Seeing is Believing? The Ethics of AI Art and Surveillance

by

Author: Denis Avetisyan

This review examines how computer vision techniques are being used in tactical AI art, and whether these works effectively address the broader societal implications of increasingly pervasive surveillance technologies.

A critical analysis of the intersection of computer vision, artificial intelligence, and art, focusing on algorithmic bias and the limitations of current ethical frameworks.

While artificial intelligence increasingly mediates our perception of reality, the critical potential of artworks engaging with these systems remains underexplored. This paper, ‘Computer Vision in Tactical AI Art’, analyzes how artistic practices critically address the implications of computer vision technologies-from biometric classification to algorithmic bias-within the broader context of surveillance and AI ethics. It argues that while tactical AI art effectively reveals the sociopolitical challenges of automated inference, its impact is constrained by underlying conceptual frameworks and a need for more robust ethical and political engagement. How can artists move beyond critique to foster genuinely transformative interventions within the rapidly evolving landscape of artificial intelligence?

The Algorithmic Mirror: Reflecting Society’s Vision

The burgeoning intersection of contemporary art and Artificial Intelligence, specifically within Computer Vision, signals a profound mirroring of societal transformations. Artists are increasingly utilizing AI not merely as a tool, but as a medium to explore how we perceive and interpret the visual world, reflecting a culture saturated with algorithmic influence. This engagement manifests in diverse forms, from generative artworks created by AI models to installations that expose the ‘vision’ of machines. This artistic exploration isn’t simply about aesthetics; it acknowledges the increasing delegation of ‘seeing’ to algorithms in areas like surveillance, facial recognition, and autonomous systems, prompting critical reflection on the implications of this shift for human experience and agency. The artistic impulse to engage with AI’s visual processing, therefore, functions as a cultural barometer, revealing anxieties and opportunities inherent in a world where machines are learning to see – and interpret – on behalf of humanity.

The burgeoning integration of artificial intelligence into visual art isn’t a detached exploration of technology, but rather a potent reflection – and potential reinforcement – of existing power dynamics surrounding who and what is seen. As algorithms increasingly mediate visual information, questions of representation become paramount; the very act of ‘seeing’ is no longer a purely human endeavor, but one shaped by the parameters and priorities encoded within these systems. This raises concerns about control – who designs these algorithms, whose perspectives are prioritized in the training datasets, and ultimately, whose realities are rendered visible while others remain obscured. The shift isn’t simply about automating perception, but about fundamentally altering the processes through which meaning is constructed and disseminated, demanding critical examination of the biases and assumptions embedded within algorithmic seeing.

The allure of Artificial Intelligence in computer vision often centers on the notion of unbiased, ‘objective’ sight, yet this perception masks a fundamental truth: these systems are inherently shaped by the data they are trained on. Algorithms don’t perceive reality directly; instead, they identify patterns within datasets compiled by humans, reflecting existing societal biases and perspectives. Consequently, an AI trained on images predominantly featuring one demographic may struggle to accurately ‘see’ others, perpetuating and even amplifying existing inequalities. This isn’t a flaw in the technology itself, but rather a consequence of its reliance on subjective information, demonstrating that algorithmic seeing is never truly neutral – it is always a representation, filtered through the lens of its creators and the data they provide.

Data’s Shadow: Unveiling Algorithmic Bias

Large-scale image datasets, such as ImageNet, have become essential resources for training computer vision algorithms; however, analyses have revealed significant biases within these datasets. Specifically, studies have shown disproportionate representation of certain demographics and objects, and frequent association of particular groups with stereotypical labels. For example, images tagged with “CEO” predominantly feature men, while images associated with “housewife” largely depict women. These skewed representations are not random occurrences; they reflect existing societal biases present during the data collection and annotation processes. Consequently, machine learning models trained on these biased datasets can perpetuate and amplify these prejudices, leading to inaccurate or unfair outcomes in applications like facial recognition and object detection.

Algorithmic bias arises not from random errors in data or code, but from the systematic reproduction of societal biases within machine learning models. These biases are embedded during multiple stages of algorithm development, including data collection – where underrepresentation or misrepresentation of certain groups occurs – and feature engineering, where choices about what data points are prioritized can inadvertently reinforce prejudiced patterns. Consequently, algorithms trained on biased data will consistently produce outcomes that unfairly discriminate against or disadvantage specific demographic groups, impacting areas like facial recognition, loan applications, and criminal justice risk assessment. The effect is not simply inaccurate prediction, but the perpetuation and amplification of existing social inequalities through automated systems.

Datafication, the conversion of aspects of life into data points for analysis, inherently amplifies existing societal biases. This process relies on the selection and categorization of attributes deemed relevant for quantification, which are often defined by dominant groups and reflect pre-existing power structures. Consequently, data collection frequently underrepresents or misrepresents marginalized communities, leading to incomplete or skewed datasets. When these biased datasets are used to train algorithms, the resulting models perpetuate and even exacerbate inequalities by reinforcing limited representations and failing to accurately reflect the diversity of the population. This creates a feedback loop where biased data leads to biased algorithms, which further contribute to the underrepresentation and misrepresentation of certain groups in subsequent data collection efforts.

Exposing the Machine Gaze: Artistic Interventions

Tactical AI Art represents a contemporary evolution of earlier art forms focused on systems and power dynamics. Rooted in the traditions of Surveillance Art, which directly addressed the implications of monitoring technologies, and Tactical Media Art, which employed media as a tool for intervention, this new genre specifically addresses the limitations and inherent biases within algorithmic systems. These biases stem from factors such as biased training data, flawed algorithms, and the subjective choices made by developers, leading to outputs that can perpetuate or amplify existing societal inequalities. Tactical AI Art seeks to expose these flaws through creative practice, functioning as a direct engagement with the increasingly pervasive influence of algorithms in daily life and challenging their perceived objectivity.

