Open-source library provides explanation for machine learning through diverse counterfactuals
This is a development of #interpretable ML. Library to explore “what-if” scenarios for ML models.
Blog post: https://www.microsoft.com/en-us/research/blog/open-source-library-provides-explanation-for-machine-learning-through-diverse-counterfactuals/
Paper: https://www.microsoft.com/en-us/research/publication/explaining-machine-learning-classifiers-through-diverse-counterfactual-examples/
Github: https://github.com/microsoft/dice
#Microsoft #ML #opensource
This is a development of #interpretable ML. Library to explore “what-if” scenarios for ML models.
Blog post: https://www.microsoft.com/en-us/research/blog/open-source-library-provides-explanation-for-machine-learning-through-diverse-counterfactuals/
Paper: https://www.microsoft.com/en-us/research/publication/explaining-machine-learning-classifiers-through-diverse-counterfactual-examples/
Github: https://github.com/microsoft/dice
#Microsoft #ML #opensource
Microsoft Research
DiCE: Employing counterfactuals to explain machine learning algorithms
Microsoft researchers & collaborators created an open-source library to explore “what-if” scenarios for machine learning models. Learn how their method generates multiple diverse counterfactuals at once & gives insight into ML algorithm decision making.
Reliable ML track at Data Fest Online 2023
Call for Papers
Friends, we are glad to inform you that the largest Russian-language conference on Data Science - Data Fest - from the Open Data Science community will take place in 2023 (at the end of May).
And it will again have a section from Reliable ML community. We are waiting for your applications for reports: write directly to me or Dmitry.
Track Info
The concept of Reliable ML is about what to do so that the result of the work of data teams would be, firstly, applicable in the business processes of the customer company and, secondly, brought benefits to this company.
For this you need to be able to:
- correctly build a portfolio of projects (#business)
- think over the system design of each project (#ml_system_design)
- overcome various difficulties when developing a prototype (#tech #causal_inference #metrics)
- explain to the business that your MVP deserves a pilot (#interpretable_ml)
- conduct a pilot (#causal_inference #ab_testing)
- implement your solution in business processes (#tech #mlops #business)
- set up solution monitoring in the productive environment (#tech #mlops)
If you have something to say on the topics above, write to us! If in doubt, write anyway. Many of the coolest reports of previous Reliable ML tracks have come about as a result of discussion and collaboration on the topic.
If you are not ready to make a report but want to listen to something interesting, you can still help! Repost to a relevant community / forward to a friend = participate in the creation of good content.
Registration and full information about Data Fest 2023 is here.
@Reliable ML
Call for Papers
Friends, we are glad to inform you that the largest Russian-language conference on Data Science - Data Fest - from the Open Data Science community will take place in 2023 (at the end of May).
And it will again have a section from Reliable ML community. We are waiting for your applications for reports: write directly to me or Dmitry.
Track Info
The concept of Reliable ML is about what to do so that the result of the work of data teams would be, firstly, applicable in the business processes of the customer company and, secondly, brought benefits to this company.
For this you need to be able to:
- correctly build a portfolio of projects (#business)
- think over the system design of each project (#ml_system_design)
- overcome various difficulties when developing a prototype (#tech #causal_inference #metrics)
- explain to the business that your MVP deserves a pilot (#interpretable_ml)
- conduct a pilot (#causal_inference #ab_testing)
- implement your solution in business processes (#tech #mlops #business)
- set up solution monitoring in the productive environment (#tech #mlops)
If you have something to say on the topics above, write to us! If in doubt, write anyway. Many of the coolest reports of previous Reliable ML tracks have come about as a result of discussion and collaboration on the topic.
If you are not ready to make a report but want to listen to something interesting, you can still help! Repost to a relevant community / forward to a friend = participate in the creation of good content.
Registration and full information about Data Fest 2023 is here.
@Reliable ML