OrgB - Algorithmic Power, Influence, and Persuasion

What types of power do online algorithms have, and what tactics of influence are they most likely to use? What are examples of these? How do online algorithmic influence tactics differ from in-person human tactics?

1. Reward power

   1. badges, checkmarks, dopamine, etc.

2. Referent Power

   1. pulls people in by showing content one likes

3. Online algorithmic influence tactics are on a much larger scale than in-person ones

   1. ex. social proof (2 million post likes could not happen in person)

How do algorithms exploit the commitment/consistency principle to create filter bubbles?

1. User engages with content they already like or agree with

2. Algorithm reinforces by showing more of this type of content (filter bubbles)

3. Engagement with other users can further reinforce views (echo chamber)

4. User feels internal consistency (opposite of cognitive dissonance) and wants to engage more

5. Cycle repeats

What are the algorithmic equivalents of titles, uniforms, and other ‘trappings’ that build authority?

1. Verification badges

2. Follower count

3. “Expert” tags

4. Engagement rankings

5. “Top stories”/”Recommended for you”

How do digital platforms use algorithmic scarcity?

Algorithmic scarcity may involve content that expires (ie stories), claiming “stock is low!”, etc.

This exploits negative framing, people become more accepting if they feel they will lose out.

How do algorithms amplify social proof beyond what is possible in human interaction?

Algorithms can fabricate unlimited social proof at scale, personalize it to your vulnerabilities, and present it as authentic, thus creating perceived consensus that may or may not exist

Where is the ethical line when algorithms use persuasion tactics?

Algorithms cross the line when persuasive design serves the platform’s best interest over the user’s well-being and informed consent