FAIR AND UNBIASED MACHINE LEARNING

  • Model fairness evaluation and correction
  • ML explanatory systems for customers and stakeholders
  • Correction of model bias
  • Model and workflow auditing

FAIR AND UNBIASED MACHINE LEARNING

Model Interpretability and Visibility

Machine learning solutions are often criticized as opaque systems which magically generate unexplainable outputs. Expero builds systems to investigate, explain, and demonstrate the decision process of machine learning models and systems. Our solutions highlight bias in model outputs to users, enable correction of biases, and explain why systems behave the way they do.

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MODEL BIAS INVESTIGATION

Discover bias in your analytics systems

  • Service-based bias discovery: Expero uncovers bias and unfair decisions generated by your machine learning models and systems
  • Find the root cause of biased outputs from your machine learning systems
  • Determine blast radius impact of biased outputs from models: which systems or constituents are affected

Explanatory systems

Algorithmic investigation tools matched with customized visuals

  • Bias investigation and discovery tools: Expero will build ad-hoc investigation tools and insights dashboards to highlight bias and unfair decisions generated by your machine learning models and systems
  • Visualizations to display to users and stakeholders the biased classes of output from models and their root causes

bias correction

Correct your models and systems behavior

  • Correct model behavior by rebalancing training data or ablating internal model components
  • Generation of new data pipeline components and model architectures to assist in mitigating model output bias
  • Reporting on systematic processes for mitigating model bias and downstream effects

system auditing

Evaluation of full analytics systems

  • Expero acts as an auditor of machine learning systems for regulators and management teams to ensure compliant behavior
  • Deliverables include reports, functioning software products, or experimental notebook-style investigatory findings

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