Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here.

In a blog post today, LinkedIn revealed that it recently completed internal audits aimed at improving People You May Know (PYMK), an AI-powered feature on the platform that suggests other members for users to connect with. LinkedIn claims the changes “level the playing field” for those who have fewer connections and spend less time building their online networks, making PYMK ostensibly useful for more people.

PYMK was the first AI-powered recommender feature at LinkedIn. Appearing on the My Network page, it provides connection suggestions based on commonalities between users and other LinkedIn members, as well as contacts users have imported from email and smartphone address books. Specifically, PYMK draws on shared connections and profile information and experiences, as well as things like employment at a company or in an industry and educational background.

PYMK worked well enough for most users, according to LinkedIn, but it gave some members a “very large” number of connection requests, creating a feedback loop that decreased the likelihood other, less-well-connected members would be ranked highly in PYMK suggestions. Frequently active members on LinkedIn tended to have greater representation in the data used to train the algorithms powering PYMK, leading it to become increasingly biased toward optimizing for frequent users at the expense of infrequent users.

“A common problem when optimizing an AI model for connections is that it often creates a strong ‘rich getting richer’ effect, where the most active members on the platform build a great network, but less active members lose out,” Albert Cui, senior product manager of AI and machine learning at LinkedIn, told VentureBeat via email. “It’s important for us to make PYMK as equitable as possible because we have seen that members’ networks, and their strength, can have a direct impact on professional opportunities. In order to positively impact members’ professional networks, we must acknowledge and remove any barriers to equity.”

VB Event

The AI Impact Tour

Connect with the enterprise AI community at VentureBeat’s AI Impact Tour coming to a city near you!


Learn More

Biased algorithms

This isn’t the first time LinkedIn has discovered bias in the recommendation algorithms powering its platform’s features. Years ago, the company found that the AI it used to match job candidates with opportunities was ranking candidates partly on the basis of how likely they were to apply for a position or respond to a recruiter. The system wound up referring more men than women for open roles simply because men are often more aggressive at seeking out new opportunities. To counter this, LinkedIn built an adversarial algorithm designed to ensure that the recommendation system includes a representative distribution of users across gender before referring the matches curated by the original system.

In 2016, a report in the Seattle Times suggested LinkedIn’s search algorithm might be giving biased results, too — along gender lines. According to the publication, searches for the 100 most common male names in the U.S. triggered no prompts asking if users meant predominantly female names, but similar searches of popular female first names paired with placeholder last names brought up LinkedIn’s suggestion to change “Andrea Jones” to “Andrew Jones,” “Danielle” to “Daniel,” “Michaela” to “Michael,” and “Alexa” to “Alex,” for example. LinkedIn denied at the time that its search algorithm was biased but later rolled out an update so any user who searches for a full name if they meant to look up a different name wouldn’t be prompted with suggestions.

Recent history has shown that social media recommendation algorithms are particularly prone to bias, intentional or not. A May 2020 Wall Street Journal article brought to light an internal Facebook study that found the majority of people who join extremist groups do so because of the company’s recommendation algorithms. In April 2019, Bloomberg reported that videos made by far-right creators were among YouTube’s most-watched content. And in a recent report by Media Matters for America, the media monitoring group presents evidence that TikTok’s recommendation algorithm is pushing users toward accounts with far-right views supposedly prohibited on the platform.

Correcting for imbalance

To address the problems with PYMK, LinkedIn researchers used a post-processing technique that reranked PYMK candidates to decrement the score of recipients who’d already had many unanswered invitations. These were mostly “ubiquitously popular” members or celebrities, who often received more invites than they could respond to due to their prominence or networks. LinkedIn thought that this would decrease the number of invitations sent to candidates suggested by PYMK and therefore overall activity. However, while connection requests sent by LinkedIn members indeed decreased 1%, sessions from the people receiving invitations increased by 1% because members with fewer invitations were now receiving more and invitations were less likely to be lost in influencers’ inboxes.

As a part of its ongoing Fairness Toolkit work, LinkedIn also developed and tested methods to rerank members according to theories of equality of opportunity and equalized odds. In PYMK, qualified IMs and FMs are now given equal representation in recommendations, resulting in more invites sent (a 5.44% increase) and connections made (a 4.8% increase) to infrequent members without majorly impacting frequent members.

“One thing that interested us about this work was that some of the results were counterintuitive to what we expected. We anticipated a decrease in some engagement metrics for PYMK as a result of these changes. However, we actually saw net engagement increases after making these adjustments,” Cui continued. “Interestingly, this was similar to what we saw a few years ago when we changed our Feed ranking system to also optimize for creators, and not just for viewers. In both of these instances, we found that prioritizing metrics other than those typically associated with ‘virality’ actually led to longer-term engagement wins and a better overall experience.”

All told, LinkedIn says it reduced the number of overloaded recipients — i.e., members who received too many invitations in the past week — on the platform by 50%. The company also introduced other product changes, such as a Follow button to ensure members could still hear from popular accounts. “We’ve been encouraged by the positive results of the changes we’ve made to the PYMK algorithms so far and are looking forward to continuing to use [our internal tools] to measure fairness to groups along the lines of other attributes beyond frequency of platform visits, such as age, race, and gender,” Cui said.

VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover our Briefings.