Abstract for: Applying Community Based System Dynamics (CBSD) during the Product Development Process to Foster ML Fairness and Ethical AI
Machine learning (ML) and Artificial Intelligence (AI) are premier technologies of what many consider the 4th industrial revolution. However, the models and algorithms that underlie these technologies have exhibited a propensity to amplify and exacerbate harmful societal biases. Recent research on Ethical AI and algorithmic fairness has highlighted that the problem formulation phase of the development of systems that use machine learning can be a key source of bias and have significant downstream impacts on fairness outcomes. However, very little attention has been paid to methods for improving the fairness of this critical phase of machine learning system development. Current practice neither accounts for the dynamic complexity of high-stakes domains nor incorporates the perspectives of vulnerable stakeholders. This talk will explore the application of community-based system dynamics (CBSD) during the product development process in order to foster equitable and inclusive technology based on machine learning and artificial intelligence.