Bhaskar Mitra | ভাস্কর মিত্র

@bmitra.bsky.social

1320 Followers 615 Following

Information Retrieval Researcher ✊🏽🍉🕊️ | Pronouns: He/him Homepage: https://bhaskar-mitra.github.io/

Preview of book covers.
Bar chart of the top 20 most militarized universities.
Sketchnote of my keynote address at CALC 2026 conference.
Photo of the panelists and moderator on stage.
Cover image with the title "Why I'm Leaving Big Tech: A tech worker's reflections" by Bhaskar Mitra.
Shot from the documentary film "Sudan, Remember Us" showing a road block sign that reads "Sorry for the delay, uprooting a regime".
Illustrating the practices, projects, and provocations of emancipatory IR. The hexagons at the center represent several potential projects. The empty hexagons indicate that the set of mentioned projects are not complete, and the community should over time identify new ones and shape existing ones, as appropriate. Sur-rounding the projects is the framework of practices of emancipatory IR that are relevant to all projects in this area, consisting of (i) diagnose and critique, (ii)imagine viable alternative futures, and (iii) elaborate theories of change. Provo-cations to spark new ideas and research directions are depicted as a nudging hand.
Photo from my balcony looking over Montreal downtown under a light blanket of snow at sunset.
Screenshot of ClearSky showing the top 5 blocked accounts on Bluesky in the last 24 hours are whitehouse-47.bsky.social, homelandgov.bsky.social, deptofwar.bsky.social, hhsofficial.bsky.social, and state-department.bsky.social.
Screenshot of ACM SIGIR profile on Bluesky displaying their logo, banner image, description, and website information.
A group photo of all the seminar attendees standing together on the Baroque hall chapel stairs at Schloss Dagstuhl.
**Responsible Web** We invite research contributions to the Responsible Web track. Over the past decades, the Web and the internet have grown to be an integral part of our lives. Through this track, we aim to surface the latest research on what it means to build a responsible web and internet, one that contributes towards realising more equitable, emancipatory, and sustainable futures. The relevant topics include, but are not limited to: * Algorithmic accountability and transparency on the web * Audits of web technologies, standards, platforms, and applications * Consent frameworks and practices on the web * Data and user privacy-enhancing technologies for the Web * Ethical and legal aspects of web-scale data analysis, uses and collection practices * Fairness, accountability, transparency and ethics of web technologies * Human-perceived consequences of algorithmic deployment on the web * Machine-in-the-loop, human agency and autonomy * Measurement, analysis, and circumvention of Web censorship * Mitigating misinformation and disinformation * Mitigating online harms, including harassment, spam, and fake reviews * Social and technical mechanisms of refusal for web technologies and applications * Sustainability and climate impact of web technologies * Technology governance, policy, and regulations * Web for democratic and emancipatory futures
Poster for WAIE+ "Join the Debate" with Mia Shah-Dand, Hessie Jones, Beatriz González Mellídez, and Bhaskar Mitra.
**IR-for-Good Track Dates** Abstract submission: October 21, 2025, 11:59pm (AoE) Paper submission: October 28, 2025, 11:59pm (AoE) Paper notification: December 16, 2025 **IR-for-Good Track Chairs:** Bhaskar Mitra – Independent Researcher, Canada Maria Heuss – University of Amsterdam **Contact:** ecir2026-ir4good@easychair.org
Submissions of papers must be at least 6 pages (the sixth page should have at least some content but not necessary to fill it) and at most 12 pages in length plus additional pages for references. The "Theory of Change" section and Appendices count toward the page limit. Please put appendices before the references for paper submission. While this year we do not set separate submission tracks for full and short papers in the IR-for-Good track, the assessment of each submission will be based on whether the paper length is commensurate with its contribution. For example, a 6-page paper would be accepted if its scientific contribution is worth 6 pages. However, a 12-page paper would be considered weak if it only contains the substance of a 6-page paper.
Claim: Our work that proposes a method for making expensive machine learning models for IR more efficient contributes towards sustainability and reducing impact on the environment. Considerations for this claim: What preconditions are necessary for the efficiency improvements to translate to reduced impact on the environment? E.g., according to the Jevons paradox in economics, when technological advancements make a resource more efficient to use (thereby reducing the amount needed for a single application) it often results in overall increase in demand, causing total resource consumption to rise instead of falling. Is it more likely then that more efficient models may in fact lead to a false sense of mitigation and result in much wider adoption contributing to increased harm to the environment?
