Artificial intelligence (AI) programmers are developing bots that can identify digital bullying and sexual harassment.
Known as “#MeTooBots” after the high-profile movement that arose after allegations against the Hollywood producer Harvey Weinstein, the bots can monitor and flag communications between colleagues, and are being introduced by companies around the world.
Programmers say it is not easy to teach computers what harassment looks like, with its linguistic subtleties and gray lines.
Illustration: Mountain People
“I wasn’t aware of all the forms of harassment. I thought it was just talking dirty. It comes in so many different ways. It might be 15 messages ... it could be racy photos,” said Jay Leib, chief executive of Chicago-based AI firm NexLP.
NexLP’s AI platform is used by more than 50 corporate clients, including law firms in London.
The industry is a potentially fertile ground for the bots to examine — a third of female lawyers in the UK report having experienced sexual harassment.
The bot uses an algorithm trained to identify potential bullying, including sexual harassment, in company documents, e-mails and chat. Data is analyzed for various indicators that determine how likely it is to be a problem, with anything the AI reads as being potentially problematic then sent to a lawyer or human resources (HR) manager to investigate.
Exactly what indicators are deemed red flags remains a company secret, but Leib said that the bot looked for anomalies in the language, frequency or timing of communication patterns across weeks, while constantly learning how to spot harassment.
Leib believes other industries could also benefit.
“There’s a lot of interest from clients across sectors, such as financial services, pharmaceuticals,” he said.
Brian Subirana, a lecturer in AI at Harvard and the Massachusetts Institute of Technology, said that the idea of using AI to root out harassment was promising, though the capabilities of the bots are limited.
“There’s a type of harassment that is very subtle and very hard to pick up. We have these training courses [about harassment] at Harvard and it requires the type of understanding that AI is not yet capable of,” Subirana said.
The underlying issue is that AI can only reliably conduct basic story analysis, meaning it is taught to look for specific triggers. It cannot go beyond that parameter and cannot pick up on broader cultural or unique interpersonal dynamics. This means the bots risk leaving gaps or proving oversensitive.
“We don’t know when AI will break the ‘story understanding’ frontier,” Subirana said.
If an employee’s correspondence gets flagged, it can create a climate of distrust, and offenders might learn how to trick the software, he said.
Alternatively, offenders could resort to other mediums of communication that are not monitored by the bots, he added.
A further concern is protecting the confidentiality of the data that is collected.
If the software makes a mistake and the data is leaked, internal communications between employees could be seen by rival companies, Subirana said.
“There are still hurdles to jump before AI can do what we think it can,” Subirana said. “I haven’t heard of any HR [department] who has said it is useful for this yet.”
He is not the only skeptic.
Sam Smethers, chief executive of the Fawcett Society, a women’s rights non-governmental organization, said that there could be unforeseen consequences from policing staff communications.
“We would want to look carefully at how the technology is being developed, who is behind it, and whether the approach taken is informed by a workplace culture that is seeking to prevent harassment and promote equality, or whether it is in fact just another way to control their employees,” Smethers said.
“It has implications for the privacy of staff and could be abused,” she added.
She suggested educating staff about appropriate attitudes and behaviors would be more effective in protecting potential victims.
Despite the concerns, Subirana believes the bots could offer indirect benefits.
“The use case that I could imagine is a training one. It could provide a database [of problematic messages],” he said.
Believing communications are monitored could make people less prone to harass colleagues, in a placebo known as the Hawthorne effect, he added.
“There is a preventive element here,” Subirana said.
Similar technology is being used to retrospectively scour large volumes of digital communications to fight harassment claims.
One law firm using the technology is Morgan Lewis, which specializes in US employee and labor practice. Instead of monitoring employees, the AI is used to analyze clients’ past communications.
“We’ve used this in dozens of cases,” partner Tess Blair said.
The technology usually helps to build a case rather than provide the smoking gun, she said.
AI start-up Spot has created a chatbot that allows employees to anonymously report sexual harassment allegations.
It is trained to give advice and asks sensitive questions to further an investigation into the alleged harassment, which might have played out digitally or physically.
Spot aims to account for gaps in HR departments’ abilities to deal with such issues sensitively, while also preserving anonymity.
These variants of AI could work together to detect workplace harassment more effectively, Blair said.
Tools such as Spot can be deployed before lawyers or HR staff are involved. If a full investigation proceeds, technology such as Morgan Lewis’ can analyze clients’ digital communications to build a case.
However, as good as the bots become, Blair sees their role as assistants to humans, rather than an all-seeing judge, jury and executioner.
“Computers are not value-judging, they are saying: ‘This doesn’t fit the pattern,’” she said. “It is then up to us to interpret.”
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