WORKSHEET FILM SCREENING ARANYA SAHAY’S HUMANS IN THE LOOP (2024)

 Blog is given by Dr. Barad sir. 

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This blog is part of move screening. This worksheet helps to critically think about a movie and how to analyze in better way, so here questions on before watching and after watching movie impact on student.  

Pre-Viewing: 

AI Bias & Indigenous Knowledge Systems

AI bias refers to the tendency of artificial intelligence systems to produce unfair or prejudiced outcomes due to the data they are trained on, the assumptions of developers, or the algorithms themselves. In Humans in the Loop, AI systems rely on human-labeled data to learn patterns, but when the human input reflects dominant cultural norms or stereotypes, the technology reproduces these biases. Indigenous ecological knowledge, like the local understanding of flora, fauna, or land use practices in Jharkhand, often does not fit neatly into rigid algorithmic categories. By incorporating such knowledge, the film demonstrates how technology may misinterpret or undervalue lived experiences that are context-specific, relational, and non-quantifiable. This tension highlights the ethical and epistemological challenges in AI development: while AI seeks universality, indigenous knowledge emphasizes localized, nuanced understanding. The narrative invites viewers to question who controls knowledge, whose perspectives are encoded into technology, and what consequences emerge when AI misrepresents culturally specific information. The film thus positions AI bias not only as a technical flaw but as a reflection of social hierarchies and epistemic exclusion.

Labour & Digital Economies

Invisible labour in digital economies refers to the work performed by humans that supports automated systems but often goes unrecognized or undervalued. In the film, Nehma’s work of labelling images for AI datasets exemplifies such labour: it is essential for the functioning of machine learning systems, yet it is often poorly paid, overlooked, and framed as mechanical rather than intellectual. Highlighting this labour reveals the hidden human cost behind “intelligent” technologies and challenges narratives of automation that erase the worker. By centering Nehma’s perspective, the film exposes economic and social inequalities in digital ecosystems, especially for marginalized communities. It also raises questions about ethics, compensation, and visibility in AI-driven industries. Recognizing invisible labour underscores the fact that technological progress relies on human effort and that socio-economic structures shape who bears these burdens. The narrative thus reframes AI not just as a neutral tool but as a site of labour politics, exploitation, and value production.

Politics of Representation

Representation in Humans in the Loop operates on multiple levels, including how technology and Adivasi culture are depicted. Publicity and reviews emphasize the film’s focus on an Adivasi woman navigating AI systems, foregrounding marginalized voices often excluded from technology narratives. The film juxtaposes local cultural knowledge with global technological frameworks, showing both the limitations of AI in capturing lived experience and the richness of indigenous epistemologies. Technology is represented not as neutral but as shaped by social, political, and economic forces, reflecting systemic biases. Simultaneously, the film’s portrayal of Adivasi culture challenges stereotypical depictions by presenting nuanced characters, personal agency, and knowledge systems embedded in everyday life. Through cinematography, storytelling, and character focus, the film draws attention to the ethical and cultural stakes of representation: who is seen, who is heard, and how knowledge is validated or dismissed. This politics of representation encourages viewers to critically assess both media narratives and AI systems for inclusivity, fairness, and cultural sensitivity.  

While Watching: 

Narrative and Story Telling 

1) Narrative & Storytelling: Personal Life and Algorithmic Structures

In Humans in the Loop, Nehma’s personal life is closely interwoven with larger algorithmic systems, highlighting how technology intersects with everyday existence. The film situates her family responsibilities, community ties, and cultural knowledge alongside the technical task of labelling data, showing that her work is not isolated but embedded in social and economic contexts. Key narrative turns, such as moments when she struggles to balance household duties with deadlines, or when her intimate knowledge of local ecology clashes with AI’s rigid categories, foreground labour, family, and knowledge systems simultaneously. By portraying these intersections, the story emphasizes that algorithmic processes are not abstract or neutral—they are shaped by human experience, social hierarchies, and cultural practices. The narrative uses Nehma’s perspective to make visible the human dimensions behind technological systems, showing how personal histories and community contexts influence, and are influenced by, machine learning infrastructures. The storytelling thus blends individual agency with systemic critique, creating a layered understanding of AI’s societal impacts.

Human-Machine Learning Loops

When Nehma “teaches” AI, the film moves beyond the technical language of machine learning to show the ethical, cultural, and cognitive dimensions of human-machine interaction. Her input is not mere data entry; it involves interpretation, judgment, and negotiation between human experience and algorithmic expectations. This process demonstrates that AI systems learn from humans in iterative loops, absorbing both knowledge and bias. The film suggests that these loops are reciprocal: while AI adapts to human input, human understanding is also shaped by the constraints and categories imposed by technology. By focusing on Nehma’s active role, the film highlights the co-dependence of humans and machines, revealing that AI development is inseparable from human labour, cultural knowledge, and social context. This perspective challenges the notion of AI as autonomous or objective, framing learning loops as ethical and relational engagements rather than purely technical processes.