We encourage authors and reviewers to critically engage with this section while acknowledging the real uncertainty of how any well-intentioned research may impact society in practice. Our goal is not to encourage authors to inflate their claims of social impact but to rigorously deliberate on their sociotechnical assumptions and enumerate the necessary preconditions for the work to have its desired impact and also potential negative externalities. We recommend that the "Theory of Change" section should explicitly state: 1. What is the identified societal need / problem, and how are the core contributions from this current work expected to address them? 2. What preconditions are necessary or what assumptions need to hold for this work to have its desired effect, and how likely are they to hold true in practice? 3. What are possible negative externalities of this approach and is it plausible that this may lead to new or different harms? Authors are encouraged to include any additional discussions that they may deem relevant in this section.
**Theories of Change** IR research that tries to affect positive social change needs to be grounded in rigorous understanding of the sociotechnical challenges and the complex socio-political context in which they exist. We require every submission that proposes new IR tools, methods, datasets, and interventions to explicitly include a separate section elaborating how the work contributes towards desired social outcomes, i.e., their theory of change. This section should not be an afterthought, instead it should be a critical part of the core motivation of the work. It is not mandatory to name the section "Theory of change" and position papers and critiques are exempted from this requirement.
Specific areas of interest include, but are not limited to, how IR intersects with and/or can support: * Accessibility and disability justice * Art, culture, and representation * Crisis and disaster management * Decolonization and racial justice * Emancipation, anti-oppression, and social justice * Gender and sexuality justice * Informed citizenry, democracy, and collective decision making * Law and restorative justice * Literacy and knowledge production * Privacy and dignity * Public health and community health * Social, political, and economic equity * Sustainability and environmental justice * Worker rights and labor movements All submissions must be relevant to IR. For clarity on what should be considered relevant to IR please check the Call for Full papers for ECIR 2026.
**Scope** IR-for-Good includes IR research that: 1. Explicitly concerns with new research directions and system design to achieve specific societally beneficial outcomes, 2. Develops new fairness, privacy, transparency, accessibility, sustainability, and other similar societally-motivated interventions, and/or 3. Identifies and critiques the ways in which existing IR methods and systems and how we do IR research may contribute to systemic harm or impede social progress.
Screenshot that reads: "Workers of the world, unite! This is the moment for movement building. It is incredibly difficult work, as it always has been throughout history. It requires us to reconcile collective risks and systemic harms with individual circumstances. I am empathetic towards those who may genuinely find these tools useful. I am aware of some of the ongoing debates around AI art and accessibility, and again I empathize with those arguments. I don’t believe these positions are irreconcilable, but it does require bringing a multitude of people to the table to build solidarity, find workable solutions, collectively strategize, and build a movement. Because we cannot let Big Tech co-opt our differences to divide and defeat us. We should resist when Big Tech tries to pry open those cracks and claim that AI shaming is “ableist” or that it arises from “class anxiety induced in middle class knowledge workers” and intended to protect “privileged class of knowledge work”. Nuh uh! Our liberation is bound together and capitulating to the politics of AI hype will be catastrophic for the entirety of the working class. And we must also outright oppose when Big Tech claims that the path forward is to “upskill artists and knowledge workers to leverage AI” because training artists and knowledge workers to prompt an AI model is not teaching them a meaningful new skill but luring them to abdicate their craft, creativity, and critical thinking. So, workers of the world, unite! We have nothing to lose but our cha… chatbots."
Screenshot that reads: "But this is not a call for idle speculation, this must be an integral part of our day-to-day emancipatory praxis. For example, if you work on developing AI tools and systems for knowledge work, ask yourself who you are building it for, the workers or the bosses? How would your approach and design fundamentally change if your goal was explicitly to shift power from bosses to workers? What mechanisms of collective action and resistance would you build in into the system design? What technologies would you develop to empower artists and knowledge workers to safeguard the artefacts they produce from being commodified and appropriated (example)? What direct actions can you take today to challenge the AI hype and its uncritical adoption within the tech community? What spaces can you create for collective learning about the history of labor movements, for collective sensemaking of our current challenges and risks, and for collective organizing (unions and otherwise)? We must remember that it is not just our desired futures that should inform our actions in this present moment, but also our past. Learn about the true history of the Luddite uprising, and ask yourself what would be the equivalent of “machine breaking” for our generation?" followed by an image of the cover of Brian Merchant's book "Blood in the machine".
