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AI Governance: Achieving True Algorithm Neutrality will be Elusive
AI Governance refers to the application of Artificial Intelligence algorithms in a way that is responsible and governed. AI governance goes beyond ethical usage, it also includes risk management and regulatory compliance. For example, Cisco defines these elements of AI governance: “Transparency, Fairness, Accountability, Privacy, Security, and Reliability.”
Many businesses are now beginning to transition from exploratory to production usage of AI. While businesses tinkered with how they might be able to use AI to automate and innovate, they often gave little thought to governance issues. But now, as production rollouts are planned and executed, questions roll in.
Mike Beckley, CTO of Appian, told Tom Taulli in a Forbes article, that “AI projects are rarely coordinated across a company and data science teams are often isolated from application development. And now regulators are starting to ask questions businesses don’t know how to answer.”
The Rockefeller Foundation wrote that “AI’s many applications and potentially profound outcomes make its governance complex and use-specific. Governance, therefore, requires bold action and novel blended approaches that tap incentives, goodwill, and expertise.”
But ultimately proving that AI is neutral and ethical will be something very difficult to achieve.
Peter Wayner, technology writer, in a piece for VentureBeat wrote that “where AI governance matters the most is where the decisions are the most contentious. While the algorithms can provide at least the semblance of neutrality, they cannot simply eliminate human conflict. If people are unhappy with the result, a good governance mechanism can only reduce some acrimony… The success of governance is limited by the size and magnitude of the problems that the AI is asked to solve. Larger problems with more in-depth effects generate deeper conflict. Asking AIs to make decisions that affect people’s health, wealth or careers is asking for frustration.”
