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AI in Social Media: Ethical Considerations of AI and Algorithms in Shaping Social Media Interactions

by NimitApril 30th, 2024

Too Long; Didn't Read

This article delves into the influence of social media, tracing its evolution and the pivotal role of AI and algorithms in content personalization. It discusses the ethical implications, such as privacy concerns and bias, that arise from the use of consumer data by companies like Meta. The article also examines the potential for algorithms to create echo chambers and the need for regulatory measures to address algorithmic bias, aiming for a balanced approach between innovation and user protection in the rapidly evolving digital landscape.

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These days, social media plays an integral part in life, influencing how we communicate, obtain information, and share information with each other. Although less than 30 years old [1], we have seen a huge evolution of platforms like Facebook, Twitter, TikTok, and LinkedIn: Facebook is now Meta and owns WhatsApp, Instagram, and Threads; Twitter is X. Both are owned by billionaires who have anticipated technological trends, capitalized on these opportunities and pioneered the way for a rapidly innovative industry.

The biggest driving success of social media, from a business point of view, is consumer data. A large majority of social media platforms are free of charge and are able to operate this way because you’re allowing them access to your data in return. With this, companies like Meta have been able to shape social media content and interactions. This is something that can be viewed as beneficial or unethical depending on the intent and use case, with issues like privacy and manipulation far from uncommon now.

In this article, we will be delving into the ethics of using AI and algorithms in social media, examining how they help personalize content, potentially leading to bias and echo chambers, and the future of AI in social media platforms.

‘An algorithm is a series of instructions designed to solve specific problems, perform tasks, or make decisions’ [2]. In social media, these instructions govern user experiences such as content ranking, filtering, and personalization, typically based on trends for different consumer groups. This allows you as the user to find content that will interest you much faster, as it is likely to appear on For You Pages without you having to search for it.

Algorithms haven’t always been used in social media. It was in 2009 that Tumblr and Facebook introduced ranking metrics and personalized feeds. In 2012, more followed suit. Facebook upped its algorithmic use in its news feed and introduced sponsored content [3]. LinkedIn began a “semi-structured feed,” and YouTube introduced a ranking algorithm, prioritizing watch time over quantity [4]. Since then, algorithms’ roles in such social media have only continued to increase. By 2015, machine learning was starting to play a role in sorting and filtering algorithms, underpinned by developments in Big Data efforts. 2016’s political events like the Trump election and Brexit referendum (and a pivotal year in the Cambridge Analytica scandal) then brought to public attention some of the ethical dilemmas surrounding such heavy and increasing use of algorithms and user data collection in social media.

These days, AI-powered algorithms are built so precisely they recommend content based on your specific previous interactions on the platform, with growing user data allowing social media companies to eliminate a significant majority of any guesswork. Documentaries like The Social Dilemma [5] and The Great Hack [6] give some insight into just how engineered some of these algorithms are, often with the end goal of keeping a user on their platform for longer and driving interaction with the platform. While increased user interaction means more data for social media companies to collect, tweak, and repeat this process, this goal does not take into consideration what’s best for the user and wider society with regard to social media consumption.

Ethical Issues Stemming from Algorithmic Bias

Algorithms can perpetuate harmful biases, and in the context of social media, this often manifests as the reinforcement of stereotypes and limited exposure to diverse viewpoints. Culminated over time, this can fuel discrimination, polarise electorates, and even contribute to extremism and right-wing/autocratic populism, which exploit societal divisions.

At a low level of impact, examples of algorithmic bias could include job postings for certain industries, e.g., LinkedIn being shown more to men than women, because the training data sets the algorithms were modeled on predict that more men are likely to work in those roles. While this doesn’t necessarily have an immediately obvious harmful impact, it creates a negative feedback loop that works against society’s goals to improve equality and representation across workplaces and specific industries. In this way, gender bias is perpetuated as a result of algorithmic bias by limiting opportunities for women to see those ads.

Now, to delve deeper into the higher-level implications of algorithmic bias, we will examine the 2016 Trump election as a case study.

Case Study: How Algorithms Affected Polarization/Echo Chambers in Trump 2016 Election

Throughout the course of the 2016 US presidential election, research has shown that algorithms may have exacerbated political polarization by amplifying echo chambers and limiting the electorate’s exposure to diverse viewpoints. Many think it was this mechanism that allowed Trumpism to run rampant and also why so few people predicted his victory [7], as these dynamics were not fully understood nor visible during the election period.

Even on social media, therefore, it seems true that ‘birds of a feather flock together’ [8].

Algorithms tailor content to align with users' preferences, shielding them from opposing views and opinions, and creating ‘filter bubbles’. In turn, some individuals within these filter bubbles continue the positive reinforcement of their views by further isolating themselves within digital environments where their beliefs and opinions are echoed and even more so reinforced. These are called ‘echo chambers’.

Essentially, social media fosters an environment in which we all ingest such extremely personalized news and content feeds that we become blind to others’ differing perspectives.

Currently, it is not clear from research whether social media only serves as a platform allowing echo chambers to emerge or whether its use of algorithms goes so far as to play a role in creating these echo chambers[8].

The 2016 Trump win shows how social media companies’ agendas can have widespread consequences affecting millions of people, which, while unintended, are still hugely impactful and also unethical. We know that social media algorithms are designed to maximize user engagement and retention by serving content tailored to individual preferences. Now, though, we also see how this applies even to political conversations, leveraging users’ political leanings and vulnerabilities.

Who knows how the election would have played out if algorithms weren’t impacting people’s personal political beliefs?

And how does this affect democracy and political stability? If people are not aware of these manipulations, can the outcomes be considered fair and valid?

The role of algorithms and AI in social media is only continuing to evolve alongside our innovations within those fields. From AI-generated content to AI-powered algorithms allowing the precision we touched on earlier, the ethical challenges that come with its integration into social media will similarly need to be increasingly considered. This needs to start with addressing algorithmic biases and promoting algorithmic transparency in social media platforms.

Future policy considerations and regulations will play a crucial role in shaping the trajectory of AI use in social media. There is already growing attention being given to the need for algorithmic regulation and protection from the harm these invisible biases can cause, especially in the US. So far, initiatives include[9]:

The “Algorithmic Accountability Act of 2022”
The “Eliminating Bias in Algorithmic Systems Act of 2023”
The “AI Bill of Rights”

While there are more frameworks currently being drawn up in the proposal stages, there is still insufficient regulation of such powerful technologies when we consider the impact they already have. Striking a delicate balance between innovation and safeguarding user interests will require a collaborative approach involving technology companies, policymakers, and users alike.

Social media does have the power to be a force for good, uniting communities and fostering inclusivity; however, without adequate frameworks and regulations protecting user rights, its use of AI and algorithms also has the power to be destructive to individuals and wider society.