The Ethics of the Algorithm: When “Smart” Tech Gets Too Personal
AI algorithms impact every aspect of life, from emerging smart cities to social media. Algorithms shape what people see, read, and engage with across social media, determine browser search results, and recommend videos on streaming services. Ubiquitous enough to become part of the background noise of daily life, social media algorithms in particular have the potential to influence the products we buy, the entertainment we consume, our politics, and personal values. The sheer amount of online data even impacts our infrastructure, driving the increased need for high-speed fiber internet.
The rise of influential social media algorithms raises some thorny ethical questions. Algorithms train on data that reflects very human biases, potentially reinforcing stereotypes surrounding race, gender, sexuality, religion, and culture. Algorithms can expose children to inappropriate content, a concern that led Australia to ban social media use for children aged 16 or younger. The massive amounts of user data collected to train and run social media algorithms raise concerns about consent, privacy, and security, raising the very legitimate question of who owns an individual’s social media data — the person, or the platform?
How Do Social Media Algorithms Work?
A social media algorithm is an AI-driven system that analyzes likes, shares, comments, and watch times to predict which content users will interact with. Some even analyze mouse hovering behavior, as hovering indicates content the user is considering viewing.
Algorithms optimize relevance based on your interests, values, and engagement history. Importantly, relevance does not necessarily translate into well-being. Emotionally charged content ranks highly for engagement, leading algorithms to push content that may offend, anger, or disgust. Content presented in chronological order helps people stay on the platform, as otherwise users risk missing out on trending topics.
So how do social media algorithms work? In most cases, they follow an ongoing cycle:
- The algorithm tracks a user’s behavior, noting which content garners the most clicks, comments, and shares.
- The algorithm selects new content that aligns with the user’s interests. User location, language, and even device type are considered.
- Content is scored based on its popularity, relevance, and recency.
- The algorithm evaluates how the user reacts to recommended content, generating new data that informs future recommendations.
Unless the user chooses to permanently leave the platform, this process continues indefinitely, generating increasingly accurate data on user preferences.
When Personalization Becomes Intrusive
On the surface, social media algorithms seem harmless: if you watch a few cooking videos, you’ll likely see more recipes in your feed. The issue arises when personalization becomes reinforcement. Once an algorithm identifies patterns in your interests or beliefs, it tends to prioritize similar content — including sensational or misleading posts designed to drive engagement. Over time, this narrowing effect can limit exposure to diverse viewpoints and deepen existing biases.
The ethical concern grows when data-driven personalization intersects with profit motives. Platforms monetize user behavior primarily through targeted advertising and partnerships, which incentivizes deeper tracking and prediction. When algorithms are optimized for engagement above all else, content curation can begin to resemble influence. Ads or recommendations based on sensitive behavioral data can feel invasive, especially when users don’t fully understand how that data was collected or used.
As for the amount of data gathered by social media platforms, few users truly understand the extent of the risk. Depending on the platform, social media collects an average of 21 out of 32 possible data points:
- Behavioral data, including likes, dislikes, comments, and search histories.
- Contact data, call logs, and SMS histories.
- Device information, such as device type, network connection, operating system, storage information, and even battery level.
- Location data through GPS and IP addresses.
- Off-platform activity is tracked through tracking cookies, browser fingerprinting, and geofencing.
- Payment information for in-app purchases.
- Personal information, such as names, phone numbers, email addresses, dates of birth, marital status, and gender.
Ethical Concerns Around Data and Influence
Social media platforms’ insatiable need for data raises serious ethical questions and concerns about data ownership. Researchers and regulatory bodies alike express concern about the lack of transparency regarding how apps gather and store data, the types of data they collect, and how they use and disseminate that data. Others question how information gleaned from massive amounts of data can shape user behavior, influence decisions, and impact societal norms.
The most serious ethical concerns surrounding social media data gathering include the following:
Surveillance Capitalism
Ethically acceptable data collection requires user consent and a commitment to gather only the data necessary for a specific purpose, which should be transparently outlined in the app’s data policy. Sadly, this is rarely the case, with companies gathering massive amounts of data to sell predictions of user behavior to third parties.
Even when data is “anonymized” to hide user identity, it’s often possible to identify individuals, potentially exposing their data to parties who can then use it for their own ends.
Social Media Algorithm Bias
AI algorithms are trained by vast amounts of data created by humans. This data often reflects historical and societal prejudices and biases, which the AI uses to make algorithmic decisions. AI discrimination is most problematic in systems used for hiring, law enforcement, or banking, but can have negative impacts on your social media feed as well, pushing prejudicial content or ignoring content created by marginalized communities.
Algorithmic Manipulation
A social media algorithm’s ability to predict and recommend content can create a “filter bubble,” in which each piece of new content reinforces the user’s existing beliefs. Such echo chambers often become more extreme over time, as anger-inducing content attracts more engagement, prompting the algorithm to serve up similar content. The result is social media apps that polarize discourse and foster an “us vs. them” mentality that rewards engagement at the expense of critical thinking.
Echo chambers are susceptible to manipulation of consumer behavior and political views. At its worst, social media feeds can manipulate public opinion on important issues and influence elections.
Who Owns Your Data?
As a user, you have little control over the data you generate in social media apps. Most Terms and Conditions specifically grant ownership of in-app data to the social media platform, granting the app access to and ownership of a highly valuable asset they can use with little or no accountability.
Why Infrastructure Still Matters
Algorithm-driven platforms rely on fast, always-on connectivity to enable real-time personalization. Without a reliable internet provider, users experience buffering, lag during live streams, and reduced quality when viewing high-definition content. Data is also vulnerable to interception when transmitted over insecure connections or electrical wiring. What is fiber internet if not an innovative solution to many of these issues? It offers greater security by sending data as light pulses rather than electrical signals.
Multiple social media users on a single home network can impact data delivery speeds. To keep your data secure, run a routine internet speed test to ensure you’re getting the speeds promised by your internet provider, and secure your network with a strong, unique password.
Smart tech, such as social media algorithms, raises ethical questions with no easy answers. Stay aware of changes to your social media terms and conditions, and stay current on data-related news. Few of us are likely to abandon social media entirely. Instead, approach your apps with an eye towards intentional use, and try to make your social media algorithms work for you, not against you.