Nonhuman humanitarianism: when 'AI for good' can be harmful
Open accessAbstract
Artificial intelligence (AI) applications have been introduced in humanitarian operations in order to help with the significant challenges the sector is facing. This article focuses on chatbots which have been proposed as an efficient method to improve communication with, and accountability to affected communities. Chatbots, together with other humanitarian AI applications such as biometrics, satellite imaging, predictive modelling and data visualisations, are often understood as part of the wider phenomenon of ‘AI for social good’. The article develops a decolonial critique of humanitarianism and critical algorithm studies which focuses on the power asymmetries underpinning both humanitarianism and AI. The article asks whether chatbots, as exemplars of ‘AI for good’, reproduce inequalities in the global context. Drawing on a mixed methods study that includes interviews with seven groups of stakeholders, the analysis observes that humanitarian chatbots do not fulfil claims such as ‘intelligence’. Yet AI applications still have powerful consequences. Apart from the risks associated with misinformation and data safeguarding, chatbots reduce communication to its barest instrumental forms which creates disconnects between affected communities and aid agencies. This disconnect is compounded by the extraction of value from data and experimentation with untested technologies. By reflecting the values of their designers and by asserting Eurocentric values in their programmed interactions, chatbots reproduce the coloniality of power. The article concludes that ‘AI for good’ is an ‘enchantment of technology’ that reworks the colonial legacies of humanitarianism whilst also occluding the power dynamics at play.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The term AI for social good is deeply contested because what is good for one group may not be good for others.
“According to Madianou, the term ' AI for social good ' is deeply contested, because ' What is good for one group, may not be good for others ' (2021, 853).”
From A decolonial approach to AI in higher education teaching and learning: strategies for undoing the ethics of digital neocolonialism (2023) · cites this paper as Madianou 2021
The claim of AI for social good is rooted in mythology and colonial legacies.
“the claim of ' AI for social good ' is rooted in mythology and colonial legacies (Madianou 2021).”
From A decolonial approach to AI in higher education teaching and learning: strategies for undoing the ethics of digital neocolonialism (2023) · cites this paper as Madianou 2021
A decolonial approach to AI for social good reveals power asymmetries underpinning AI for social good initiatives and highlights the colonial ethics and rationality embedded in the measurement and classification systems of the empire to control colonial subjects.
“Hence a decolonial approach to AI for social good, argues Madianou (2021), does not only reveal the power asymmetries underpinning ' AI for social good ' initiatives, but also highlights the colonial ethics and rationality that has always been embedded in the measurement and classi fi cation systems of the empire in order to control colonial subjects (see Mbembe 2017).”
From A decolonial approach to AI in higher education teaching and learning: strategies for undoing the ethics of digital neocolonialism (2023) · cites this paper as Madianou 2021
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 59 · Cited by 91
Research works printed but not linked: 31
- Amoore, L. (2020). Cloud ethics. Algorithms and the attributes of ourselves and others. Duke University Press.
- Appadurai, A. (1993). Number in the postcolonial imagination. In C. Breckenridge & P. Van der Veer (Eds.), Orientalism and the postcolonial predicament (pp. 314-339). University of Pennsylvania Press.
- Bakardjieva, M. (2015). Rationalizing sociality: An unfinished script for socialbots. The Information Society, 31(3), 244-256. https://doi.org/10.1080/01972243.2015.1020197 · DOI
- Barassi, V. (2020). Child, data, citizen. MIT Press.
- Bolter, J. D., & Grusin, R. (2000). Remediation. MIT Press.
- boyd, d., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662-679. https://doi.org/10.1080/1369118X.2012.678878 · DOI · Paper page
- Broussard, M. (2018). Artificial unintelligence. MIT Press.
- Burrell, J., & Oreglia, E. (2015). The myth of market price information: Mobile phones and the application of economic knowledge in ICTD. Economy and Society, 44(2), 271-292. https:// doi.org/10.1080/03085147.2015.1013742 · DOI
- Calhoun, C. (2008). The imperative to reduce suffering. In M. Barnett & T. G. Weiss (Eds.), Humanitarianism in question (pp. 73-97). Cornell University Press.
- Chouliaraki, L. (2013). The Ironic Spectator: Solidarity in the age of post-humanitarianism. Cambridge, UK: Polity.
- Chun, W. H. K. (2011). Programmed visions: Software and memory. MIT Press.
