Humans in the Loop(2024)
- ARANYA SAHAY
This blog is part of an academic task assigned by Dr. Dilip Baradsir for to guide structured engagement with Humans in the Loop (2024) directed by Aranya Sahay. The assignment is designed to support critical inquiry before, during, and after viewing the film through pre-study research, guided observation points, and post-screening reflection. It encourages us to analyze the film’s exploration of AI, digital culture, invisible labour, knowledge systems, bias, identity, and socio-cultural representation using key concepts from film theory such as mise-en-scène, cinematography, editing, sound, ideology, and power relations. By integrating theoretical lenses from Film Studies, the task aims to deepen our understanding of how cinema not only represents technological systems but also critiques the cultural and political structures embedded within them.
PRE-VIEWING TASK:
• AI Bias & Indigenous Knowledge Systems: What do you understand by AI bias? How might indigenous ecological knowledge challenge technological framings?
AI Bias & Indigenous Knowledge Systems:
AI bias refers to systematic errors in artificial intelligence systems that produce unfair, skewed, or discriminatory outcomes due to biased training data, flawed categorization, or unequal representation.
AI systems learn from human-labelled data; if the input reflects dominant cultural assumptions, the output reproduces those hierarchies.
Bias can appear in facial recognition, language processing, hiring algorithms, or ecological classification systems.
Indigenous knowledge systems are holistic, relational, and context-based rather than rigidly categorical.
Indigenous ecological knowledge often understands forests, rivers, animals, and land as interconnected living entities, not merely “resources.”
AI systems, however, often reduce complex realities into fixed tags, datasets, and binary categories.
This creates tension when lived, experiential knowledge does not fit predefined algorithmic labels.
Indigenous epistemologies may challenge technological framings by:
- Questioning extraction-based data practices
- Resisting universal classification systems
- Offering relational models of knowledge rather than hierarchical ones
The clash reveals how AI is not neutral but embedded in power structures and cultural assumptions.
• Labour & Digital Economies: What is invisible labour in digital economies? Why is it significant to highlight such labour in narratives about AI?
Labour & Digital Economies
Invisible labour in digital economies refers to work that is essential but undervalued, hidden, or unacknowledged.
In AI systems, this includes data labelling, content moderation, annotation, tagging, and cleaning datasets.
Although AI is often portrayed as autonomous and intelligent, it relies heavily on human input.
Workers in the Global South frequently perform repetitive micro-tasks for low wages.
This labour remains “invisible” because:
- It is outsourced and geographically distant
- It is fragmented into small digital tasks
- Public narratives celebrate innovation rather than labour
Highlighting such labour disrupts the myth of fully automated AI.
It exposes how AI depends on human cognition, emotional labour, and cultural interpretation.
It also reveals economic inequalities embedded in global tech industries.
Narratives about AI often glorify engineers and corporations while ignoring workers who train the system.
Making invisible labour visible reframes AI as a socio-economic system rather than a purely technological achievement.
• Politics of Representation: From film publicity and reviews, how does representation both of technology and of Adivasi culture operate in the film?
Politics of Representation
Representation concerns who gets depicted, how they are depicted, and who controls that depiction.
Film publicity and reviews suggest the film foregrounds an Adivasi woman’s perspective within the AI industry.
Technology is not represented as neutral or futuristic, but as culturally embedded and politically charged.
AI appears as a system shaped by dominant knowledge frameworks.
Adivasi culture is portrayed not as “backward” but as epistemologically rich and intellectually valid.
The film challenges stereotypes by positioning indigenous knowledge as critical rather than primitive.
Reviews highlight themes of cultural misrecognition where algorithmic systems fail to interpret indigenous realities.
Representation operates at two levels:
- Within the narrative (how AI categorizes knowledge)
- Through cinematic framing (whose story is centered)
By placing an Adivasi woman at the heart of technological critique, the film rebalances power in storytelling.
It questions who defines knowledge, who labels reality, and whose worldview becomes “data.”
1. NARRATIVE & STORYTELLING:
• How does the film situate Nehma’s personal life with larger algorithmic structures? What narrative turns foreground labour, family, and knowledge systems?
