper/1603.06212/) (2016)
- [Graying the black box: Understanding DQNs](https://mlsanity.com/paper/1602.02658/) (2016)
- [A Survey of Visual Analytics Techniques for Machine Learning](https://mlsanity.com/paper/2008.09632/) (2020)
- [One button machine for automating feature engineering in relational databases](https://mlsanity.com/paper/1706.00327/) (2017)
- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization](https://mlsanity.com/paper/2012.00467/) (2020)
- [AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates](https://mlsanity.com/paper/1912.06723/) (2019)
- [ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning](https://mlsanity.com/paper/1902.05009/) (2019)
- [Amazon SageMaker Autopilot: a white box AutoML solution at scale](https://mlsanity.com/paper/2012.08483/) (2020)
- [Why? Why not? When? Visual Explanations of Agent Behavior in Reinforcement Learning](https://mlsanity.com/paper/2104.02818/) (2021)
- [Optimization Problems for Machine Learning: A Survey](https://mlsanity.com/paper/1901.05331/) (2019)
- [AutoAI-TS: AutoAI for Time Series Forecasting](https://mlsanity.com/paper/2102.12347/) (2021)
- [DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning](https://mlsanity.com/paper/1909.02982/) (2019)
- [A Sparse Stress Model](https://mlsanity.com/paper/1608.08909/) (2016)
- [Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents](https://mlsanity.com/paper/2112.09462/) (2021)
- [Conditional Parallel Coordinates](https://mlsanity.com/paper/1906.07716/) (2019)
- [Boolean Decision Rules for Reinforcement Learning Policy Summarisation](https://mlsanity.com/paper/2207.08651/) (2022)

## Built on by (most cited first, 3 of 3)

- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [On the Design and Evaluation of Human-centered Explainable AI Systems: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2510.12201/) (2025)
- [Human-Centered AI Product Prototyping with No-Code AutoML: Conceptual Framework, Potentials and Limitations](https://mlsanity.com/paper/2402.07933/) (2024)

## Abstract (arXiv)

We present AutoDOViz, an interactive user interface for automated decision optimization (AutoDO) using reinforcement learning (RL). Decision optimization (DO) has classically being practiced by dedicated DO researchers where experts need to spend long periods of time fine tuning a solution through trial-and-error. AutoML pipeline search has sought to make it easier for a data scientist to find the best machine learning pipeline by leveraging automation to search and tune the solution. More recently, these advances have been applied to the domain of AutoDO, with a similar goal to find the best reinforcement learning pipeline through algorithm selection and parameter tuning. However, Decision Optimization requires significantly more complex problem specification when compared to an ML problem. AutoDOViz seeks to lower the barrier of entry for data scientists in problem specification for reinforcement learning problems, leverage the benefits of AutoDO algorithms for RL pipeline search and finally, create visualizations and policy insights in order to facilitate the typical interactive nature when communicating problem formulation and solution proposals between DO experts and domain experts. In this paper, we report our findings from semi-structured expert interviews with DO practitioners as well as business consultants, leading to design requirements for human-centered automation for DO with RL. We evaluate a system implementation with data scientists and find that they are significantly more open to engage in DO after using our proposed solution. AutoDOViz further increases trust in RL agent models and makes the automated training and evaluation process more comprehensible. As shown for other automation in ML tasks, we also conclude automation of RL for DO can benefit from user and vice-versa when the interface promotes human-in-the-loop.