Introduction
Humans in the Loop, directed and written by Aranya Sahay, explores the intersection of artificial intelligence (AI), human knowledge, and the socio-political dimensions of digital labour. The film follows Nehma, an Adivasi woman from Jharkhand, as she labels images for AI systems, revealing the hidden human effort behind machine learning. By juxtaposing Nehma’s digital work with her cultural environment, the narrative interrogates algorithmic bias, epistemic hierarchies, and the social valuation of invisible labour. Using cinematic form, representation, and narrative, the film raises critical questions about power, knowledge, and technology.
Pre-Viewing Understanding: Key Ideas
AI Bias and Indigenous Knowledge
Artificial Intelligence (AI) systems are often perceived as neutral or objective tools, capable of making decisions independently. However, the reality is that these systems are trained on data created, curated, and labeled by humans. When the majority of this data represents the perspectives, experiences, and norms of dominant social groups, the AI system inevitably reflects those biases. This means that the so-called “intelligence” of AI is culturally and socially constructed rather than inherently impartial. Algorithmic bias arises when AI fails to understand or misrepresents experiences outside the dominant cultural framework, producing outputs that perpetuate inequality or invisibility.
Indigenous knowledge systems, such as those of the Adivasi communities depicted in the film, are rooted in lived experience, ecological understanding, and deeply embedded cultural traditions. This form of knowledge is holistic, relational, and often non-linear, developed through generations of interaction with the environment and social life. AI systems, in contrast, require structured and simplified categories, rigid taxonomies, and quantifiable labels. The mismatch between these two knowledge systems means that AI often cannot “read” or properly represent indigenous perspectives, leaving them marginalized or invisible in technological outputs.
Humans in the Loop foregrounds this tension, showing that AI is not a neutral instrument but a reflection of societal power hierarchies. By highlighting how certain cultural knowledges are excluded, the film critiques the assumption that technology is inherently objective and underscores the need for diverse and inclusive datasets that respect multiple forms of knowledge.
Invisible Labour in Digital Economies
While AI may appear to function automatically, the film demonstrates that it is heavily dependent on human labour. The repetitive and meticulous work of labeling images, correcting outputs, and training algorithms is essential for AI systems to “learn.” Despite its centrality, this labour often goes unnoticed in public discourse, as the narrative surrounding AI tends to glorify machine autonomy.
The concept of “human in the loop” acknowledges that humans are integral to AI functioning, yet in practice, the contributions of these workers—many of whom belong to marginalized communities—are undervalued or ignored. Humans in the Loop places this hidden labour at the center of its story, making visible the human effort behind supposedly independent technological systems. Through scenes that show Nehma methodically labeling images and interacting with AI interfaces, the film emphasizes that technology is not separate from human action; it is sustained by the knowledge, expertise, and repetitive work of ordinary people.
By portraying the emotional and cognitive toll of this invisible labour, the film also highlights the social inequities embedded in digital economies. Workers who maintain AI systems are often economically vulnerable and socially marginalized, reinforcing the ethical question of who benefits from technological advancement and who bears its costs.
Politics of Representation
Representation in media and technology shapes not only visibility but also power. Tribal communities, including Adivasi groups, are often stereotyped as disconnected from modern technology, portrayed as “primitive” or technologically incapable. The film directly challenges these assumptions by presenting Nehma as an active contributor to AI development.
Rather than reducing indigenous identity to backwardness or exoticism, the narrative emphasizes that Adivasi knowledge is sophisticated, culturally rich, and technologically relevant. Nehma’s work in training AI systems positions her as a knowledge bearer whose input is essential for accurate representation. By giving screen space to her expertise and labour, the film reframes indigenous communities as participants in technological processes rather than passive subjects.
This reframing carries broader implications for how audiences understand technology and culture. By centering marginalized voices, the film critiques both the invisibility of certain knowledge systems in AI and the ideological assumptions that underlie digital technologies. Representation becomes a tool for questioning whose knowledge is recognized, whose labour is valued, and how cultural hierarchies are reproduced in technological systems.