The film Humans in the Loop (2024) by Aranya Sahay situates Nehma’s personal life in direct conversation with global algorithmic systems. Rather than presenting AI as an abstract technological force, the narrative embeds it within her everyday realities her home, family responsibilities, and community life. This structural choice collapses the distance between the “local” and the “global,” showing how multinational AI infrastructures depend on intimate, often precarious, domestic spaces.
One major narrative turn occurs when Nehma enters the data-labelling workforce. This shift foregrounds digital labour as a survival strategy while simultaneously revealing its exploitative dimensions. The repetitive act of categorizing images contrasts sharply with her lived ecological knowledge as an Adivasi woman. Scenes of family life and village interactions highlight relational knowledge systems, which stand in tension with the rigid, reductionist logic of algorithmic classification.
Through this juxtaposition, the film foregrounds three interconnected elements:
- Labour as cognitive and emotional extraction
- Family as the socio-economic context enabling participation in digital work
- Knowledge systems as sites of conflict between indigenous epistemologies and technological categorization
Thus, Nehma’s personal story becomes a lens through which larger algorithmic power structures are critically examined.
• When Nehma “teaches” AI, what does this suggest about human-machine learning loops beyond technological jargon?
When Nehma “teaches” AI by labelling and categorizing data, the film demystifies machine learning. It reveals that AI systems do not autonomously generate intelligence; rather, they rely fundamentally on human judgement, interpretation, and cultural understanding. The term “loop” becomes significant because it suggests a continuous cycle of mutual shaping.
On one level, humans train machines by providing data and classifications. On another level, machines reshape human perception by imposing predefined categories that influence how reality is interpreted. This loop is not neutral it carries embedded cultural biases and hierarchies. Nehma’s role exposes the irony that marginalized workers contribute essential knowledge to systems that may not recognize or value their epistemologies.
Beyond technical jargon, the human–machine loop suggests:
- Dependency: AI depends entirely on human cognitive labour
- Asymmetry: Workers teach systems but lack control over their outcomes
- Ethical entanglement: Bias, power, and responsibility circulate within the loop
Ultimately, the act of “teaching AI” becomes a metaphor for broader questions about knowledge production, authority, and the politics of technological modernity.
2. REPRESENTATION & CULTURAL CONTEXT:
• How are Adivasi culture, language, tradition, and ecological knowledge represented?
In Humans in the Loop (2024) directed by Aranya Sahay, Adivasi culture is represented with depth, dignity, and epistemic legitimacy rather than as a folkloric or marginal backdrop. The film foregrounds everyday practices language, familial interactions, ecological engagement to present culture as lived knowledge rather than exotic spectacle.
Language operates as a marker of identity and worldview. Indigenous speech patterns and expressions signal a relational understanding of land and community. Tradition is not portrayed as static or regressive; instead, it is shown as adaptive and intellectually rich. The film emphasizes ecological knowledge understanding forests, land, and non-human life as interconnected systems. This contrasts sharply with the AI system’s need for rigid labels and simplified categories.
Ecological knowledge is represented as experiential and context-sensitive. It resists reduction into binary data classifications. By placing indigenous epistemology alongside algorithmic logic, the film frames Adivasi knowledge not as inferior but as an alternative, sophisticated way of understanding the world. Thus, representation becomes political: it asserts that indigenous knowledge systems hold value within technological modernity.
• Does the film challenge or reinforce dominant media stereotypes about tribal communities and modern technology?
The film strongly challenges dominant media stereotypes. Tribal communities in mainstream narratives are often portrayed as technologically backward, isolated, or resistant to progress. In contrast, the protagonist’s role in AI data labelling disrupts this binary between “primitive” and “modern.”
By situating an Adivasi woman within the global AI economy, the film rejects the assumption that technological spaces belong exclusively to urban, elite actors. However, it does not romanticize inclusion. Instead, it reveals how participation in modern technology often occurs under unequal and exploitative conditions.