2) Representation & Cultural Context: Adivasi Culture

In Humans in the Loop, Adivasi culture is depicted with nuance, highlighting language, traditions, and ecological knowledge as integral to Nehma’s identity. The film shows her understanding of local flora, fauna, and community practices, emphasizing relational knowledge passed down through generations. Language is used authentically in dialogue and narration, reflecting cultural specificity rather than flattening characters into generic roles. Traditions, rituals, and family structures are portrayed as living, dynamic systems rather than static backdrops. Ecological knowledge is presented as sophisticated and context-sensitive, often challenging AI’s rigid categorizations. Through careful cinematography, attention to daily life, and inclusion of cultural markers, the film situates Adivasi epistemologies as central rather than peripheral, asserting their value in conversations about technology, labour, and representation.

Challenging Media Stereotypes

The film challenges dominant media stereotypes about tribal communities and modern technology by presenting Adivasi characters as knowledgeable, capable, and engaged with contemporary digital economies. Nehma is not exoticized or reduced to victimhood; instead, her work in AI labelling underscores agency, skill, and intellectual contribution. By juxtaposing indigenous knowledge with algorithmic systems, the film critiques assumptions that tribal communities are “technologically backward” or disconnected from modernity. At the same time, it does not romanticize Adivasi life, showing real socio-economic pressures and ethical dilemmas. Technology is represented critically, revealing both its promise and its bias. Overall, Humans in the Loop subverts stereotypes by highlighting the intersection of culture, labour, and knowledge, while fostering respect for Adivasi expertise within global technological frameworks.

3)Cinematic style and Meaning Mise-en-Scene and Cinematography

In Humans in the Loop, mise-en-scène and cinematography are carefully crafted to contrast Nehma’s natural and digital worlds. Forest scenes are framed with wide shots and soft, natural lighting, emphasizing depth, texture, and the interconnectedness of ecology, highlighting the richness of her indigenous environment. In contrast, computer screens and AI workspaces are depicted with tight framing, harsh artificial light, and muted colors, creating a sense of confinement and monotony. Rituals and family moments use medium and close-up shots to capture gestures, expressions, and cultural detail, giving intimacy and human presence. The framing juxtaposes expansive, organic spaces with claustrophobic, grid-like digital environments, visually reinforcing the tension between nature and technology, personal knowledge and algorithmic abstraction. Camera movements are deliberate—slow pans in the forest versus static, repetitive shots at the workspace—enhancing the thematic contrast between fluid, lived experience and rigid digital labour.

Cinematic Style & Meaning: Sound Design and Editing

Sound design and editing rhythms further emphasize the contrast between analog life and digital labour. Natural environments feature diegetic sounds like birdsong, rustling leaves, and community chatter, creating an immersive, organic soundscape. In the AI workspace, clicks, keyboard strokes, and notification alerts dominate, with a mechanical, repetitive rhythm that underscores monotony and invisible labour. Editing alternates between longer, contemplative takes in nature and rapid, fragmented cuts in the digital setting, reflecting cognitive strain and procedural constraints of Nehma’s work. The juxtaposition of sound textures—organic versus mechanical—along with contrasting pacing, heightens the thematic tension between human, cultural knowledge and algorithmic systems. Collectively, these elements shape narrative meaning, making visible the ethical, emotional, and cultural stakes embedded in Nehma’s engagement with AI.

4) Ethical & Political Questions: AI and Culturally Specific Data

In Humans in the Loop, ethical dilemmas emerge when AI is trained on culturally specific data because the systems risk misrepresenting, oversimplifying, or erasing nuanced local knowledge. Nehma’s work with images and descriptions of flora, fauna, and social practices highlights how algorithmic categories may impose dominant cultural frameworks on indigenous epistemologies. The film shows tensions between accuracy, fairness, and utility: should AI reflect local knowledge even if it conflicts with standard datasets? Is it ethical to use marginalized communities’ labour and knowledge without recognition or adequate compensation? These questions foreground issues of bias, epistemic injustice, and the moral responsibility of technology developers. By dramatizing these dilemmas, the film encourages viewers to consider how ethical AI must navigate cultural diversity, human labour, and social accountability rather than relying solely on technical correctness.

