Embarking on the journey of creating ethical AI? Here's a practical guide that will illuminate the path. Navigate the complexities, understand the importance of ethics in AI, and learn how to incorporate them into your AI development process.
Failure to operationalize data and AI ethics leads to wasted resources, inefficiencies in product development and deployment, and even an inability to use data to train AI models at all.
Companies need a plan for mitigating risk
- An operationalized approach to data & AI ethics must systematically & exhaustively identify ethical risks throughout the organization, from IT to HR to marketing to product and beyond.
What Not to Do
There are three standard approaches to data and AI ethical risk mitigation: academic, business, and on-the-ground
- Academic approach
- Academics are fantastic at rigorous and systematic inquiry
- Businesses, on the other hand, tend to ask, “Given that we are going to do this, how can we do it without making ourselves vulnerable to ethical risks?”
- This translates to the absence of clear directives to the developers on the ground and the senior leaders who need to identify and choose among a set of risk mitigation strategies
- On the ground approaches lack the kind of training that academics receive and lack institutional support
How to Operationalize Data and AI Ethics
Identify existing infrastructure that a data and AI ethics program can leverage
- Use a data governance board that convenes to discuss privacy, cyber, compliance, and other data-related risks
- Governance board buy in works for a few reasons
- The executive level sets the tone for how seriously employees will take these issues
- Does not result from lies, manipulation, or communications the patient cannot understand
- Protecting the brand from reputational, regulatory, and legal risk is ultimately a C-suite responsibility
- Change how you think about ethics by taking cues from successes in health care
