12-01, 08:00–08:30 (UTC), Talk Track I
Counterfactual explanations (CFE) are methods that
explain a machine learning model by giving an alternate class prediction
of a data point with some minimal changes in its features.
In this talk, we describe a counterfactual (CF)
generation method based on particle swarm optimization (PSO) and how we can have greater control over the proximity and sparsity properties
over the generated CFs.
Counterfactual explanations (CFE) are methods that
explain a machine learning model by giving an alternate class prediction
of a data point with some minimal changes in its features.
It helps the users to identify their data attributes that caused
an undesirable prediction like a loan or credit card rejection.
We describe an efficient, and an actionable counterfactual (CF)
generation method based on particle swarm optimization (PSO).
We describe a simple objective function for the optimization of
instance-centric CF generation problem. The PSO brings in a lot
of flexibility in terms of carrying out multi-objective optimization
in large dimensions, capability for multiple CF generation and
setting box constraints or immutability of data attributes therby
enables greater control over the proximity and sparsity properties
over the generated CFs.
Keywords
- machine learning, counterfactual explanation, pso , explainability
Outline
- Introduction to Counterfactual Analysis (CFA) and challenges
- Introduction to Particle Swarm Optimization (PSO)
- Describe best practices for generating Counterfactuals and the proposed method
- Demo using Jupyter notebook
- Questions and answers
Timeline
This talk done will be done in 3 parts -
1. Introduce the Background (0:00-0:05 )
- Introduce CFA and PSO
- Current challenges
2. Describe the method proposed and best practices (0:05 - 0:20)
- Best practices
- Theoretical explanation of the proposed method
3. Demo on Jupyter notebook (0:20 - 0:30)
Key Takeaways
- Understanding how counterfactual analysis can be done using PSO.
- How to leverage multi-objective optimization to control proximity, validity, sparsity and diversity of counterfactuals generated.
- Application in real-world usecase.
Previous knowledge expected
Niranjan G S is a Manager at AI Labs Subex . His work at Subex revolves around the areas of Fraud detection in the telecommunication domain and democratizing AI by helping build HyperSense - No code platform for creating and deploying ML workflows. He was previously a Data scientist at Amazon where he built AI solutions for detecting fraud in ecommerce domain.
Shashank is Data Sciences leader with diverse experience across verticals including Telecom, CPG, Retail, Hitech and E-commerce domains. He is currently heading the Artificial Intelligence Labs at Subex. In the past, he has worked in VMware, Amazon, Flipkart and Target and has been involved in solving various complex business problems using Machine Learning and Deep Learning. He has been part of the program committee of several international conferences like ICDM and MLDM and was selected as a mentor in Global Datathon 2018 organized by Data Sciences Society. He has multiple patents and publications in the field of artificial intelligence, machine learning, deep learning and image recognition in several international journals of repute to his credit. He has spoken at many summits and conferences like PyData Global, APAC Data Innovation Summit, Big Data Lake Summit, PlugIn etc. He has also published three open-source libraries on Python and is an active contributor to the global Python community.