Ethical & Political Questions: Human-in-the-Loop Metaphor
The human-in-the-loop metaphor in the film operates beyond its technical meaning of human supervision in AI. Politically, it highlights the dependence of global technologies on marginalized labour, revealing inequalities in who produces knowledge and who benefits from it. Socially, it emphasizes relational and interpretive human work, showing that algorithms cannot function independently of cultural and cognitive contexts. Culturally, it underscores the tension between local epistemologies and global technological systems, suggesting that human oversight is not just about error correction but about preserving knowledge, identity, and ethical values. By centering Nehma’s perspective, the metaphor critiques assumptions of automation and neutrality, illustrating that “human-in-the-loop” carries implications for justice, representation, and recognition in socio-technical landscapes.

Post-View:

Task: 1

AI, Bias, & Epistemic Representation: Critical Reflection

Humans in the Loop presents a nuanced exploration of the intersection between technology and human knowledge, focusing on how AI systems are not neutral but deeply shaped by social, cultural, and epistemic contexts. The narrative centers on Nehma, an Adivasi woman from Jharkhand, whose work in AI data-labelling brings to light the complexities of encoding human knowledge into algorithmic structures. Through her experiences, the film illustrates that algorithmic bias is culturally situated rather than purely technical. The AI systems she works with depend on human-labelled data that reflect dominant societal norms. For instance, when Nehma encounters images or local practices that do not conform to pre-set categories, the AI misclassifies or rejects them. These moments demonstrate that biases in AI emerge not from mathematical imperfection alone, but from social hierarchies embedded in the datasets, highlighting how technology perpetuates existing power structures.

The film also foregrounds epistemic hierarchies, revealing whose knowledge is validated within technological systems. Nehma’s indigenous ecological knowledge—her understanding of local flora, fauna, and cultural practices—is positioned against algorithmic frameworks designed with a universalist, often Western-centric logic. Cinematically, the contrast is emphasized through mise-en-scène: wide, vibrant shots of forests and community spaces reflect the richness of lived experience, whereas the AI workspace is depicted with harsh artificial lighting and repetitive, tight-framed shots, visually reinforcing the tension between human knowledge and computational abstraction. This framing underscores that certain forms of knowledge—quantifiable, codifiable, and aligned with dominant technical paradigms—are privileged, while localized, experiential, or relational knowledge is marginalized. Nehma’s labour becomes a site where these epistemic inequalities are enacted and contested.

Representation plays a critical role in the film’s cultural critique. By centering Nehma’s perspective, Humans in the Loop challenges stereotypical portrayals of tribal communities as technologically disengaged or intellectually subordinate. Instead, the film highlights their expertise and agency, suggesting that technological systems are incomplete without incorporating diverse epistemologies. This aligns with concepts from film studies regarding ideology and power: the AI system represents institutional authority, embedding normative assumptions about knowledge and culture, while Nehma’s work destabilizes these assumptions, making visible the human dimension behind ostensibly neutral technologies. Sound design and editing further reinforce this critique; natural diegetic sounds in the forest juxtapose mechanical, repetitive digital noises in the workspace, emphasizing the dissonance between embodied human knowledge and automated systems.

The narrative also raises questions about ethical responsibility in AI. Nehma’s interventions in labelling data are not merely technical; they involve interpretive judgement, cultural translation, and moral decisions about what knowledge should be preserved or prioritized. In teaching the AI, she becomes a mediator between human experience and machine abstraction, embodying the “human-in-the-loop” concept not just as a technical function but as a metaphor for epistemic stewardship. This highlights how AI development is inseparable from human labour, culture, and ethics, challenging discourses that present technology as objective or autonomous.

From a scholarly perspective, the film can be analyzed using concepts of representation, ideology, and power relations. Representation here is not only about portraying Adivasi culture authentically, but also about making visible the epistemic labour that underpins AI systems. Ideology operates through algorithmic structures that privilege certain knowledge and marginalize others, reflecting broader societal hierarchies. Power relations are enacted in both the workplace dynamics—between Nehma, her supervisors, and the technology—and in the epistemic authority embedded in AI systems themselves. By depicting these intersections, Humans in the Loop encourages a critical examination of how technological knowledge is socially produced and culturally mediated.

In conclusion, Humans in the Loop effectively critiques the notion of AI as an objective or neutral force, showing that algorithmic bias is inherently cultural and that epistemic hierarchies determine whose knowledge is recognized. Through its narrative, visual style, and soundscape, the film foregrounds the ethical, political, and cultural stakes of human-machine interactions, emphasizing the value of indigenous knowledge and the labour required to integrate it into technological systems. It demonstrates that technology is not separate from society but is deeply entwined with questions of power, representation, and justice, offering a compelling case for human-centered and culturally sensitive approaches to AI development.

