Fairness, Explainability and Bias in Machine Learning Applications | Kisaco Research

Fairness, Explainability and Bias in Machine Learning Applications

  • Exploring Machine Learning Applications in Credit Risk
  • Understanding ‘The Fairness Issue’
  • Explaining ‘The Explainability Issue’
  •  Investigating Machine Learning and Model Risk Frameworks

Session Topics: 
AI/ ML
Speaker(s): 
Speaker

Author:

Peter Quell

Head of the Portfolio Analytics Team for Market and Credit Risk
DZ Bank

Dr. Peter Quell is Head of the Portfolio Analytics Team for Market and Credit Risk in the Risk Controlling Unit of DZ BANK AG in Frankfurt. He is responsible for methodological aspects of Internal Risk Models and Economic Capital. Prior to joining DZ BANK AG Peter was Manager at d-fine GmbH where he dealt with various aspects of Risk Management Systems in the Banking Industry. He holds a MSc. in Mathematical Finance from Oxford University and a PhD in Mathematics. Peter is member of the editorial board of the Journal of Risk Model Validation and a founding board member of the Model Risk Management International Association (mrmia.org).

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Peter Quell

Head of the Portfolio Analytics Team for Market and Credit Risk
DZ Bank

Dr. Peter Quell is Head of the Portfolio Analytics Team for Market and Credit Risk in the Risk Controlling Unit of DZ BANK AG in Frankfurt. He is responsible for methodological aspects of Internal Risk Models and Economic Capital. Prior to joining DZ BANK AG Peter was Manager at d-fine GmbH where he dealt with various aspects of Risk Management Systems in the Banking Industry. He holds a MSc. in Mathematical Finance from Oxford University and a PhD in Mathematics. Peter is member of the editorial board of the Journal of Risk Model Validation and a founding board member of the Model Risk Management International Association (mrmia.org).