What Lawyers Want Everyone to Know About AI Liability
There are more discussions about AI ethics and responsible AI these days, but companies need to be clear about potential AI liability issues.
As artificial intelligence moves deeper into enterprises, companies have been responding with AI ethics principles and values and responsible AI initiatives. However, translating lofty ideals into something practical is difficult, mainly because it’s something new that needs to be built into DataOps, MLOps, AIOps and DevOps pipelines.
There’s a lot of talk about the need for transparent or explainable AI. However, less discussed is accountability, which is another ethical consideration. When something goes wrong with AI, who’s to blame? Its creators, users, or those who authorized its use?
“I think people who deploy AI are going to use their imaginations in terms of what could go wrong with this and have we done enough to prevent this,” said Sean Griffin, a member of the Commercial Litigation Team and the Privacy and Data Security Group at law firm Dykema. “Murphy’s Law is undefeated. At the very least you want to have a plan for what happened.”
Actual liability would depend on proof, and it would depend on the facts of the case. For example, did the user utilizes the product for its intended purpose(s) or did the user modify the product?
In some ways, AI liability is kind of like the multichannel attribution concepts used in digital marketing. Multichannel attribution arose out of an oversimplification, which was “last click attribution.” For example, if someone had searched for a product online, navigated a few sites and then later responded to a pay per click ad or an email, then the last click leading to the sale received 100% of the credit for the sale when the transaction was more complicated. But how does one attribute a percentage of the sale to the various channels that contributed to it?
Similar discussions are happening in AI circles now, particularly those focused on AI law and potential liability. Frameworks are now being created to help organizations translate their principles and values into risk management practices that can be integrated into processes and workflows.
More HR departments are using AI-powered chatbots as the first line of candidate screening because who wants to read through a sea of resumes and interview candidates that aren’t really a fit for the position?
“It’s something I’m seeing as an employment lawyer. It’s becoming used more in all phases of employment from job interviews through onboarding, training, employee engagement, security and attendance, said Paul Starkman a leader in the Labor & Employment Practice Group at law firm Clark Hill. “I’ve got cases now where people in Illinois are being sued based on the use of this technology, and they’re trying to figure out who’s responsible for the legal liability and whether you can get insurance coverage for it.”
Illinois is the only state in the US with a statute that deals with AI in video interviews. It requires companies to provide notice and get the interviewee’s express consent.
Another risk is that there still may be inherent biases in the training data of the system used to identify likely “successful” candidates.
Then there’s employees monitoring. Some fleet managers are monitoring drivers’ behavior and their temperatures.
“If you suspect someone of drug use, you’ve got to watch yourself because otherwise you’ve singled me out,” said Peter Cassat, a partner at law firm Culhane Meadows.
Of course, one of the biggest concerns about HR automation is discrimination.
“How do you mitigate that risk of potential disparate impact when you don’t know what factors to include besides to include or exclude candidates??” said Mickey Chichester Jr., shareholder and chair of the robotics, AI and automotive practice group at law firm Littler. “Involve the right stakeholders when you’re adopting technology.