The narrative challenges stereotypes in several ways:
- It presents Adivasi identity as dynamic rather than frozen in tradition.
- It portrays indigenous knowledge as intellectually rigorous.
- It exposes the myth of AI as fully autonomous by highlighting human labour.
- It complicates the idea of “development” by showing its social costs.
Rather than reinforcing simplistic contrasts between tradition and technology, the film interrogates the power structures that define both. It ultimately reframes modern technology as culturally embedded and politically shaped, not universally neutral.
3. CINEMATIC STYLE & MEANING
• Lines of mise-en-scène and cinematography how are the forest, computer
screens, workspace, and rituals framed visually?
In Humans in the Loop (2024) directed by Aranya Sahay, mise-en-scène and cinematography construct a powerful visual contrast between ecological life and digital labour.
The forest is framed through natural lighting, open compositions, and layered depth of field. The setting emphasizes organic textures trees, soil, wind, and movement creating a sense of relational space. Actor placement within the landscape often suggests harmony rather than domination. The mise-en-scène here foregrounds interconnectedness and lived ecological knowledge.
In contrast, computer screens and workspace environments are framed with tighter compositions and restricted spatial depth. The mise-en-scène includes artificial lighting, static props (desks, monitors, cables), and confined interiors. This visual compression conveys rigidity and surveillance-like order. The framing of screens within screens creates a meta-visual structure, emphasizing mediation and technological filtering.
Rituals and community gatherings are often composed with balanced framing and collective positioning, reinforcing shared identity and cultural continuity. Costume, gesture, and spatial arrangement contribute to a visual language of tradition and communal belonging.
Thus, through cinematography camera angles, framing, and depth the film visually encodes the tension between organic, analog environments and structured digital systems.
• How do sound design and editing rhythms contribute to the contrast between analog life and digital labour?
(Mise-en-scène, cinematography, editing and sound are core film language elements that shape narrative and meaning in films.)
Sound design plays a crucial narrative role in establishing contrast. In forest and domestic scenes, diegetic sounds birds, wind, footsteps, conversation create an immersive sensory atmosphere. Natural soundscapes function as auditory mise-en-scène, reinforcing presence and continuity.
By contrast, digital workspace scenes foreground mechanical sounds: keyboard clicks, notification tones, electronic hums. These sounds are repetitive and rhythmic, mirroring the monotony of data-labelling labour.
Editing rhythm further intensifies this contrast. Scenes of community life may employ longer takes and slower pacing, allowing viewers to dwell in lived experience. Digital labour sequences tend toward sharper cuts and repetitive visual patterns, emphasizing routine and fragmentation.
Through rhythm and juxtaposition recalling principles of montage theory the film generates meaning not just within shots but between them. The contrast in pacing creates a cognitive and emotional divide between ecological time (cyclical, continuous) and algorithmic time (measured, segmented, task-driven).
Together, mise-en-scène, cinematography, editing, and sound form a coherent film language that transforms thematic tension into sensory experience.
4. ETHICAL & POLITICAL QUESTIONS:
• What ethical dilemmas are depicted when training AI with culturally specific data?
In Humans in the Loop (2024) directed by Aranya Sahay, the process of training AI with culturally specific data reveals several layered ethical dilemmas. At the core is the problem of representation and reduction. Indigenous ecological knowledge, which is relational, contextual, and orally transmitted, must be translated into rigid, predefined categories that often fail to capture its depth. This raises the ethical question of whether complex cultural knowledge can or should be compressed into algorithmic labels.
Another dilemma concerns consent and ownership. When culturally embedded knowledge is used to train AI systems, who owns that knowledge? Are communities adequately informed or compensated for the extraction of their lived experiences? The film subtly highlights how global AI infrastructures may benefit from localized knowledge without equitable recognition.
There is also the issue of bias and distortion. If AI systems are trained on partial or misinterpreted cultural data, they risk reproducing stereotypes or inaccuracies. Thus, the ethical challenge lies not only in technical accuracy but in safeguarding epistemic integrity and cultural dignity.
• How does the film’s human-in-the-loop metaphor operate beyond the technical term politically, socially, and culturally?
