Algorithmic Bias and Political Representation in the Digital Age

James Black

Political representation relies on fairness- it’s based on the principle that all people should be equally heard and considered in collective decisions. Yet, in the new digital age, political representation has been increasingly challenged by algorithms- systems that process data through instructions to predict behaviour, often influencing outcomes to extreme levels. When these systems produce forms of prejudice, algorithmic bias exists, distorting representation and further deepening inequality. The extent of this distortion varies across the world: some countries have begun to subdue algorithmic bias through intervention, whilst other countries have allowed it to undermine the very basis of fair political participation. 

The U.S remains the clearest example of how algorithmic bias can reshape political transparency. During the 2016 presidential election, data analytics firm ‘Cambridge Analytica’ had acquired data from Facebook, where millions of users (typically swing voters) were subject to targeted political adverts- These algorithms were able to group the electorate into categories of persuadable and non-persuadable voters. The result was a broken and arguably unfair political landscape, where the algorithm, funded by political parties and billion dollar firms, were heavily influencing election outcomes. This system benefited groups that were easily influenced by the algorithms given ideology, while not rendering others. This can be defined as a form of selective representation, governed by data. The U.S still lacks the competence to enforce or algorithmic accountability laws or some form of protections from data biases, allowing private companies, usually buddied up with political parties, to exert unregulated influence over democratic processes. 

The UK presents a more complicated case, even though politics in the UK is still very much data driven. Algorithmic bias in the UK extends beyond the U.S’s case of campaigning, with the prime example being the 2020 A-level grading controversy, which revealed how state algorithms can reproduce structural inequality. Ofqual’s grade estimations, due to the emergence of COVID 19, had significantly negatively impacted students who hadn’t been initially performing well in their studies and state school students in general. This whole process had undermined the representative fairness of the education system. UK algorithmic frameworks remain inadequate compared to frameworks emerging in the EU, which rely on ethical principles. It can be understood that Britain is more aware of algorithmic bias than most, however it is still struggling to control its issues throughout society. 

India acts as a classic extreme, in which the political and social consequences of algorithmic bias affect the population to a severe degree. India has created ‘Aadhaar’, a biometric identification system which emphasizes voting access for the entire voting population- Yet, errors in data collection and algorithmic matching constantly exclude marginalised groups, particularly among rural, low-income populations. More complex, automated welfare systems have wrongly denied benefits to those most vulnerable, whilst digital verification failures have prevented citizens from voting in the first place, acting as a huge infringement on democratic processes. In a society which is already marked by deep rooted inequalities in the context of religion, class and ethnicity, algorithmic systems have entrenched, rather than eliminated, marginalisation. Weak regulation, limited amounts of transparency and dependence on automated systems has evidently created a new form of digital disenfranchisement - where technical error leads to political turmoil. 

In contrast, the EU embodies a global benchmark for fair algorithmic processes, maintaining a minimal level of unintended bias. The General Data Protection Regulation (GDPR), introduced in 2018, gives individuals rights to information about automated decisions. Although enforcing different forms of algorithmic processes will differ across the varying states, the EU’s approach reflects a very honest and clear commitment to bipartisanship in democracy by recognising algorithms as political instruments that must be governed in the public interest, or else they will be exploited by corruption. 

Taken together, these 4 examples illustrate that algorithmic bias is a variable topic, depending on which country uses it and how states choose to govern technology. In contexts like the U.S or India, regulation is weak, inefficient and private/ state sectors are able to abuse the unchecked powers or algorithmic bias, which reinforce patterns of exclusion. In the UK, there has been increased awareness on the issue, however there are still improvements to be made. By contrast, the EU’s states have begun to assert control over this issue, showing that bias can be eliminated through clear legal frameworks and transparency. 

Thus, Democracies that prioritise bipartisanship and accountability can design systems that can raise levels of political representation, rather than constrain it. Those that undermine the influence of technology and data risk allowing inequality to spread and corrupt democratic institutions at uncontrollable rates.