Task: 2

Labour & the Politics of Cinematic Visibility: Visualizing Invisible Work

In Humans in the Loop, the film visualizes invisible labour through its detailed depiction of Nehma’s AI data-labelling tasks. Cinematography and mise-en-scène emphasize repetition, confinement, and the mechanical rhythm of her work: tight framing, overhead shots of her hands on the keyboard, and close-ups of computer screens create a sense of monotony and physical fatigue. The visual language conveys the emotional experience of labour—the tension, mental focus, and occasional frustration—making the audience aware of the human effort behind ostensibly “automated” systems. Contrasting these shots with vibrant, wide-angle images of forests and community life highlights the disparity between meaningful, context-rich knowledge and its reduction to digital inputs. Editing rhythms, with quick cuts during workflow sequences, further accentuate the relentless pace and emotional strain of Nehma’s labour, capturing the invisibility of human contribution to AI technology.

Cultural Valuation of Marginalized Work
Through these visual strategies, the film critiques how society undervalues marginalized work. Nehma’s labour, essential for AI functioning, is portrayed as largely unrecognized and low-paid, reflecting broader patterns of digital capitalism where human effort is commodified but invisible. The film underscores the epistemic and economic hierarchies that determine whose work counts, showing that labour performed by marginalized communities is often rendered invisible despite its centrality to technological systems. By framing her work as both technically demanding and culturally informed, the narrative challenges assumptions that marginalized workers are replaceable or unskilled, highlighting the disconnect between labour input and social recognition.

Inviting Empathy, Critique, and Transformation
The film encourages empathy by immersing viewers in Nehma’s lived experience, using sound design, pacing, and visual focus to foreground her cognitive and emotional engagement. At the same time, it offers critique of structural inequities in the digital economy, revealing how technological systems rely on human labour while masking its value. By making the invisible visible, the film invites reflection on systemic exploitation and raises questions about ethical AI and fair labour practices. From a theoretical perspective, Marxist and cultural film theory can be applied to interpret these portrayals: the film exposes classed labour relations and commodification of human effort, while representation and identity studies reveal how Nehma’s positionality as an Adivasi woman intersects with labour, technology, and societal power structures. Ultimately, Humans in the Loop transforms the perception of digital labour, asserting the necessity of recognizing and valuing human contribution within technological infrastructures.

Task: 3

Film Form, Structure & Digital Culture: Natural vs Digital Spaces
In Humans in the Loop, the interplay of natural imagery and digital spaces is central to conveying philosophical concerns about human-AI interaction. Forests, rivers, and community spaces are filmed with wide shots, natural lighting, and lingering takes, evoking openness, relationality, and complexity inherent in indigenous knowledge systems. In contrast, AI workspaces are depicted with tight framing, harsh fluorescent lighting, and repetitive visual motifs, suggesting confinement, procedural rigidity, and abstraction. This juxtaposition visually encodes the tension between organic, contextual knowledge and algorithmic reduction, signaling how digital culture attempts to categorize and systematize lived experience. By contrasting these visual codes, the film communicates broader themes of epistemic hierarchy, cultural erasure, and the ethical stakes of human-machine interaction. The structuralist lens can interpret these contrasts as binary oppositions—nature versus technology, local knowledge versus algorithmic logic—that generate meaning through their relational dynamics within the narrative.

Film Form, Structure & Digital Culture: Aesthetic Shaping of Labour, Identity, and Technology
Aesthetic choices—including camera angles, editing rhythms, and sound design—shape the viewer’s experience of labour, identity, and technology. Close-ups of Nehma’s hands on keyboards, intercut with her facial expressions, communicate concentration, fatigue, and the cognitive demands of invisible labour. Rapid, fragmented editing in the workspace conveys monotony and procedural pressure, while slower cuts in natural settings allow reflection and emotional resonance. Sound design contrasts natural diegetic sounds—birdsong, water, community voices—with mechanical clicks, alerts, and keyboard strokes, reinforcing the sensory and emotional divide between human experience and digital abstraction. From a formalist perspective, these techniques create meaning by aligning form with thematic content, illustrating how technology mediates identity and labour. Narrative sequencing, alternating between personal, ecological, and technological spaces, emphasizes relationality and stakes in human-AI loops. Semiotic analysis reveals these cinematic codes as signs of broader cultural and ethical concerns, showing that film form itself is instrumental in critiquing digital culture and rendering human labour visible.

References:

Film Theory: Critical Concepts in Media and Cultural Studies – comprehensive foundational text on major theoretical approaches.

Film Theory: The Basics (2nd Ed., by Kevin McDonald) – covers historical and contemporary film theory, including how digital technologies shape modern cinema.

Classic texts to consider (e.g., Bazin, Bordwell & Thompson, Stam) for narrative and cultural analysis — recommended via film school reading lists.

Apparatus Theory

For ideological critique of film representation. 

Semiotics & Narrative Structure — For understanding meaning-making in films.