The Influence of Artificial Intelligence on Social Networks

Artificial intelligence seems to encourage and incubate more social interactions on the web, such implementing algorithms that automatically recognize and tag your friends in photographs. But at the same time, although programs involving artificial intelligence may provide more accurate Facebook suggestions or news feed filters, they can be limiting to the diversity of people and news that users are exposed to. For example, Bright.com, acquired by LinkedIn in 2014, provides AI technology in filtering through job-candidate matches for employers and employees. While these services facilitate and enhance the job searching process, they also fast-forward and make connections between people who have similar interests and backgrounds more easily. In other words, with more social media AI algorithms, people become more connected with those who are similar or share the same interest as they do. In contrast, people would have less facilitated opportunities to make connections with those in different backgrounds. On another note, AI technology is improving the accessibility of social media to disabled people, such as Matt King, who had been blind for over two decades. Facebook has been developing AI systems that would recognize objects and themes of photos and report these information to those who cannot see the photos. With these enhanced accessibility aspects of social media, larger portions of the population, mainly the disabled, are starting to connect on the web.

In a research done in Singapore focusing on the influence AI has on social decision making, researchers used AI algorithms to suggest to people traffic routes and fares that would save time and money. According to every person’s preferences on transit time, delay time, and congestion, the AI system provides different, personalized routes for every user. This leads to the potential that AI systems can provide alternatives to Braess’s Paradox, which states that “adding resources to a transportation network can sometimes hurt performance at equilibrium” (http://www.cs.cornell.edu/home/kleinber/networks-book/networks-book-ch08.pdf, 232). As discussed in a previous blog post, Braess’s paradox was evident in highways in Seattle. However, if people begin to rely on personal route suggestions provided by AI systems, they may decide to take different routes and spread out in traffic systems and reduce the overall traffic time at equilibrium. This way, the Seattle government would not have the trouble of removing Alaskan Way while still improving the overall city transportation system.

Article on AI and Social Media

Advertisements
This entry was posted in Uncategorized. Bookmark the permalink.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s