The DOI matches its Crossref record; 71 of the 74 references it cites were found in a registry or an index; none of the 64 sources its claims cite is checked for support yet.
Algorithmic injustice: a relational ethics approach
Open accessAbstract
It has become trivial to point out that algorithmic systems increasingly pervade the social sphere. Improved efficiency-the hallmark of these systems-drives their mass integration into day-to-day life. However, as a robust body of research in the area of algorithmic injustice shows, algorithmic systems, especially when used to sort and predict social outcomes, are not only inadequate but also perpetuate harm. In particular, a persistent and recurrent trend within the literature indicates that society's most vulnerable are disproportionally impacted. When algorithmic injustice and harm are brought to the fore, most of the solutions on offer (1) revolve around technical solutions and (2) do not center disproportionally impacted communities. This paper proposes a fundamental shift-from rational to relational-in thinking about personhood, data, justice, and everything in between, and places ethics as something that goes above and beyond technical solutions. Outlining the idea of ethics built on the foundations of relationality, this paper calls for a rethinking of justice and ethics as a set of broad, contingent, and fluid concepts and down-to-earth practices that are best viewed as a habit and not a mere methodology for data science. As such, this paper mainly offers critical examinations and reflection and not "solutions."
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
Birhane argues that algorithmic injustice is perpetuated by technical fixes that fail to center disproportionately impacted communities, and proposes a shift from rational to relational ethics. Relational ethics grounds justice and ethics in context, history, lived experience, and power asymmetries rather than technical solutions.
Key findings
- The paper contends that dominant responses to algorithmic harm—such as debiasing datasets and fairness metrics—are inadequate because they treat bias as a static deviation from a “correct” description and obscure structural, historical, and power asymmetries.
- It proposes four tenets of relational ethics: knowing that centers human relations and the disproportionately impacted; bias is not a deviation from the “correct” description; prioritizing understanding over prediction; and treating data science as a practice that alters the social fabric.
- It claims marginalized groups are disproportionately harmed by algorithmic systems and hold epistemic privilege to recognize harm, citing examples including object detection, recidivism, gender recognition/classification, and medicine.
- It calls for ethics as a down-to-earth habit and practice, not a mere methodology or tool, and states that this work is never done.
Methods
- Design: critical, conceptual, and reflective essay; no empirical study, dataset, or sample is used.
- Main analysis: comparative and philosophical argument drawing on Afro-feminist epistemology, enactive cognitive science, ubuntu, Bakhtinian dialogism, and complexity science to contrast rationality and relationality.
- No quantitative analysis or original numerical results are reported.
Limitations
- The paper states it mainly offers critical examinations and reflection and not “solutions”; it is not a set of general guidelines, principles, or out-of-the-box tools.
- The paper states relational ethics is not a rationally constructed abstract “theory of ethics” and does not provide implementable remedies.
- Evident from the text: no empirical validation, case study, or measurement is provided; the framework is conceptual and programmatic.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
60 Not checked yet · 23 No basis given · 17 Own interpretation
In the paper
“Pushing back against the dominant Western orthodoxy, Afro-feminist epistemology grounds knowing in an active and engaged practice.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
What the sources say
- Not checked yetcites [31] Collins, 2002
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In the paper
“Mhlambi11 has further argued that the AI industry, grounded in rationality, reproduces harmful and discriminatory outcomes.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Rationality: the dominant orthodoxy
What the sources say
- Not checked yetcites [11] Mhlambi, 2020, From rationality to relationality: ubuntu as an ethical and human rights framework for artificial intelligence governance
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In the paper
“The reality of the Western straight white male then masquerades as the invisible background that is taken as the “normal,” “standard,” or “universal” position.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Rationality: the dominant orthodoxy
What the sources say
- Not checked yetcites [17] Ahmed, 2007, A phenomenology of whiteness, Femin. Theor. source
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In the paper
“a persistent and recurrent trend within the literature indicates that society's most vulnerable are disproportionally impacted.”
§ Algorithmic injustice: a relational ethics approach > Abstract
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In the paper
“In contrast, for Afro-feminist thought, the researcher is an important participant in the knowledge production process.33”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
What the sources say
- Not checked yetcites [33] Nnaemeka, 2004, Nego-feminism: theorizing, practicing, and pruning Africa’s way, Signs source
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In the paper
“Unjust and harmful outcomes, as a result, are treated as side effects that can be treated with technical solutions such as “debiasing” datasets8 rather than problems that have deep roots in the mathematization of ambiguous and contingent issues, historical inequalities, and asymmetrical power hierarchies or unexamined problematic assumptions that infiltrate data practices.”