Observations While Watching the Film
Narrative Structure
The film employs a dual-spatial narrative, juxtaposing Nehma’s home and natural surroundings with her digital workspace. Village life is depicted through intimate shots of domestic activities, community interactions, and moments of connection with the forested landscape. In contrast, the digital workspace emphasizes repetitive labour, confined spaces, and machine-centric tasks. This spatial contrast highlights the tension between human experience and technological systems, making visible the labour that sustains AI.
The narrative unfolds at a deliberate, measured pace, focusing on everyday routines rather than dramatic or sensationalized events. This slow, observational rhythm allows viewers to appreciate the significance of seemingly mundane activities, such as data labelling, and underscores the cognitive and emotional labour involved. By allowing ordinary work to occupy screen time, the film humanizes technological processes and emphasizes that AI relies on continuous, careful human effort.
Representation of Adivasi Culture
A key strength of the film is its authentic and respectful portrayal of Adivasi culture. The narrative foregrounds daily life, traditional rituals, and ecological knowledge, presenting these practices as living, adaptive, and integral to contemporary existence. Forest landscapes, agricultural practices, and village spaces are captured in wide shots with natural lighting, highlighting the deep connection between people and their environment.
Language plays a vital role in conveying cultural specificity. The film incorporates Kurukh, the mother tongue of the community, in dialogues and everyday interactions. This linguistic inclusion not only enhances authenticity but also affirms cultural identity, making the voices of the community central to the narrative. Scenes where Nehma converses with family members in Kurukh convey intimacy, knowledge transmission, and the continuity of indigenous traditions, while interactions in Hindi demonstrate the negotiation between local and broader societal contexts.
By portraying culture as dynamic rather than static, the film challenges stereotypical representations of tribal communities as backward or disconnected from modern technology. Indigenous knowledge is framed as both meaningful and technologically relevant. Rituals, seasonal work, and community gatherings are shown as interwoven with contemporary life, giving viewers a sense of continuity, resilience, and cultural richness.
Mise-en-Scène and Cinematography
The visual composition reinforces the thematic contrast between natural and digital spaces. Outdoor scenes are framed with openness, flowing movement, and warm, saturated colours, evoking a sense of freedom, rootedness, and continuity. The camera often lingers on gestures, landscapes, and interactions, allowing the environment itself to communicate cultural meaning.
In contrast, indoor digital workspaces are portrayed with tight framing, artificial lighting, and geometric compositions that emphasize confinement and repetition. Screens, keyboards, and desks dominate the frame, visually reflecting the mechanized and controlled nature of AI-related labour. The use of close-up shots on Nehma’s face while labelling images captures her concentration, patience, and emotional engagement, highlighting the human element behind technological outputs. Repeated shots of clicking, scrolling, and data categorization reinforce the monotony and intensity of her work, creating empathy for the labourers often rendered invisible in AI narratives.
Sound Design and Editing
Sound design plays a critical role in differentiating cultural and technological spaces. Natural environments are accompanied by layered, organic sounds such as rustling leaves, birdsong, flowing water, and distant communal chatter. These sounds create a sensory experience of groundedness, reinforcing the connection between the community and its environment.
In contrast, digital spaces are dominated by mechanical keyboard clicks, mouse sounds, electronic tones, and notification alerts. The repetitive auditory patterns mirror the structured, algorithm-driven nature of AI work, emphasizing the mechanical rhythm of digital labour.
Editing further strengthens this distinction. Outdoor and cultural scenes feature fluid transitions and longer takes, reflecting the organic flow of community life. In digital work sequences, longer, slower takes of repetitive actions accentuate the mental and physical effort involved in sustaining AI systems. By carefully manipulating sound and temporal rhythm, the film allows viewers to experience the contrasting textures of human and technological labour, highlighting both dependence and disconnection.
Post Viewing Task
Task 1: AI, Bias, and Epistemic Representation
Algorithmic Bias as Culturally Situated
The narrative of Humans in the Loop demonstrates that algorithmic bias is socially and culturally constructed rather than purely technical. The AI system repeatedly fails to generate authentic representations of Adivasi culture until Nehma and her daughter upload real photographs. This failure indicates that AI’s “knowledge” is shaped by dominant cultural datasets, which exclude marginalized perspectives. The AI is not neutral; it reflects the ideologies and social hierarchies embedded in the data it learns from.