- Eubanks, V. (2017). Automating inequality. How high-tech tools profile, police and punish the poor. St Martin's Press.
- Gebru, T. (2020). Race and gender. In M. Dubber, F. Pasquale, & S. Das (Eds.), The Oxford hand- book of ethics of AI (pp. 253-270). Oxford University Press.
- Gehl, R. W., & Bakadjieva, M. (2017). Socialbots and their friends. In R. W. Gehl & M. Bakadjieva (Eds.), Socialbots and their friends: Digital media and the automation of sociality (pp. 1-16). Routledge.
- Gell, A. (1992). The technology of enchantment and the enchantment of technology. In J. Coote & A. Shelton (Eds.), Anthropology, art and aesthetics (pp. 40-66). Clarendon.
- Gunkel, D. (2018). Ars ex machina: Rethinking responsibility in the age of creative machines. In A. Guzman (Ed.), Human-Machine communication (pp. 221-236).
- Kember, S., & Zylinska, J. (2012). Life after new media. Mediation as a vital process. MIT Press.
- Krause, M. (2014). The good project. Chicago University Press.
- Lester, A., & Dussart, F. (2014). Colonization and the origins of humanitarian governance. Cambridge University Press.
- Madianou, M. (2019a). Technocolonialism: Digital innovation and data practices in the humani- tarian response to refugee crises. Social Media & Society, 5(3), 1-13. https://doi.org/10.1177/ 2056305119863146 · DOI
- Madianou, M. (2019b). The biometric assemblage: Surveillance, experimentation, profit, and the measuring of refugee bodies. Television & New Media, 20(6), 581-599. https://doi.org/10.1177/ 1527476419857682 · DOI · Paper page
- Madianou, M. (2021). Technological futures as colonial debris: Tech-for-good as technocolonial- ism. In Zylinska, J. (Ed.), The Future of Media. London, UK: Goldsmiths Press.
- Madianou, M., Ong, J. C., Longboan, L., & Cornelio, J. S. (2016). The appearance of accountability: Communication technologies and power asymmetries in humanitarian aid and disaster recov- ery. Journal of Communication, 66(6), 960-981. https://doi.org/10.1111/jcom.12258 · DOI
- Mbembe, A. (2017). Critique of black reason. Duke University Press.
- McDonald, S. (2019, August 12). From space to supply chain: Humanitarian data governance. https://ssrn.com/abstract=3436179 or http://dx.doi.org/10.2139/ssrn.3436179 · DOI
- Noble, S. (2018). Algorithms of oppression. New York University Press.
- Nothias, T. (2020). Access granted: Facebook's free basics in Africa. Media, Culture & Society, 42 (3), 329-348. https://doi.org/10.1177/0163443719890530 · DOI
- Pasquale, F. (2015). The Black Box society. Cambridge University Press.
- Petryna, A. (2009). When experiments travel. Clinical trials and the global search for human sub- jects. Princeton University Press.
- Quijano, A. (2000). Coloniality of power and eurocentrism in latin america. International Sociology, 15(2), 215-232. https://doi.org/10.1177/0268580900015002005 · DOI · Paper page
- Shi, Z. R., Wang, C., & Fang, F. (2020). Artificial intelligence for social good: A survey. ArXiv [pre- print]. arXiv:2001.01818 [cs.CY]. · arXiv
- Authors
- Madianou, M.
- Affiliations
- Goldsmiths University of London (GB)
- Corresponding author
- Madianou, Mirca
- ORCID
- Madianou, Mirca
- Venue
- Information, Communication & Society
- Publisher
- Informa UK Limited
- Published
- 2021-04-08
- Volume
- 24
- Issue
- 6
- Pages
- 850-868
- ISSN
- 1369-118X (ISSN-L)
- 1468-4462
- OpenAlex
- W3154916686
- Cited by
- 91
- References
- 59
- Citations by year
- 2026: 13 · 2025: 28 · 2024: 34 · 2023: 4 · 2022: 7 · 2021: 5
- FWCI
- 15.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Information Communication & Society (publishedVersion, other-oa)
- Goldsmiths (University of London) (acceptedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Arts and Humanities › History › Historical and Contemporary Political Dynamics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Cybernetics and Technology in Society
- Keywords
- humanitarianism, artificial intelligence, chatbots, AI for Good, decolonial critique, coloniality of power, power imbalance, humanitarian communication, affected communities, misinformation, Eurocentrism, humanitarian technology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review