§ Algorithmic injustice: a relational ethics approach > Introduction
What the sources say
- Not checked yetcites [8] Gonen, 2019, Lipstick on a pig: debiasing methods cover up systematic gender biases in word embeddings but do not remove them, arXiv
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In the paper
“The intrinsically political tasks of categorizing and predicting things such as “acceptable” behavior, “ill” health, and “normal” body type then pass as apolitical technical sorting and categorizing tasks.7”
§ Algorithmic injustice: a relational ethics approach > Introduction
What the sources say
- Not checked yetcites [7] Bowker, 2000
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In the paper
“Data science, and the wider discipline of computer science, have implicitly or explicitly inherited this worldview.11”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Rationality: the dominant orthodoxy
What the sources say
- Not checked yetcites [11] Mhlambi, 2020, From rationality to relationality: ubuntu as an ethical and human rights framework for artificial intelligence governance
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In the paper
“according to the sub-Saharan tradition of ubuntu as encapsulated by Mbiti's28 phrase “I am because we are, and since we are, therefore I am,” a person comes into being through the web of relations.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
What the sources say
- Not checked yetcites [28] Mbiti, 1969
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In the paper
“socially and politically contested matters that were traditionally debated in the open are now reduced to mathematical problems with a technical solution.6”
§ Algorithmic injustice: a relational ethics approach > Introduction
What the sources say
- Not checked yetcites [6] McQuillan, 2020, Non-fascist AI
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In the process of data collection, for example, the data scientist decides what is worth measuring (making some things visible and others invisible by default) and how.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
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In the paper
“Wisdom, and not “book learning,” enables one to resist oppression.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
What the sources say
- Not checked yetcites [31] Collins, 2002
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The rational worldview that aspires to an “objective” knowledge from a “God's eye view” has resulted in the treatment of the researcher as invisible, and their interests, motivations, and background as inconsequential.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
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In the paper
“In the desire to establish timeless and absolute knowledge, abstract and contextless reasoning is prioritized over concrete lived experience submersed in co-relations, interdependence, fluidity, and connectedness.16”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Rationality: the dominant orthodoxy
What the sources say
- Not checked yetcites [16] Merleau-Ponty, 1968
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In the paper
“Knowledge claims are not worked out in isolation from others but are developed in dialog with the community.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
What the sources say
- Not checked yetcites [31] Collins, 2002
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Relational frameworks emphasize the primacy of relations and dependencies.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Knowledge, according to this worldview, is rooted in the ideal rational, static, self-contained, and self-sufficient subject that contemplates the external world from afar in a “purely cognitive” manner as a disembodied and disinterested observer.15”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Rationality: the dominant orthodoxy
What the sources say
- Not checked yetcites [15] Gardiner, 1998, The incomparable monster of solipsism: Bakhtin and Merleau-Ponty
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In the paper
“Knowing and being are active processes that are necessarily political and ethical.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
What the sources say
- Not checked yetcites [31] Collins, 2002
plaincorrelational- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“algorithmic systems, especially when used to sort and predict social outcomes, are not only inadequate but also perpetuate harm.”
§ Algorithmic injustice: a relational ethics approach > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“However, despite the association of Bayes with rational predictions, Bayesian models are prone to spurious relationship and amplification of socially held stereotypes.24”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Rationality: the dominant orthodoxy
What the sources say
- Not checked yetcites [24] Pager, 2009, Bayesian bigot? Statistical discrimination, stereotypes, and employer decision making, Ann. Am. Acad. Polit. Soc. Sci. source
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Subsequently, discussions and understanding of concepts such as knowledge and ethics tend to be abstract, genderless, contextless, and raceless.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Rationality: the dominant orthodoxy
plain- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“can be exemplified by the deep contention that reason and logical coherence are superior for knowledge production (in understanding the world) above and beyond relational and embodied becoming.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Rationality: the dominant orthodoxy
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Since the rational worldview has come to be seen as the standard, anything outside of this is viewed as an outlier.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Rationality: the dominant orthodoxy
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“A proponent of this position, Hanne De Jaegher,35 contends that our most sophisticated human knowing lies in how we engage with each other.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
What the sources say
- Not checked yetcites [35] De Jaegher, 2019, Loving and knowing: reflections for an engaged epistemology, Phenomenol. Cogn. Sci.
