Tackling Ethical Risk and Bias in Machine Learning Applications
Tackling Ethical Risk and Bias in Machine Learning Applications

Abstract: 

As new applications in AI and Machine Learning enable game-changing opportunities, they generate risks related to ethical considerations and emerging pitfalls. From model and data bias to integrity and consumer privacy, unexpected ethical ‘landmines’ can cause significant harm for businesses, their customers, and others. Companies building AI and Machine Learning capabilities into business initiatives must tackle expected and unexpected ethical issues that arise.
While some policy choices allow for a subjective decision-making approach, others require planning and deliberate engagement. Participation in community outreach, for example, or initiatives related to corporate social responsibility, present relatively clear-cut trade-offs. On the other hand, it is hard to avoid biased service offerings or breaches of consumer privacy. In such situations, getting it right requires coordination between managers, data scientists, and, increasingly, internal compliance.
As teams approach critical points in development, managers need to provide explicit guidance, as well as more general frameworks and references for understanding the ethical decisions being made. Without active management, firms can easily lose control of ethical policy and engage in practices inconsistent with corporate and cultural values.

This presentation addresses overcoming bias and ethical risks, as well as frameworks and regulations for data science and related fields including FATE (fairness, accountability, transparency, and ethics), Weapons of Math Destruction, and GDPR. Using detailed case studies, it will help you recognize the ethical impact of data projects, identify latent risks, and address issues using appropriate frameworks.
Business leaders who attend this workshop will be able to:
- Recall and understand common data ethics frameworks
- Recognize ethical impacts of data projects in real business applications
- Apply data ethics frameworks to business decisions

Bio: 

Javed is an economist and data scientist with experience in banking, finance, forecasting, risk management, consulting, policy, and behavioral economics. He has led development of analytic applications for large organizations including Amazon and the Federal Reserve Board of Governors, and served as a researcher with the Office of Financial Research (U.S. Treasury). He holds a PhD in financial economics and MA in statistics from U.C. Berkeley, as well as undergraduate degrees in operations management and systems engineering from the University of Pennsylvania. Currently, Javed is a Senior Data Scientist on the Corporate Training team at Metis, where he works with companies to upskill their staff in data science and analytics.