From the perspective of Apparatus Theory, cinema itself, like AI, constructs meaning through mediated systems. The film visually represents AI as a technology that depends on human input, reinforcing the notion that technological systems mirror societal power structures. The camera’s close-ups of screens and data input highlight the mediation between human knowledge and machine processing, making the audience aware of the cultural and ideological framing within AI systems.
Epistemic Hierarchies
The film foregrounds the uneven distribution of knowledge recognized by technological systems. Indigenous ecological knowledge, relational understanding, and lived experiences are initially invisible to the AI, whereas standardized, dominant cultural knowledge is readily processed. This creates an epistemic hierarchy: some forms of knowledge are valued, others are excluded. Cinematic techniques—contrasting wide shots of village life with constrained frames of digital labour—visually reinforce the unequal recognition of different knowledge systems.
Representation plays a critical role here. The use of Kurukh language, natural forest settings, and cultural rituals emphasizes the legitimacy and depth of indigenous knowledge. These elements position Adivasi expertise as essential for shaping AI outputs. Through this lens, the film critiques the ideology that positions technical knowledge as superior to experiential or cultural knowledge, highlighting power relations between marginalized communities and technological systems.
Task 2: Labour and the Politics of Cinematic Visibility
Visualizing Invisible Labour
AI systems appear autonomous, yet the film emphasizes that human labour is central to their operation. Nehma’s work of labelling images, correcting outputs, and training algorithms is repetitive, meticulous, and emotionally taxing. Cinematic strategies, such as tight framing, repeated close-ups, and constrained lighting, communicate both monotony and cognitive effort.
By visually foregrounding this labour, the film challenges assumptions that digital work is low-value or invisible. Sequences showing scrolling, clicking, and categorizing emphasize the mechanical rhythm and emotional weight of data-labelling. In contrast, wide shots of Nehma’s village and natural environment depict relational and culturally rich knowledge, highlighting the contrast between socially valued and undervalued labour.
Cultural Valuation and Marginalised Work
From a Marxist and Cultural Film Theory perspective, the film critiques digital capitalism’s exploitation of marginalized labour. Human effort is commodified and rendered invisible, yet essential for technological development. The audience is made to recognize that AI systems depend entirely on workers who remain socially and economically undervalued.
The narrative invites empathy and ethical reflection. By showing Nehma’s emotional engagement, fatigue, and resilience, the film encourages viewers to consider the human cost of technological progress. It also highlights structural inequalities: digital work is necessary but socially marginalized, while dominant knowledge and computational outputs are valorized.
Representation, Identity, and Labour
Through Representation and Identity Studies, the film demonstrates how identity and labour intersect. Nehma’s role as an Adivasi woman performing technical labour disrupts stereotypes of indigenous communities as disconnected from technology. The film portrays her as both culturally rooted and technologically skilled, emphasizing that knowledge and labour are inseparable from social identity. This reframing challenges audiences to reconsider assumptions about who contributes to technological systems and whose labour is recognized or neglected.
Cinematic Techniques and Form
The film uses mise-en-scène, cinematography, sound, and editing to reinforce its thematic concerns. Natural environments are shown with wide, open compositions, warm colours, and flowing movement, evoking freedom, relational knowledge, and cultural continuity. Digital workspaces employ tight framing, muted tones, and constrained camera movement, visually reflecting mechanization and repetition.
Sound design amplifies these contrasts: ambient forest sounds (wind, birds, community chatter) evoke cultural grounding, while keyboard clicks, mouse movements, and electronic tones create a mechanical auditory environment. Editing rhythm mirrors these spaces, with slower, repetitive cuts in digital sequences emphasizing monotony and effort, while natural scenes flow organically. These formal choices align with Bordwell and Thompson’s principles, demonstrating how cinematic techniques convey narrative meaning and highlight labour, identity, and the human-technology relationship.
