How Companies Should Answer The Call For Responsible AI
There’s widespread consensus that we’re in the throes of the fourth industrial revolution Artificial intelligence and its sister technologies is transforming virtually every business. Yet with AI’s enormous potential comes great responsibility. The majority (77%) of CEOs say that AI threatens to increase vulnerability and disruption to the ways they do business. Unfortunately, the call for responsible AI has taken a backseat for many companies. Only 25% of companies say that they definitely prioritize considering the ethical implications of an AI solution before investing in it, according toresearch by PwC.
In 2016, Amazon, Facebook, Google, DeepMind, Microsoft, and IBM came together to found the Partnership on Artificial Intelligence to Benefit People and Society (Partnership on AI). Since it was founded, the nonprofit coalition has amassed more than 100 partners, including members from industry, academia, and civil society. The partnership marks an important shift towards prioritizing responsible AI. But it’s merely a small step in the right direction.
Here are three important steps that companies need to embrace in order to progress towards responsible AI.
An important prerequisite for responsible AI is explainability and interpretability. According to PwC’s research, 84% of CEOs agree that AI-based decisions need to be explainable in order to be trusted. The proverbial “black box” of AI needs to be opened. Black box models should be supplanted with models that are interpretable. As PwC warns, a lack of interpretability can “expose an organization to operational, reputational, and financial risks. To instill trust in AI systems, people must be enabled to look “under the hood” at their underlying models, explore the data used to train them, expose the reasoning behind each decision, and provide coherent explanations to all stakeholders in a timely manner.” As IBM’s AI ethics policystates, “Allow for questions. A user should be able to ask why an AI is doing what it’s doing on an ongoing basis. This should be clear and up front in the user interface at all times.”
Explainability goes hand in hand with documentation throughout the entire AI lifecycle, from model design to implementation and use. Design and decision-making processes should be documented. Additionally, it should be clear when and why AI systems make mistakes. AI is inevitably going to fail. By making all aspects of AI development transparent, we can empower humans’ judgment to kick in and avoid many negative fallouts.
It’s easy to pay lip service to the importance of responsible AI. This tendency is so pervasive that we now have a dedicated term to encompass the practice—“ethics washing”, also known as “ethics theater”. Ethics washing involves falsifying or exaggerating a company’s promotion of “AI for good” initiatives. As a recentreport from research institute AI Now highlights, “While we have seen a rush to adopt such codes, in many instances offered as a means to address the growing controversy surrounding the design and implementation of AI systems, we have not seen strong oversight and accountability to backstop these ethical commitments.