Technically, “human-in-the-loop” refers to systems where human input remains essential for machine learning. However, in the film, the metaphor operates on broader political and social levels.
Politically, the loop exposes global inequalities. Marginalized workers provide cognitive labour that sustains powerful technological systems, yet they remain excluded from decision-making power. The loop therefore symbolizes asymmetrical participation in the digital economy.
Socially, it reveals interdependence masked as automation. AI is often marketed as autonomous, but the film shows that human judgement remains central. This challenges dominant narratives of technological inevitability and neutrality.
Culturally, the metaphor suggests a cycle of translation and transformation. Humans teach machines using culturally situated knowledge, but machines then reshape how knowledge is categorized and valued. This creates a feedback loop in which cultural meaning is continuously negotiated, filtered, and sometimes distorted.
Ultimately, the “human-in-the-loop” metaphor expands into a critique of technological modernity itself. It foregrounds questions of power, labour, representation, and responsibility, showing that AI systems are not merely technical tools but socially embedded structures shaped by human choices and hierarchies.
POST-VIEWING REFLECTIVE ESSAY TASKS:
TASK 1 — AI, BIAS, & EPISTEMIC REPRESENTATION:
• How does the narrative expose algorithmic bias as culturally situated rather than purely technical?
• In what ways does the film highlight epistemic hierarchies that is, whose knowledge counts in technological systems?
Support your answer with film examples and relevant scholarly concepts such as representation, ideology, and power relations from film studies.
AI, Bias, and Epistemic Representation in Humans in the Loop (2024):
In Humans in the Loop (2024), directed by Aranya Sahay, artificial intelligence is not portrayed as a futuristic abstraction but as a system deeply entangled with human labour, cultural knowledge, and power hierarchies. Through the story of Nehma, an Adivasi woman from Jharkhand engaged in AI data labelling, the film interrogates the presumed neutrality of machine learning systems. Rather than presenting algorithmic bias as a technical glitch, the narrative reveals it as culturally situated and structurally embedded. By foregrounding indigenous epistemology alongside digital labour, the film exposes epistemic hierarchies demonstrating that technological systems privilege certain forms of knowledge while marginalizing others. Using concepts from film studies such as representation, ideology, power relations, and Apparatus Theory, this essay critically examines how the film reframes AI as a political and cultural formation rather than a neutral tool.
Algorithmic Bias as Culturally Situated:
One of the central interventions of the film is its critique of algorithmic bias. AI systems are commonly understood as mathematical and objective; bias is often attributed to faulty data or imperfect coding. However, the film challenges this perception by showing that bias originates in the social and cultural assumptions embedded within datasets and classification systems.
Nehma’s role as a data labeler demonstrates that machine learning depends entirely on human categorization. The repetitive act of tagging images and organizing information into predefined categories exposes how AI systems require simplification. Indigenous ecological knowledge rooted in relational, experiential, and contextual understanding must be compressed into rigid algorithmic frameworks. In this translation, complexity is lost. The system demands binary distinctions and standardized labels, whereas Nehma’s lived knowledge of forests, land, and community resists such reduction.
The narrative therefore situates algorithmic bias within broader structures of ideology. Bias is not merely a technical flaw; it reflects dominant epistemologies. The categories available to Nehma are not culturally neutral they reflect the worldview of those who designed the system. By visually juxtaposing scenes of forest life with the sterile digital workspace, the film emphasizes that AI systems operate within ideological constraints that privilege extractive, classificatory knowledge over relational ways of knowing.
From a film theory perspective, this critique can be connected to the concept of representation. Representation is not simply depiction but the construction of meaning through selection and framing. Just as cinema frames reality through mise-en-scène and editing, AI systems frame reality through data selection and categorization. The film thus parallels cinematic representation with algorithmic representation, suggesting that both are shaped by power structures.
Epistemic Hierarchies and the Politics of Knowledge:
The film also foregrounds epistemic hierarchies systems that determine whose knowledge counts as legitimate. Nehma’s indigenous knowledge is shown to be rich, embodied, and ecologically attuned. However, within the AI workspace, this knowledge must conform to pre-existing digital taxonomies. The system does not adapt to her epistemology; she must adapt to its logic.
