Designing and implementing transparency for real time inspection of autonomous robots

By Andreas Theodorou, Robert H. Wortham, Joanna J. Bryson. 2017

106 citations recorded by the source corpus.

Subfields: Computer Science Applications, Social Psychology, Surgery, Safety, Robustness & Interpretability, Robotics & Control

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Ben Shneiderman
2. Alexandre Heuillet
3. Fabien Couthouis
4. Natalia Díaz Rodríguez
5. Siddhartha Chaudhuri
6. Vladlen Koltun
7. Simon Colton
8. Geraint A. Wiggins

In Social Psychology

1. Ben Shneiderman
2. Alexandre Heuillet
3. Fabien Couthouis
4. Natalia Díaz Rodríguez

In Surgery

1. Alexandre Heuillet
2. Fabien Couthouis
3. Natalia Díaz Rodríguez

In Safety, Robustness & Interpretability

1. Ben Shneiderman
2. Alexandre Heuillet
3. Fabien Couthouis
4. Natalia Díaz Rodríguez

In Robotics & Control

1. Alexandre Heuillet
2. Fabien Couthouis
3. Natalia Díaz Rodríguez

Citations in the corpus

• Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “It is important that the generated explanations consider the profile of the user that will receive those explanations (the so-called audience as per the definition given in Section 2.2 ) in order to adjust the transparency level, as indicated in [45] .”
• Bridging the Gap Between Ethics and Practice (2020, background): “This controversial GDPR requirement is vague and difficult to satisfy in general, but international research efforts at developing Explainable AI (XAI) have blossomed [9, 47, 48, 106, 116, 75, 71].”
• Explainability in Deep Reinforcement Learning (2020, methodology): “Despite explainable deep RL being still an emerging research field, we observed that numerous approaches were developed so far, as detailed in Section 2.”