Governing Emerging Tech: AI, Data & Digital Ethics
Overseeing Innovation, Opportunity, and Uncharted Risk.
Core Learning Objectives
- Establish a board-level governance framework for the ethical development and deployment of Artificial Intelligence.
- Scrutinize AI models for potential bias, and understand the resulting reputational and legal risks.
- Guide management on treating data as both a strategic asset and a significant liability.
- Oversee the risk-reward calculus of large-scale digital transformation initiatives.
Detailed Course Outline
Module 1: AI Governance for the Modern Board
Key Concepts: Demystifying AI: Machine Learning (ML), Large Language Models (LLMs), Generative AI (GenAI). The board's role is not to understand the algorithms, but to govern their *use* and *impact*. Establishing an AI Ethics Committee or framework. The risks of "black box" models and the importance of explainability.
Special Lesson: AI Bias and its Reputational Fallout
Rationale: AI models learn from data. If the data reflects historical societal biases (in gender, race, etc.), the AI will amplify them at scale. This can lead to discriminatory outcomes in hiring, credit lending, and customer service, resulting in massive brand damage and litigation.
Board's Role:
The board must challenge management to prove how they are testing for and mitigating bias in the AI systems they deploy. This includes scrutinizing the diversity of the data sets used for training and the results of model audits.
Case Study: The Apple Card Gender Bias Investigation (2019)
Scenario: Apple and Goldman Sachs launched a new credit card with an algorithm determining credit limits. High-profile tech figures publicly showed that the algorithm offered men significantly higher credit limits than their wives, even with shared assets and higher female credit scores. This sparked a viral outcry and a regulatory investigation into gender discrimination.
Boardroom Takeaway:
The algorithm was the problem, but the brand was the victim. This case proves that AI bias is not a theoretical risk; it is a clear and present danger to a company's reputation and can expose it to discrimination lawsuits. The board must ensure that a robust ethical and testing framework is in place *before* a customer-facing AI product goes to market.
Module 2: Data: The Fuel for Digital Transformation
Key Concepts: Data as a strategic asset on the balance sheet. Data provenance (where did it come from?), data quality, and data lineage (how has it changed?). The governance challenges of "Big Data." The intersection of data strategy and privacy regulations (GDPR, CCPA).
Module 3: Overseeing Digital Transformation Holistically
Key Concepts: This module ties everything together. A successful digital transformation is a cultural and business model change, enabled by technology (Cloud, Data, AI). The board's role is to ensure these initiatives have clear business objectives, realistic budgets, and are managed within the company's risk appetite. Common failure points: culture clash, talent gaps, and "shiny object syndrome."