Artists are employing the psychological phenomenon of pareidolia – the tendency to perceive meaningful patterns in random stimuli – as a technique to demonstrate the operational logic of machine vision systems. By creating inputs specifically designed to trigger pareidolic responses in algorithms, these interventions reveal how artificial intelligence can misinterpret data and identify false positives. This practice highlights that algorithmic ‘perception’ is not objective but a constructed interpretation based on training data and inherent biases, effectively demonstrating the artificiality of pattern recognition in both humans and machines and challenging the notion of algorithms as neutral observers.

Artistic interventions utilizing algorithmic critique operate as a form of applied critical inquiry, extending theoretical frameworks from Critical Theory into demonstrable practice. These projects move beyond purely aesthetic considerations to actively investigate the mechanisms of algorithmic perception and the power dynamics embedded within automated systems. By exposing the constructed nature of algorithmic ‘vision’ and highlighting potential biases, artists aim to deconstruct the perceived objectivity of these systems and encourage viewers to critically evaluate the authority increasingly delegated to algorithmic processes in areas ranging from surveillance to decision-making.

Beyond Critique: Reimagining Algorithmic Futures

The proliferation of biometric classification systems and facial recognition technologies necessitates careful scrutiny due to their inherent potential for misuse and the substantial threat they pose to personal privacy. These technologies, while often presented as tools for security and convenience, operate by collecting, analyzing, and categorizing sensitive biological data – effectively transforming uniquely human characteristics into quantifiable data points. This process introduces vulnerabilities to surveillance, misidentification, and discriminatory practices, particularly impacting marginalized communities already subject to heightened scrutiny. Concerns extend beyond simple data breaches; the very act of continuous biometric monitoring can chill free expression and assembly, fundamentally altering the dynamics of public space and eroding the expectation of anonymity. A robust critical assessment must therefore address not only the technical capabilities of these systems, but also the broader societal implications of normalizing their pervasive deployment.

The pervasive integration of corporate artificial intelligence, though frequently touted for gains in productivity and streamlined processes, often operates with a fundamental prioritization of financial return over broader societal impacts. This emphasis routinely manifests as the amplification of pre-existing inequalities; algorithmic systems designed to optimize profit can inadvertently, or even deliberately, disadvantage marginalized communities through biased loan applications, discriminatory pricing strategies, or inequitable access to essential services. These systems, optimized for efficiency rather than fairness, frequently lack transparency and accountability, making it difficult to identify and rectify these embedded biases, ultimately reinforcing systemic disadvantages under the guise of objective technological advancement. The pursuit of efficiency, unchecked by ethical considerations, risks solidifying a future where automated systems exacerbate, rather than alleviate, social disparities.

Tactical AI Art transcends simple criticism of artificial intelligence by actively demonstrating alternative technological possibilities. Rather than merely identifying biases embedded within algorithms – such as those perpetuating societal inequalities or reinforcing discriminatory practices – this approach builds working prototypes and artistic interventions that embody more equitable systems. These creations aren’t theoretical exercises; they are functional demonstrations of how automated inference could operate with fairness, transparency, and accountability at its core. By showcasing these alternatives, Tactical AI Art doesn’t just ask what’s wrong with current AI, but powerfully proposes what could be, fundamentally shifting the conversation toward proactive reimagining and responsible innovation in the field.

The pursuit of tactical AI art, as detailed in this analysis, often prioritizes revealing the mechanics of surveillance and biometric classification over addressing the underlying ethical quandaries. This echoes a sentiment articulated by Carl Friedrich Gauss: “If other people would think differently about things, they would think differently.” The work presented attempts to force a re-evaluation of how computer vision impacts society, yet its efficacy is constrained by conceptual limitations. The study rightly identifies a need for greater political and ethical grounding; merely showing the problem isn’t sufficient. true progress necessitates a shift in perspective, a rigorous examination of assumptions, and a willingness to dismantle flawed frameworks – a principle Gauss understood implicitly regarding the nature of thought itself.

Where Do We Go From Here?

The exploration of computer vision’s role within so-called ‘tactical AI art’ reveals less a pathway to genuine critique and more a reflection of existing anxieties. The work, as it stands, frequently circles the problem of algorithmic bias – stating the obvious, as it were – without meaningfully disrupting the systems that generate such bias. If the art merely shows the surveillance state, rather than interrogates its fundamental logic, it remains a symptom, not a solution. The field would benefit from discarding conceptual layers that obscure the core issue: the increasing automation of power.

A productive next step requires a deliberate stripping away of artistic pretension. The focus should not be on creating ‘beautiful’ or ‘provocative’ images, but on developing tools and methods for directly impacting the technologies themselves. This necessitates a deeper engagement with the technical underpinnings of computer vision – not as a subject for art, but as a domain of practice. The question is not whether the art is effective as art, but whether it alters the balance of power, even incrementally.

Ultimately, the limitations of this field are not artistic, but political. If the aim is to challenge surveillance, then the tools of resistance must be more robust, more direct, and less reliant on the very systems they critique. If such clarity is impossible, the entire endeavor remains a self-indulgent exercise in mapping symptoms, rather than treating the disease.

Original article: https://arxiv.org/pdf/2602.18189.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-02-23 12:37