This asymmetry reflects broader power relations in technological modernity. Knowledge produced in corporate or urban centers is institutionalized within technological systems, while indigenous knowledge is often treated as informal or anecdotal. Yet paradoxically, the AI system depends on Nehma’s cognitive labour to function. The film highlights this contradiction: marginalized workers sustain global technological infrastructures but remain excluded from their authority structures.
The concept of ideology, central to film studies, helps unpack this dynamic. Ideology operates by naturalizing dominant worldviews so that they appear universal and inevitable. AI is frequently marketed as objective and progressive, reinforcing the ideology of technological neutrality. However, the film disrupts this narrative by revealing the hidden human labour and cultural translation that underpin machine learning. In doing so, it exposes the ideological function of AI discourse itself.
This critique resonates with Apparatus Theory, which examines how cinematic technology structures spectatorship and reproduces ideology. Apparatus theorists argue that film form camera positioning, editing, narrative alignment conditions how viewers perceive reality, subtly reinforcing dominant perspectives. Similarly, AI systems function as technological apparatuses that structure perception. They filter, classify, and prioritize information, shaping how reality is interpreted. In Humans in the Loop, the digital interface becomes a visible apparatus: screens frame data just as cameras frame images. The user interface dictates what can be seen and how it can be categorized, mirroring how cinematic framing controls audience perception.
Thus, the technological apparatus within the narrative reflects the ideological apparatus of cinema itself. The film becomes self-reflexive: it critiques not only AI systems but also the broader structures through which knowledge is mediated.
Power Relations and the Human-in-the-Loop:
The metaphor of the “human-in-the-loop” extends beyond its technical meaning. While in computer science it refers to systems requiring human input, in the film it symbolizes a cycle of dependency and control. Humans train machines, yet machines reshape human cognition and labour conditions.
Politically, this loop reveals global inequalities. Nehma participates in a digital economy that extracts cognitive labour from marginalized communities. The benefits of AI development accrue elsewhere, while she remains in a precarious socio-economic position. The loop is therefore asymmetrical: participation does not equate to empowerment.
Culturally, the loop underscores a process of translation. Nehma must reinterpret her ecological knowledge in terms legible to the algorithm. This translation risks distortion, as nuanced understandings are forced into simplified categories. The loop thus becomes a site of epistemic negotiation, where knowledge is both transmitted and transformed.
Socially, the film dismantles the myth of automation. By emphasizing repetitive human input, it reveals that AI is sustained by invisible labour. This exposure challenges the ideology of self-sufficient technology and re-centers human agency even as it highlights the uneven distribution of power.
Through cinematic techniques contrasting mise-en-scène, sound design that shifts from organic ambience to mechanical repetition, and editing rhythms that juxtapose communal life with digital monotony the film materializes these power relations at the sensory level. The audience experiences the tension between relational time and algorithmic time, reinforcing the thematic critique.
Cinema as Critical Apparatus:
By applying Apparatus Theory, we can further interpret how the film itself intervenes in ideological structures. Cinema is not neutral; it shapes spectatorship. However, Humans in the Loop uses its apparatus critically. Instead of naturalizing technological power, it renders it visible and questionable. Close framings of computer screens, the repetition of labelling tasks, and the contrast with expansive forest imagery encourage viewers to reflect on how knowledge is mediated.
In doing so, the film aligns with critical traditions in film theory that emphasize cinema’s capacity to expose ideology rather than reproduce it. It positions AI not as destiny but as a socially constructed system embedded in power relations.
Conclusion:
Humans in the Loop reframes AI from a purely technical phenomenon to a cultural and political formation. By centering Nehma’s lived experience, the film exposes algorithmic bias as culturally situated, rooted in dominant epistemologies rather than computational error. It highlights epistemic hierarchies that privilege standardized, institutional knowledge while marginalizing indigenous ways of knowing. Through the lens of representation, ideology, power relations, and Apparatus Theory, the film reveals AI systems as technological apparatuses that mirror societal inequalities.