Task 3 — Film Form, Structure & Digital Culture
Interplay of Natural Imagery and Digital Spaces
The film juxtaposes Nehma’s village and natural environment with her digital workplace to explore tensions between human knowledge and machine systems. Forests, fields, and domestic spaces are framed in wide shots with natural lighting and warm colours, symbolizing relational knowledge, cultural identity, and ecological connection. Conversely, AI workspaces employ tight framing, artificial lighting, and constrained movement, reflecting mechanical repetition, algorithmic abstraction, and the monotony of digital labour.
From a Structuralist / Film Semiotics perspective, these contrasting visual codes function as signifiers: expansive natural settings signify human autonomy and cultural depth, while confined digital spaces signify dependence on rigid, impersonal technological structures. This formal contrast communicates broader thematic concerns about human-AI interaction, showing that AI is culturally situated and reliant on human input.
Aesthetic Choices and the Viewer’s Experience
Camera Techniques: Close-ups of Nehma’s hands, eyes, and gestures during data labelling emphasize focus, cognitive effort, and emotional strain. Repeated shots of clicking and scrolling illustrate the rhythm and monotony of invisible labour. Wide shots of village life and rituals contextualize her identity, knowledge, and agency, reinforcing the epistemic significance of indigenous perspectives in shaping AI.
Editing and Sequencing: Slower cuts during labelling sequences allow viewers to experience the repetitive labour physically and emotionally. Natural and domestic sequences are edited with smoother, flowing transitions, highlighting relational and ecological knowledge. The sequencing of AI failures followed by correction through Nehma’s intervention visually narrates human-machine interdependence.
Sound Design: Natural ambient sounds (wind, birds, community dialogue in Kurukh) reinforce cultural grounding and relational knowledge, while digital sequences feature mechanical sounds of keyboards and electronic alerts, producing a sense of repetition and mechanization. Sound contrasts amplify the narrative tension between human and machine spaces.
Narrative Form: The film’s deliberate pacing, intercutting domestic, cultural, and digital sequences, situates labour, identity, and AI within the broader socio-cultural context. Moments of AI failure and correction illustrate epistemic hierarchies and the centrality of human knowledge in shaping technological outputs.
Formalist Analysis: According to Formalist and Narrative Theory, these techniques are meaningful rather than aesthetic. Camera angles, framing, editing rhythm, and sound all encode philosophical concerns: the invisibility of labour, the dependence of AI on human knowledge, and the ethical implications of digital culture.
Conclusion
Humans in the Loop presents a nuanced exploration of the relationship between human knowledge, labour, and artificial intelligence. By highlighting algorithmic bias, epistemic hierarchies, and the centrality of indigenous expertise, the film demonstrates that AI is culturally situated and dependent on human input. Through its visual language, sound design, and narrative structure, it makes invisible labour tangible, challenges stereotypes about marginalized communities, and emphasizes the ethical and social dimensions of digital technologies. Ultimately, the film underscores that technology is inseparable from the humans who sustain it, inviting reflection on power, representation, and the valuation of labour in contemporary digital culture.
References:-
Anjum, Nootan. "Aranya Sahay's Humans in the Loop and the Politics of AI Data Labelling." The Federal, 2026, thefederal.com/films/aranya-sahay-humans-in-the-loop-oscar-adivasi-data-labelling-jharkhand-ai-tribal-216946.
Barad, Dilip. "Humans in the Loop: Exploring AI, Labour and Digital Culture." Blog post, Jan. 2026, blog.dilipbarad.com/2026/01/humans-in-loop-film-review-exploringai.html.
"Humans in the Loop (Film)." Wikipedia, Wikimedia Foundation, retrieved 15 Feb. 2026, en.wikipedia.org/wiki/Humans_in_the_Loop_(film).
Indian Express Editorial. "Humans in the Loop Explores How AI Clashes with Traditional Belief Systems." The Indian Express, 3 May 2025, indianexpress.com/article/express-sunday-eye/humans-in-the-loop-explores-how-ai-clashes-with-traditional-belief-systems-9980634/.
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