Screenshot that reads: "Imagining new futures, learning from our past It doesn’t have to be this way. Technology is not inherently repressive. But it does have its politics. Technology is shaped by our visions of desired futures, and in turn actualizes social transformations towards envisioned futures. And that is why the AI hype is dangerous because it manufactures a crisis of imagination, trying to convince everyone that the path we are on is the only possible future, and aggressively rebukes anyone for questioning or resisting their desired future being forced on all of us." with the Ursula K. Le Guin quote "The exercise of imagination is dangerous to those who profit from the way things are because it has the power to show that the way things are is not permanent, not universal, not necessary."
Screenshot that reads: "The environmental costs of AI The environmental impacts of global data center expansion for AI are also being felt disproportionately by already marginalized and vulnerable communities. Climate change is a racial justice issue. Climate change is a social justice issue. Instead of investing in our social infrastructure, protecting the already vulnerable communities, and taking drastic steps to reduce our fossil fuel emissions, Big Tech wants you to believe that AI will solve climate change. Of course, it won’t. And coincidentally all of Big Tech’s climate pledges are also a hot mess (pun intended). It is remarkable to me that of all things it is chatbots that the ruling class has decided is worth burning the whole planet down for. This is naked necropolitics." followed by the New Yorker cartoon: https://www.newyorker.com/cartoon/a16995.
Screenshot that reads: "Let them eat chatbots The adverse effects of AI on the working class are not restricted to the commodification and appropriation of labor. AI technologies are also being positioned to provide cover for depriving the working class from basic services such as healthcare and education. Most of us I imagine have heard at least one AI-bro predict that the solution to the scarcity of doctors, teachers, and therapists in under-funded communities is to give them access to AI chatbots designed to serve in those functions. This idea is not just particularly ill-premised, it is maliciously ableist, classist, and racist, and mirrors the already ongoing dismantling of social services globally. Our communities globally are suffering from lack of investments in those communities because of generations of class, colonial, and racial oppression. A capitalist society only invests in communities to the extent that it in return supports more capital accumulation for them. It wants us to forget that healthcare and wellbeing are universal rights, not privileges. It wants to forget that education is supposed to liberate us and teach us to find community in each other, not to be perfectly shaped into cogs for the capitalist machine. So, of course instead of meaningful investments in our communities to affect social change it wants us to further divert investments towards Big Tech. Chatbots in this context aren’t meant to be real solutions, just a placebo for the masses, and a cover for further dismantling of our social infrastructure. Put Big Tech in charge of education and it almost surely also guarantees further dismantling of Humanities and every other critical pedagogy that is supposed to teach us to resist capitalism, colonization, and oppression."
Screenshot that reads: "The ghost in the machine is us When AI models are trained on writings and other works of people, it is not merely appropriating their labor but also their identities. Imagine a film, a video game, or a video ad that includes an AI-generated character who is Black. The character was created based on limited instructions in the prompt with the AI model filling in all the remaining details. No Black person was hired to play that character nor to write it. The AI model is simply drawing from the ghostly echoes of all the stories and lived experiences of real Black people in its training data. The model nor the person using it to create the character could ever experience the joys or the struggles of being Black, nor can they truly appreciate the history and culture of its peoples. The character wears that identity as a hollow shell, reconstructed from an amalgamation of the entirety of human experience as it is shallowly reflected on the web, and has its strings pulled to say or act in ways that a real Black person might strongly oppose to. This is the new “Digital Blackface”. This is the great displacement of art that tells the stories of its peoples by synthetic content."
Screenshot that reads: "AI for me, data labor for thee Another pernicious aspect of AI data labor dynamics is how they mirror and reify racial capitalism and coloniality, employ global labor exploitation and extractive practices, and reinforce the global north and south divide. While worldwide jobs might be created in certain cases, the workers are typically low paid and deprived of any share of the profit made from technologies built with their labor. These dynamics encompass accruing the benefits of generative AI to privileged populations in western and other rich countries, while data labor is relegated to already marginalized populations, for example, in the global south. Communities that significantly contribute to AI data labor may even find their own linguistic styles being labeled AI-ese and being forced to repeatedly prove their own humanity."
