Responsible AI in Consumer Enterprise

This framework presents the privacy, security, and ethics choices businesses face when using machine learning on consumer data. It breaks things down into the various small decisions teams need to make when building a machine learning system. It is an agile approach to ethics and risk management that aligns with agile software development practices. Businesses waste time if governance or ethics reviews start after systems are built. When done well, accountability quickens rather than slows innovation: business and risk teams need to make contextual calls about what constraints are required when and clearly define desired business outcomes. The scientists’ job is to apply the best algorithms to optimize for these goals. There’s no silver bullet. Contextual judgment calls early on can move mountains. This framework is neither a regulatory compliance compendium nor an exhaustive list of risk management controls. It is a tool to help businesses applying AI to think about ethics and risk contextually. It provides detailed insights for implementation teams and high-level questions for executive leadership.