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In the paper
“The privileging of reason as the ultimate criterion makes knowing a distant act.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Rationality: the dominant orthodoxy
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In the paper
“Far from a distant and “objective” discretizing logic, knowing is an activity that happens in the relationship between the knower and the known.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
What the sources say
- Not checked yetcites [35] De Jaegher, 2019, Loving and knowing: reflections for an engaged epistemology, Phenomenol. Cogn. Sci.
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In the paper
“Kyselo,27 for example, contends that the self is social through and through—it is co-generated in interactions and relations with others.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
What the sources say
- Not checked yetcites [27] Kyselo, 2014, The body social: an enactive approach to the self, Front. Psychol. source
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In the paper
“Current data practices, for the most part, follow the rational model of thinking where data are assumed to represent the world out there in a “neutral” way.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
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In the paper
“In the process of data cleaning, rich information that provides context about which data are collected and how datasets are structured is stripped away.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
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In the paper
“Ahmed17 contends that all bodies inherit history and the inheritance of Cartesianism is grounded in a white straight ontology.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Rationality: the dominant orthodoxy
What the sources say
- Not checked yetcites [17] Ahmed, 2007, A phenomenology of whiteness, Femin. Theor. source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Any data scientist working to automate issues of a social nature, in effect, is engaged in making moral and ethical decisions”
§ Algorithmic injustice: a relational ethics approach > Introduction
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In the paper
“The rational view serves as the backbone for much of Western science and philosophy, permeating most fields of enquiry”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Rationality: the dominant orthodoxy
What the sources say
- Not checked yetcites [11] Mhlambi, 2020, From rationality to relationality: ubuntu as an ethical and human rights framework for artificial intelligence governance
- Not checked yetcites [12] Terry, 1986
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In the paper
“Bakhtin29 emphasized that nothing is simply itself outside the matrix of relations in which it exists.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
What the sources say
- Not checked yetcites [29] Bakhtin, 1984
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In the paper
“The most reliable form of knowledge, especially concerning social and historical injustice, is grounded in lived experience.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
What the sources say
- Not checked yetcites [31] Collins, 2002
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In the paper
“At the heart of it, the Afro-feminist approach to knowing contends that concrete experiences are primary and abstract reasoning secondary.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
What the sources say
- Not checked yetcites [31] Collins, 2002
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In the paper
“The mathematization and formalization of social issues brings with it a veneer of objectivity and positions its operations as value-free, neutral, and amoral.”
§ Algorithmic injustice: a relational ethics approach > Introduction
What the sources say
- Not checked yetcites [7] Bowker, 2000
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In the paper
“Data science and data practices reincarnate rationalism in many forms, including in the manner in which messiness, ambiguity, and uncertainty are not tolerated”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Rationality: the dominant orthodoxy
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In the paper
“Distant statistics or theoretical accuracies do not take precedence over the actual experience of a person.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
What the sources say
- Not checked yetcites [31] Collins, 2002
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In the paper
“The deep quest for the rational worldview is certainty, stability, and order, and thus isolation, separation, and clear binaries form the foundations in place of connectedness, interdependence, and dynamic relation.13”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Rationality: the dominant orthodoxy
What the sources say
- Not checked yetcites [13] Prigogine, 1984
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In the paper
“Selfhood and knowledge are evolving and dynamic; the self is never finished—it is an open book.30”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
What the sources say
- Not checked yetcites [30] Birhane, 2017, Descartes was wrong:‘a person is a person through other persons’, Aeon
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Not only does the inheritance of rationality to data sciences and computation make these fields inadequate to deal with complex and inherently indeterminable phenomena”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Rationality: the dominant orthodoxy
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Contrary to the rationalist and individualist worldview, relational perspectives view existence as fundamentally co-existent in a web of relations.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“Any generalization or theory constructed in the absence of deep understanding, not grounded in the concrete and particular, is vacuous.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Prioritizing of understanding over prediction
What the sources say
- Not checked yetcites [58] Geertz, 1973
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“machine learning presents an extreme case of a very human predicament, which is that the only way we can generalize is on the basis of past experience.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Prioritizing of understanding over prediction
What the sources say
- Not checked yetcites [61] Gross
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In a supposedly objective worldview, bias, injustice, and discrimination are (mis)conceived as being able to be permanently corrected.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Bias is not a deviation from the “correct” description
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
None of the sources cited for this claim have been checked yet.