Ultimately, the film argues that technology is never neutral. It is shaped by the values, assumptions, and labour of those within and often beneath the system. By making visible the human within the loop, the film insists on accountability, recognition, and epistemic justice in the age of artificial intelligence.
TASK 2 — LABOR & THE POLITICS OF CINEMATIC VISIBILITY
• How does the film’s visual language represent labelling work and the emotional
experience of labour?
• What does this suggest about cultural valuation of marginalised work?
• Does the film invite empathy, critique, or transformation in how labour is perceived?
Labour and the Politics of Cinematic Visibility in Humans in the Loop (2024):
In Humans in the Loop (2024), directed by Aranya Sahay, labour becomes the central site through which artificial intelligence is demystified. While AI is often celebrated as autonomous and innovative, the film reveals the hidden human infrastructure sustaining it. Through the story of Nehma, an Adivasi woman engaged in data-labelling work, the narrative visualizes what digital capitalism prefers to keep invisible: repetitive cognitive labour performed by marginalized workers. By employing a deliberate visual language and drawing attention to emotional experience, the film critiques the commodification of human effort under global technological systems. Using Marxist and Cultural Film Theory alongside Representation and Identity Studies, this essay examines how the film renders invisible labour visible and redefines its cultural value.
Visualizing Invisible Labour:
Digital labour, particularly data labelling, is structurally hidden. It takes place in dispersed locations, mediated through screens, and fragmented into micro-tasks. The film counters this invisibility by foregrounding the physical and emotional dimensions of the work.
Through mise-en-scène, the workspace is depicted as confined and repetitive. Computer screens dominate the frame, often isolating Nehma within tight compositions. The artificial lighting contrasts sharply with the organic textures of her village environment. The screen becomes both a tool and a barrier framing her labour while simultaneously enclosing her within a system she does not control.
Cinematography emphasizes stillness and repetition. Static shots and prolonged takes mirror the monotony of labelling tasks. The editing rhythm reinforces this pattern: sequences of clicking, tagging, and scrolling create a sense of mechanical repetition. The viewer becomes acutely aware of duration labour is not abstract but experienced as time spent, attention drained, and concentration sustained.
Sound design further intensifies this effect. The repetitive tapping of keys and the faint hum of digital devices replace the layered soundscape of forest life. These auditory cues transform cognitive labour into a sensory experience. Rather than presenting AI as seamless automation, the film makes the human effort behind it audible and visible.
From a Marxist film theory perspective, this visualization exposes the material conditions underlying digital production. Labour is not erased but centered. The film reveals that technological commodities AI systems are produced through human cognitive work, echoing Marx’s argument that value emerges from labour power.
Emotional Experience and the Commodification of Attention:
The film does not restrict itself to economic critique; it also explores the emotional dimensions of digital labour. Nehma’s engagement with labelling tasks is not merely mechanical. It requires judgement, interpretation, and translation. Her indigenous ecological knowledge must be reformatted into standardized categories, often stripping away nuance.
This process suggests a deeper commodification not only of physical labour but of cognition and cultural knowledge. Under digital capitalism, attention and interpretation become marketable resources. The system extracts value from Nehma’s capacity to think, categorize, and understand context.
Marxist Cultural Film Theory emphasizes how cinema can reveal relations of production embedded within everyday life. Here, labour is not factory-based but cognitive and distributed. The means of production are digital interfaces controlled by distant institutions. Nehma participates in production but lacks ownership over the product or decision-making power within the system.
Emotionally, the repetitive rhythm of the work conveys fatigue and quiet resilience. The film avoids melodrama; instead, it presents labour as routine and necessary for survival. This subtle portrayal underscores how exploitation often operates through normalization rather than overt coercion. Labour becomes an accepted condition of participation in modernity.
Cultural Valuation of Marginalized Work:
One of the film’s most powerful interventions lies in its critique of cultural valuation. In dominant narratives, AI innovation is associated with engineers, entrepreneurs, and corporations. The intellectual labour of data workers remains unrecognized. By centering an Adivasi woman within this system, the film disrupts assumptions about who contributes to technological advancement.