Screenshot that reads: "Grand theft data labor Data labor is defined as “activities that produce digital records useful for capital generation”. Appropriation of data labor includes underpaid crowd work for data labeling and content moderation that are critical for training and operationalizing AI models. It also includes the uncompensated appropriation of works by writer, artists, and programmers created outside of the AI development process that are nonetheless extracted from the Web and fed in as training data to generative AI models. It furthermore includes the user behavior data and other data generated when users interact with and participate on the platforms, i.e., simply by using these systems, they generate more data for the AI model to train on to further automate exactly the type of tasks they are currently performing. It is particularly harmful that these AI technologies developed on appropriated labor is then employed to displace and automate the jobs of those whose labor was appropriated, e.g., artists, coders, and other knowledge workers. This may result in vicious cycles of skill transfer from people to AI models whereby proprietary AI model capabilities continue to improve—by learning from both what the workers produce and from the traces of their personal workflows captured by the AI platforms when workers interact with them—while workers progressively lose their economic value and power."
Screenshot that reads: "Note that this loss of compensation is tied to the perceived simplification of the task and corresponding speculations of productivity boosts. If producing an article using generative AI and then editing it takes similar effort as writing it without these tools, that is of less consequence in the compensation negotiations than the speculated productivity boosts that the dominant AI narratives may have convinced us to buy into. It is exactly for this reason that the “AI hype” is not extraneous to the value proposition of AI, but rather is an integral part of the same package. The key profit-maker here is not the productivity tools, but the social construction of the AI productivity myth that creates the exact conditions that the capitalist class so eagerly desires to renegotiate down the compensation for labor."
Screenshot that reads: "For most, this commodification not only distances them from the crafts they enjoy and moves them to a functional role of lower status but also translates to a significant loss of compensation because their new function is viewed as requiring less-skills that can be performed by any of the surplus of workers available in the market. The surplus from any actual productivity improvements, if any, from the usage of these tools will be collected exclusively by the capitalist class. On the other hand, artists and knowledge workers will be expected to produce more to deserve the same compensation. Many of these roles will also become susceptible to gigification which will further correspond to many in the working-class losing benefits and protections associated with full time work that decades of labor movements have fought to put in place."
Screenshot that reads: "Now, imagine in an alternative universe a group of inventors got together and instead of focusing on the horse and the carriage, redirected their focus entirely on what the driver was doing and built an approximate model of the driver. The model itself is unintelligent and simply tries to predict at each moment what would the driver do, and invariably it often gets it wrong. So, now the job of the driver is no longer to drive the vehicle but to constantly monitor and correct for the model’s mistakes. Instead of discovering the joys of driving an automobile and travelling longer distances more comfortably, the driver now sits atop the same slow horse-drawn carriage constantly supervising the model. This would make for a rather hilarious cyber-punk fiction if not for the fact that it is a rather apt metaphor for what we have come to call as “AI” today." followed by a figure captioned "Are you automating the carriage or the driver?". The figure on the left shows a horse carriage with a driver atop, and on the right shows two possible futures: (i) the driver in an automobile, and (ii) a robot-like figure replacing the driver on the carriage but the driver sits behind holding the rein to the robot.
Screenshot that reads: "It also has the insidious effect of bolstering a technodeterministic view that imagines a fabled race between “humans vs. machines” at the heart of the issue, with Big Tech and Silicon Valley simply acting out in roles preordained to them rather than being active participants in subverting technological progress towards unprecedented wealth and power accumulation. That framing claims with self-righteous indignation that it should be self-evident that technological progress will increasingly make bigger strides and therefore the machine is inevitably destined to surpass human capabilities at some point, and it is society that must constantly evolve to survive the new realities. What is left to debate then? If you do not agree it must be because YOU are a technophobe, a luddite, anti-progress, and anti-AI."
Screenshot that reads: "To summarize, generative AI technologies developed using theft and appropriation of data labor are commodifying the jobs of those whose labor it appropriates, and then acting to provide political cover for the dismantling of social services while also dangerously accelerating anthropogenic climate change that disproportionately impacts vulnerable and marginalized working-class people."
Screenshot of email that reads as follows: Dear SIGIR community, Voting for ACM SIGIR Executive Committee roles has concluded and results are in. Your next Executive Committee will be: Nicola Ferro, Chair (nicola.ferro@unipd.it) Yi Zhang, Vice Chair (yiz@soe.ucsc.edu) Bhaskar Mitra, Secretary (bhaskar.mitra@acm.org) Chirag Shah, Treasurer (chirags@uw.edu) along with Vanessa Murdock as Past Chair. Congratulations to all!