In the paper
“Furthermore, gender classification systems often operate under essentialist assumptions and operationalize gender in a trans-exclusive way, resulting in disproportionate harm to trans people.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Knowing that centers human relations
What the sources say
- Not checked yetcites [48] Hamidi, 2018, Gender recognition or gender reductionism? The social implications of embedded gender recognition systems
- Not checked yetcites [50] Keyes, 2018, The misgendering machines: trans/HCI implications of automatic gender recognition, Proc. ACM Hum.-Comput. Interact.
plaindirect- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Eventness is always particular, and never exhaustively describable in terms of rules.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Prioritizing of understanding over prediction
What the sources say
- Not checked yetcites [59] Morson, 1989
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“The machine-detected patterns often provide “answers” to fuzzy, contingent, and open-ended questions. These “answers” neither reveal any causal relations nor provide explanation on why or how.65”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Data science as a practice that alters the social fabric
What the sources say
- Not checked yetcites [65] Pasquale, 2015
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“our deepest intuitions about all sorts of things, and in particular justice and injustice, can change dramatically.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Prioritizing of understanding over prediction
What the sources say
- Not checked yetcites [61] Gross
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For any individual person, community, or situation, algorithmic classifications and predictions give either an advantage or they hinder.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Data science as a practice that alters the social fabric
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Humans are inherently historical, social, cultural, gendered, politicized, and contextualized organisms.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“Proposing an understanding of human knowing in analogy with loving, De Jaegher argues that in knowing, like loving, what happens is not neutral, general, or universal.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
What the sources say
- Not checked yetcites [35] De Jaegher, 2019, Loving and knowing: reflections for an engaged epistemology, Phenomenol. Cogn. Sci.
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“neither people nor the environment and context in which they are embedded are static.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Bias is not a deviation from the “correct” description
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The rationalist tradition's tendency toward timeless and generalizable knowledge aspires to establish timeless laws and generalizable theories.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Prioritizing of understanding over prediction
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“Not only do machine-learning systems detect patterns and cluster similarities, they also make predictions based on the observed patterns.62”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Prioritizing of understanding over prediction
What the sources say
- Not checked yetcites [62] O’Neil, 2013
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Nonetheless, the relational versus rational divide is not something that can be clearly demarcated, but overlaps with fuzzy boundaries.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Likewise, the historian of science Lorraine Daston contends that the endeavor for a universal law is a predicament that does not stand against unanticipated particulars, since no universal ever fits the particulars.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Prioritizing of understanding over prediction
What the sources say
- Not checked yetcites [60] Daston, 2018, Calculation and the division of labor, 1750-1950, Bull. German Hist. Inst.
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Since knowing is a relational affair, it matters who enters into the knower-known relations.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Knowing that centers human relations
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Machine-learning systems “work” by identifying patterns in vast amounts of data.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Prioritizing of understanding over prediction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“What society deems fair and ethical changes over time and with context and culture.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Bias is not a deviation from the “correct” description
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“To understand people, we must take into account “unrepeatable contextual meaning.””
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Prioritizing of understanding over prediction
What the sources say
- Not checked yetcites [59] Morson, 1989
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“Living bodies, according to Di Paolo et al.,37 are processes, practices, and networks of relations which have “more in common with hurricanes than with statues.””
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
What the sources say
- Not checked yetcites [37] Di Paolo, 2018
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Machine learning, at its core, is a tool that predicts. It reveals statistical correlations with no understanding of causal mechanisms.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Prioritizing of understanding over prediction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“While the latter approaches often neglect those at the margins53 and shy away from power asymmetries and structural inequalities that permeate the social world”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Knowing that centers human relations
What the sources say
- Not checked yetcites [53] Harrington, 2020, The forgotten margins: what is community-based participatory health design telling us?, Interactions source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The findings of Wilson et.al.,44 for instance, demonstrate that object detection systems designed to predict pedestrians display higher error rates identifying dark-skinned pedestrians while light-skinned pedestrians are identified with higher precision.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Knowing that centers human relations
What the sources say
- Not checked yetcites [44] Wilson, 2019, Predictive inequity in object detection, arXiv
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Accordingly, their knowing and understanding of the world around them necessarily takes place through their respective lenses.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although datasets are often part of the problem, this commonly held belief relegates deeply rooted societal and historical injustices, nuanced power asymmetries, and structural inequalities to mere datasets.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Bias is not a deviation from the “correct” description
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Concepts such as bias, fairness, and justice, however, are moving targets.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Bias is not a deviation from the “correct” description
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, underlying the idea of “fixing” bias is the assumption that there exists a single correct description of reality where a deviation from it has resulted in bias.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Bias is not a deviation from the “correct” description
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
““Our most sophisticated knowing,” according to De Jaegher, “is full of uncertainty, inconsistencies, and ambiguities.””