Representation and Identity Studies provide a useful lens here. Identity and labour intersect in complex ways. Nehma’s social position gendered, indigenous, economically precarious shapes how her labour is perceived and valued. Although her work is essential to AI functionality, it remains culturally invisible.
The film challenges this hierarchy by making her labour the narrative focus. The camera lingers on her concentration, her gestures, and her environment. Through this sustained attention, the film reassigns value. It suggests that technological systems are collective achievements sustained by marginalized contributors.
At the same time, the film does not romanticize digital inclusion. Participation in the AI economy does not eliminate structural inequality. Instead, it reveals how digital capitalism incorporates marginalized workers into global supply chains while maintaining asymmetrical power relations. The cultural valuation of labour remains uneven: intellectual prestige is attributed to designers, while repetitive classification work is minimized.
This critique aligns with Marxist analysis of commodification. Human effort becomes abstracted into data, detached from the worker who produced it. The worker’s identity is secondary to productivity metrics. By restoring visibility to the worker, the film resists this abstraction.
Empathy, Critique, and Transformation:
A key question is whether the film invites empathy, critique, or transformation. In practice, it achieves all three, though with emphasis on critical awareness.
Empathy emerges through cinematic intimacy. Close framing and careful pacing allow the audience to inhabit Nehma’s routine. The viewer experiences the monotony and emotional weight of the labour. This humanization counters the myth of automation and invites recognition of the person behind the interface.
However, the film extends beyond empathy to systemic critique. By juxtaposing forest life with digital workspace environments, it contrasts relational knowledge systems with extractive digital economies. The editing structure encourages viewers to reflect on the structural conditions that make such labour necessary. The film does not isolate Nehma’s experience as individual hardship; it situates it within global economic systems.
Transformation occurs at the level of perception. After witnessing the labour behind AI, viewers are less likely to perceive technological systems as self-generated or neutral. The film reshapes how labour is imagined in the digital age. It insists that technological modernity is built on hidden forms of human effort.
In Marxist terms, the film performs ideological critique. It disrupts the “fetishism” of technology the tendency to treat commodities as independent of the labour that produces them. By reattaching AI systems to human faces and environments, the film demystifies digital capitalism.
Cinema as Political Visibility:
Cinema itself becomes a political tool of visibility. By framing data-labelling work as worthy of narrative focus, the film challenges dominant representational hierarchies. Labour that typically occurs behind screens is brought to the forefront of cinematic space.
This strategy aligns with Cultural Film Theory’s emphasis on how cinema reflects and contests social structures. Representation is never neutral; what is shown and what is excluded reveal ideological priorities. By centering marginalized labour, the film reconfigures cinematic visibility. It transforms background work into foreground narrative.
Moreover, the intersection of identity and labour complicates assumptions about technological participation. The film challenges stereotypes that position tribal communities as technologically disconnected. Instead, it shows that they are embedded often precariously within global digital systems.
Conclusion:
Humans in the Loop offers a profound meditation on labour under digital capitalism. Through deliberate visual language, rhythmic editing, and immersive sound design, it renders invisible labour visible. It exposes the commodification of cognitive and cultural knowledge while critiquing the unequal valuation of marginalized work.
Using Marxist and Cultural Film Theory, the film can be read as a critique of capitalist production in its digital form where labour is dispersed, abstracted, and obscured. Through Representation and Identity Studies, it reveals how identity shapes both participation in and recognition within technological systems.
Ultimately, the film invites viewers not only to empathize with Nehma but to rethink their understanding of AI itself. Technology is not autonomous; it is built on human effort. By illuminating this hidden foundation, the film calls for a transformation in how labour is perceived, valued, and acknowledged in the age of artificial intelligence.
References:
Barad, D. (2026, January). Humans in the loop: Exploring AI, labour and digital culture
Number Analytics. (2023). Film theory essentials: Key concepts and frameworks.
Sahay, A. (Director). (2024). Humans in the loop [Film]. India.
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