AI ethics has long been a hot-button issue. For some, it’s reduced to a debate about whether AI should be making decisions traditionally reserved for humans. There are misconceptions that AI systems will quickly evolve into superhuman intelligence, or that they’ll be a silver bullet to solve problems for which we as a society don’t yet have answers.
For others, it’s figuring out how to operationalize ethics beyond simply articulating a set of ethics principles. – There’s also too much talk about ethics after negative AI incidents are exposed.
As other countries and big tech companies rush to win the AI race, there are global conversations underway to advocate for AI solutions that safeguard human rights, minimize the impact of unintended bias, and advance the social good. All recognize (though not all will heed) the vital need to establish clear standards for trustworthy AI, and uphold them.
The first step is to define your ethical principles. Numerous policy and research organizations have already done the hard work of analyzing and documenting the different standards and ethics documents around the world, which can serve as a good starting point. [i] Once you’ve established your AI ethics principles, what’s next?
It’s time to align your culture and institutional processes with your ethical principles. Operationalizing ethical AI requires a shift at all levels within an organization to embed and execute against a fundamentally new way of thinking. When your ethical principles are championed by leadership, embodied throughout staff, and in lockstep with your overarching business goals, it’s much easier for your teams to follow suit and make sure the technology measures up.
Leaders should take the conversation forward. Have hard discussions about what applications of your technology meet your principles, and what violates them. These types of difficult conversations ensure everyone is on the same page to take a stance against violations of the principles. This is your chance to create channels to escalate risks and build paths for recourse when mishaps do happen. Most importantly, it’s about incentivizing the people on the ground developing the AI to realize the value of this way of building AI, and empowering them to speak up when something isn’t working. When leaders take responsibility for how the ethics and AI message is spread and embraced throughout a culture, they help ensure the systems developed are accountable.
Overseeing the build of AI systems to align with your ethical principles is no small feat. Beyond open discourse among stakeholders, organizations need to institute training programs. Managers and developers must know how to identify and flag risks in systems so that they can be appropriately documented and monitored.
Even the most careful organization will have instances where an AI-enabled system runs amiss or doesn’t perform as expected. To address this challenge, last year Accenture announced an AI governance guidebook with recommendations on how to embed governance throughout development teams, including the selection of “fire wardens” with the responsibility to escalate issues when they arise.[ii]
By creating mechanisms to document and share lessons learned internally about how and why these incidents happen, you will spur the evolution of an ethics-driven culture, and make these mishaps far fewer in between.
Data scientists training models and machine learning engineers building AI-powered applications shoulder the burden of putting these principles into practice. So how do you help them? Build in regular checkpoints and processes for adhering to ethical principles throughout the development process. If the first time your data scientists and developers are thinking about whether a system is transparent or accountable is after it’s deployed, it’s too late. Work in tandem to establish checkpoints throughout model design and application development.
