Recalibrating merit in the age of Artificial Intelligence

There needs to be a sophisticated understanding of the interplay between technology and societal structures

February 19, 2024 12:48 am | Updated 12:48 am IST

‘The advent of AI challenges the traditional notion of individual merit by prioritising access to technology’

‘The advent of AI challenges the traditional notion of individual merit by prioritising access to technology’ | Photo Credit: Getty Images

The concept of meritocracy, wherein individuals are rewarded and advance based on their abilities, achievements and hard work, rather than their social status or background, has been extensively debated. Proponents and critics of meritocracy offer compelling arguments about its impacts on society, highlighting its virtues and shortcomings. The evolution of meritocracy has witnessed significant transformations, influenced by the critiques and analyses of thinkers such as Michael Young, Michael Sandel, and Adrian Wooldridge.

Varied views

Young, a British sociologist, foresaw a dystopian meritocratic world in his satirical book, The Rise of the Meritocracy (1958). He envisioned a future, specifically 2034, as a society where social class and mobility were determined solely by intelligence and effort, as measured through standardised testing and educational achievement. It was a critique of the then-emerging trend towards a merit-based system, which he feared would lead to a new form of social stratification.

Sandel’s critique focuses on the divisive consequences, arguing that meritocracy fosters a sense of entitlement among the successful and resentment among those left behind, thereby eroding social cohesion. Critical theorists, including those from the Frankfurt School, also argue on similar lines by critiquing meritocracy for masking deeper power dynamics and inequalities. They say that meritocracy can perpetuate social hierarchies by legitimising the status of the elite under the guise of fairness and neutrality.

Post-structuralists challenge the notion of merit, questioning who defines merit and how it is measured. They argue that concepts of merit are socially constructed and reflect the biases and interests of those in power. Post-structuralism highlights the fluidity and contingency of merit, suggesting that meritocratic systems are inherently subjective and can reinforce existing inequalities.

In contrast to Young’s dystopian vision of meritocracy leading to a rigid class system and Sandel’s emphasis on its moral and social repercussions, Wooldridge lays stress on the practical evolution of meritocracy and its potential for reform. In his book, The Aristocracy of Talent, he explores how meritocracy, initially a force for progress and social mobility, has inadvertently fostered new inequalities by becoming somewhat hereditary, with privileges being passed down generations. Despite recognising the potential for meritocracy to create a new elite, Wooldridge believes in its intuitive fairness and proposes reforms that include making selective schools as “escalators into the elite” while improving access for underprivileged students and advocating better technical education.

AI as a disruptive factor

However, introducing Artificial Intelligence (AI) into this equation completely complicates the idea of reforming meritocracy. AI, with its rapidly evolving capabilities, will be reshaping merit and the idea of meritocracy in six ways.

First, by its very nature, AI questions the basis of human merit by introducing a non-human entity capable of performing tasks, making decisions, and even ‘creating’ at levels that can surpass human abilities. If machines perform the majority of tasks previously deemed as requiring human intelligence and creativity, the traditional metrics of merit become less relevant. OpenAI’s Sora is evidence that creativity is not an exclusive human trait any more.

Second, the advent of AI challenges the traditional notion of individual merit by prioritising access to technology. Individuals with access to AI tools gain a significant advantage, not necessarily due to their personal abilities, but because of the enhanced capabilities of these tools.

Third, AI systems trained on historical data can perpetuate and even exacerbate biases present in that data, leading to discriminatory outcomes in areas such as hiring, law enforcement, and lending. These biases can disadvantage groups which are already marginalised.

Fourth, a recent paper published in Nature Medicine showed that an AI tool can predict pancreatic cancer in a patient three years before radiologists can make the diagnosis. Capabilities such as this can lead to the displacement of jobs that involve routine, predictable tasks. This also means that AI would impact high-wage jobs.

Regardless of these, AI would push the workforce towards either high-skill, high-wage jobs involving complex problem-solving and creativity or low-skill, low-wage jobs requiring physical presence and personal interaction, which AI cannot replicate yet. This polarisation will exacerbate socioeconomic disparities, as individuals without access to high-level education and training are pushed towards lower-wage roles.

Fifth, the opaque nature of many AI algorithms, coupled with the concentration of power in a few tech giants, poses significant challenges to accountability. In a meritocratic society, individuals must understand the criteria by which their efforts and talents are evaluated. However, the ‘black box’ nature of many AI systems can obscure these criteria, making it difficult for individuals to know how to advance or challenge decisions made by AI, thus eroding the meritocratic ideal.

Sixth, at the organisational level, the core of AI’s power lies in data and algorithms that process this data. Tech giants with access to unprecedented volumes of data have a distinct advantage in training more sophisticated and accurate AI models. This data hegemony means that these entities can set the standards for what constitutes ‘merit’ in the digital age, potentially sidelining smaller players who may have innovative ideas but need access to similar datasets.

Thus, recalibrating meritocracy in the face of AI advancements demands a sophisticated understanding of the interplay between technology and societal structures. It calls for a deliberate rethinking of how merit is defined and rewarded when AI tools can both augment human capabilities and deepen existing inequalities.

Aditya Sinha is Officer on Special Duty, Research, Economic Advisory Council to the Prime Minister. The views expressed are personal. X: @adityasinha004

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