The Pied Piper of Big Tech AI

Ethics in AI: The Invisible Pillar in a Tech-Driven World

TL;DR - I advocate for enhanced oversight and rigorous ethical frameworks that hold systems accountable to human-centric values rather than unchecked corporate gain. The balance between technological advancement and societal welfare demands that ethical guidelines be more than a theoretical framework—they must be enforced pillars of responsible AI practice to safeguard human dignity and agency. I will stand at the AI Ethics post, come what may, as unpopular as it is.

The Illusion of AI Ethics in Practice While ethics in AI draws attention in discussions, practical enforcement is minimal. It is often simply a theoretical exercise rather than a reality. Despite a growing number of frameworks, proper accountability is absent, as evidenced by insufficient global regulatory oversight and tech companies’ own selective compliance. My experience underscores how frequently ethics is discussed yet how infrequently meaningful action or monitoring follows.

Enforcement Deficit in an AI-Driven World Even where ethical frameworks are nominally implemented, more robust oversight mechanisms are needed to make any progress, progress that is not just superficial word salad. Regulatory bodies are still catching up to the technical complexities of AI, which allows influential entities to sidestep meaningful compliance. Research such as “The AI Ethics Paper Trail” (Hagendorff, 2020, https://link.springer.com/article/10.1007/s11023-020-09517-8) shows 88% of companies with AI ethics programs lack independent review mechanisms, underscoring my view that there is a gigantic governance void.

A Sisyphean Task Against Systemic Pressures Promoting genuine AI ethics has felt like pushing a boulder uphill, as profit motives undermine ethical concerns. With increased profits and/or influence, companies and governments alike deprioritize ethics, sidelining individual rights and amplifying AI's potential for mood and sentiment manipulation. This echoes the challenges highlighted by Binns (2018) Fairness in Machine Learning: Lessons from Political Philosophy https://proceedings.mlr.press/v81/binns18a.html in aligning machine learning practices with ethical frameworks when powerful interests exert pressure to do otherwise.

The Erosion of Privacy and Rights for Convenience Widespread data collection has normalized a culture of complacency, where individuals consent to invasive surveillance for convenience; they do not fully understand or care enough or are unable to have the luxury to grasp the issues that are being caused by AI in their lives. After all, it is a non-trivial matter to understand, and most are not bothered to try. The willingness to trade privacy for ease, documented in the work of Zuboff (2019), Digital Literacy and AI https://infoguides.pepperdine.edu/digitalliteracy/surveillancecapitalism, shows fertile ground for AI-driven exploitation and psychological manipulation, from targeted ads to predictive policing—systems that shape public opinion while eroding personal agency.

Manipulating Moods and Societal Norms AI’s ability to influence sentiment has introduced new ethical quandaries that almost all regulations or guidelines fail to address. The multiplier effect of the concept of AI systems intersectionality is ignored. The increasing use of AI to subtly guide thoughts, purchases, and beliefs reflects a reality that should concern policymakers more than it currently does. Datasets designed to shape perceptions challenge cognitive autonomy with little counterbalance regarding ethical enforcement or public recourse.

A Call for Rigorous Oversight and Transparency I advocate for enhanced oversight and rigorous ethical frameworks that hold systems accountable to human-centric values rather than unchecked corporate gain. The balance between technological advancement and societal welfare demands that ethical guidelines be more than a theoretical framework—they must be enforced pillars of responsible AI practice to safeguard human dignity and agency. I will stand that post, come what may, as unpopular as it is.