There is a rousing chorus of excitement--and investment--around new developments in machine learning and artificial intelligence. Neural network techniques first developed decades ago have been reinvigorated by new data sources, computer hardware, and theoretical advances. Under the name “Deep Learning” these techniques have produced high-profile breakthroughs in image recognition, speech recognition, playing complex games such as Go and poker, self-driving cars, and much more.
The chorus continues to swell, but it is accompanied by a counterpoint of concern and caution, raising questions about fairness, transparency, privacy and more: topics that are sometimes bundled together under the umbrella of “algorithmic accountability”.
Algorithmic accountability is already following a path trodden by debates around privacy, a topic with which many Human Capital Management professionals will be more familiar. When Big Data burst on the scene it was quickly clear that here was a major commercial opportunity. But when all that data being collected is about you and me, we become concerned about privacy. As the Big Data / privacy debate has evolved, it has become clear that companies who wish to profit from the opportunities of Big Data must reckon seriously with the challenges of privacy: Chief Privacy Officers are now appearing on executive boards, many jurisdictions now have Privacy Commissioners to stand up for the rights of citizens, court cases around privacy violations carry major penalties, and privacy breaches can badly damage brand reputations. The privacy debate is not an event that happened once and is over: it is an evolving discussion that will continue to play out for years.
The algorithmic accountability story has parallels. As algorithms become more influential and ubiquitous, and as their decisions and recommendations increasingly shape our opportunities and our experiences, it’s only natural that many people will be concerned about fairness at an individual and a group level. Businesses who wish to benefit from machine learning (especially applied to people) must pay attention to these concerns. New machine learning techniques---and particularly Deep Learning---have become increasingly difficult to interpret, and so transparency and explanation have become central to algorithmic accountability.