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By:

Correspondent

23 August 2024 at 4:29:04 pm

Algorithmic Anarchy

The NEET paper leak protests at Delhi’s Jantar Mantar should have remained a legitimate expression of student anger over the collapse of examination integrity. Instead, as the agitation escalated into clashes with the police, abusive sloganeering and an increasingly radical political campaign, Meta’s algorithms emerged as an invisible force multiplying the unrest. The controversy surrounding the brief removal of Prime Minister Narendra Modi’s message to the youth in the aftermath of the...

Algorithmic Anarchy

The NEET paper leak protests at Delhi’s Jantar Mantar should have remained a legitimate expression of student anger over the collapse of examination integrity. Instead, as the agitation escalated into clashes with the police, abusive sloganeering and an increasingly radical political campaign, Meta’s algorithms emerged as an invisible force multiplying the unrest. The controversy surrounding the brief removal of Prime Minister Narendra Modi’s message to the youth in the aftermath of the protests only reinforces that concern. Meta has attributed the takedown to a “technical glitch” and apologised. But when the world’s largest social media platform can temporarily suppress the message of the elected leader of the world’s largest democracy during a politically charged moments, the issue extends well beyond a single deleted post. Who decides what India sees? That question has become impossible to ignore during the Cockroach Janta Party’s protests. Across Instagram and Facebook, users have reported being inundated with CJP videos, reels and protest clips despite never following the organisation or engaging with similar political content. Whether this resulted from coordinated paid collaborations, recommendation algorithms or both deserves a thorough investigation. But the larger democratic concern is that public opinion is increasingly being mediated not by citizens, journalists or elected representatives, but by opaque algorithms designed in Silicon Valley and optimised for engagement rather than democratic responsibility. Meta’s recommendation engine is not a passive notice board. Every piece of political content that reaches millions has first been selected by an algorithm whose workings remain largely hidden from public scrutiny. This is hardly unique to India. Around the world, Meta has repeatedly been accused of amplifying polarisation, misinformation and political extremism because outrage keeps users engaged. From elections to ethnic conflicts and episodes of civil unrest, the company has faced persistent criticism that its commercial incentives reward divisive content over balanced discourse. Democracies cannot afford to outsource the architecture of public debate to corporations whose primary obligation is to shareholders rather than constitutional values. While citizens are entitled to challenge governments and demand accountability, there is an important distinction between a movement that expands because people are persuaded and one that appears to be algorithmically amplified into omnipresence. Equally disturbing has been the normalisation of abusive political language during the protests. When such content is repeatedly amplified through recommendation systems, platforms cease to be neutral intermediaries and become active participants in degrading democratic discourse. Platforms that influence elections, protests and public opinion must explain how political recommendations are generated and why particular narratives receive extraordinary amplification. Silicon Valley companies insist they are merely technology platforms. But their algorithms increasingly exercise editorial power. When software determines which protests dominate national conversations and whose voices disappear, technology has already become politics.

Machines and Money Managers

Will investors one day entrust their life savings to a machine? Will portfolio decisions quietly migrate from seasoned minds to silent computation? These questions, once relegated to speculative discourse, now demand serious consideration. Artificial intelligence has embedded itself into the architecture of financial markets, reading balance sheets in seconds, analyse earnings calls, tracking global liquidity, and executing trades with extraordinary speed. What appeared futuristic a decade ago now functions as everyday infrastructure. Asset management stands at an inflection point.


When it comes to processing information, machines resemble tireless archivists. They scan thousands of securities, identify patterns across decades, and rebalance portfolios without fatigue or sentiment. They neither panic during corrections nor succumb to euphoria in rallies. This dispassionate consistency constitutes their principal strength.


Quantitative investing, where mathematical models guide decisions, has operated for years. Firms such as Renaissance Technologies built empires on algorithmic foundations. Institutions like BlackRock deploy systems such as Aladdin to monitor risk across global portfolios. Robo-advisors including Betterment and Wealthfront construct and manage portfolios automatically, allocating assets, rebalancing holdings, and minimising costs.


In India, numerous platforms offer automated portfolio recommendations. These function like autopilot systems in aviation, operating smoothly under normal conditions. Yet even in commercial flight, autopilot does not eliminate the pilot.


Human Edge

Beyond quantitative analysis, investing demands interpretation of nuances that data cannot capture. A skilled fund manager often perceives what spreadsheets obscure. A hesitation during a management call, an overconfident assertion, or a tonal shift may reveal more than any financial statement. AI depends on data, and that data originates from human sources. Financial disclosures and forecasts may carry bias or error. Weak inputs inevitably produce weak outputs.


Markets are also propelled by emotion. Fear and greed act as invisible forces moving prices in ways that defy rational expectation. During crises, investors may liquidate holdings even when logic counsel patience. In booms, they may purchase without prudence. Behavioural finance has documented these tendencies extensively. AI can detect statistical patterns, but comprehending human emotion resembles forecasting weather from cloud formations alone. It remains incomplete.


Beyond returns lies a more fundamental consideration: TRUST. Investors do not merely allocate capital to funds; they invest in the individuals who oversee those funds. During turbulent periods, they seek explanation, reassurance, and accountability. A machine may deliver performance, but can it offer comfort? In markets like India, relationships carry weight. Investors draw confidence from knowing who manages their capital. The fund manager's name becomes akin to a captain navigating uncertain seas.


This raises an additional concern. If an algorithm errs, who bears responsibility? The developer? The institution? The system itself? The “disclaimer” clause?   Without the clear answers, complete trust in autonomous systems may take considerable time to develop.


Shared Future

Across global markets, automation is proliferating, yet a fully AI-driven mutual fund operating without human oversight remains rare. Most funds employ AI as an instrument, not a substitute. Certain exchange-traded funds utilise rules-based or algorithmic strategies, following fixed models rather than human discretion. Even then, humans design, monitor, and refine these models. Hedge funds such as Two Sigma Investments rely heavily on data science, yet they maintain teams of experts to supervise their systems. The notion of a completely autonomous, self-directing mutual fund remains more concept than reality.


As more participants adopt similar tools, another question emerges. If everyone deploys comparable algorithms, will generating superior returns become increasingly difficult?


The future appears less like a contest and more like a collaboration. Consider a modern cockpit. The pilot commands numerous instruments, alerts, and automated systems. These tools inform decisions, but the final determination rests with the human. Fund managers are moving in the same direction. AI helps them analyse rapidly, model scenarios, and manage risk. But when unforeseen events occur, human judgment becomes indispensable.


Regulators such as the Securities and Exchange Board of India (SEBI) will likely demand transparency regarding how investment decisions are made, whether by humans or machines. The pertinent question is not whether AI will replace fund managers, but whether fund managers who disregard AI will fall behind. History demonstrates that technology transforms roles rather than eliminates them.


The fund manager's role is evolving. Yesterday's manager concentrated on research and allocation. Today's manager integrates analysis with technology. Tomorrow's manager may function as a conductor of an orchestra, guiding intelligent systems while ensuring harmony. AI will continue to deliver speed, scale, and efficiency. But markets will always reflect human behaviour. As long as uncertainty, emotion and narrative influence decisions, human judgment will remain essential.


(The writer is a retired banker and author. Views personal.)

 


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