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
What the sources say
- Not checked yetcites [35] De Jaegher, 2019, Loving and knowing: reflections for an engaged epistemology, Phenomenol. Cogn. Sci.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Before we delve into that, it is worth reemphasizing that while the rational worldview tends to see knowledge, people, and reality in general as stable, for relational perspectives, we are fluid, active, and continually becoming.”
§ Algorithmic injustice: a relational ethics approach > Relational ethics: the roots > Relationality
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The harm, bias, and injustice that emerge from algorithmic systems varies and is dependent on the training and validation data used, the underlying design assumptions, and the specific context in which the system is deployed, among other factors.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Knowing that centers human relations
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“Bakhtin argues that such attempts to abstract general rules from particulars “loses the most essential thing about human activity, the very thing in which the soul of morality is to be found,” which Bakhtin calls the “eventness” of the event.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Prioritizing of understanding over prediction
What the sources say
- Not checked yetcites [59] Morson, 1989
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“These systems process people's behaviors, actions, and the social world at large.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Data science as a practice that alters the social fabric
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The core of what machine-learning systems do can be exemplified as clustering similarities and differences, abstracting commonalities, and detecting patterns.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Prioritizing of understanding over prediction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the most marginalized hold the epistemic privilege to recognize harm and injustice”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Knowing that centers human relations
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Some identities (and not others) are recognized as a pedestrian,44 or fit for an STEM career,69 or in need of medical care.1 Some are ignored and made invisible altogether.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Data science as a practice that alters the social fabric
What the sources say
- Not checked yetcites [44] Wilson, 2019, Predictive inequity in object detection, arXiv
- Not checked yetcites [69] Lambrecht, 2019, Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads, Manag. Sci. source
- Not checked yetcites [1] Obermeyer, 2019, Dissecting racial bias in an algorithm used to manage the health of populations, Science source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Seemingly banal tasks such as identifying and predicting “employable” or “criminal” characteristics carry grave consequences for those that do not conform to the status quo.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Data science as a practice that alters the social fabric
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The process of categorizing, sorting, and generalizing, therefore, is far from a mere technical task.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Data science as a practice that alters the social fabric
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Algorithmic systems never emerge in a social, historical, and political vacuum, and to divorce them from the contingent background in which they are embedded is erroneous.”
§ Algorithmic injustice: a relational ethics approach > Closing remarks
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Certain patterns are made visible and types of being objectified while other types are erased.”
§ Algorithmic injustice: a relational ethics approach > Ethics built on the foundations of relationality > Data science as a practice that alters the social fabric
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
The paper relies on this without support.
Most solutions on offer for algorithmic injustice revolve around technical solutions.
“most of the solutions on offer (1) revolve around technical solutions”
The paper relies on this without support.
Most solutions on offer for algorithmic injustice do not center disproportionally impacted communities.
“and (2) do not center disproportionally impacted communities.”
The paper relies on this without support.
Automated and standardized solutions to complex social issues often contribute more harm than good.
“automated and standardized solutions to complex and contingent social issues often contribute more harm than good”
The paper relies on this without support.
Algorithmic systems increasingly pervade the social sphere.
“algorithmic systems increasingly pervade the social sphere.”
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“concerns over GenAI's sustainability also intersect with social justice issues, as communities that already suffer environmental disadvantages stand to be disproportionately harmed [16]”
From Understanding the Practices, Perceptions, and (Dis)Trust of Generative AI among Instructors: A Mixed-methods Study in the U.S. Higher Education (2025) · cites this paper as [16]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Nevertheless, Birhane (2021) argues that Western AI often imposes rigid, decontextualized systems that clash with the communal and contextual ways knowledge is held in Indigenous cultures.”
From Data colonialism and indigenous languages in AI: a critical review of existing initiatives and their struggles with data sovereignty (2026) · cites this paper as Birhane 2021
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This concentration of agenda-setting power and dominance of a small set of corporate actors risks narrowing the imaginaries of AI futures, privileging commercial interests over broader social concerns [6, 8, 21].”
From Reimagining Open Source and Openness in AI: Co-Creating Responsible Technological Futures (2026) · cites this paper as [8]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The current AI field is shaped by rapid cycles of innovation and hype, with dominant generative AI narratives and agendas largely led by a narrow set of powerful actors (particularly large technology firms, but also influential standards bodies and platform owners) who exercise disproportionate influence in how openness is defined, operationalized, and legitimized.”
From Reimagining Open Source and Openness in AI: Co-Creating Responsible Technological Futures (2026) · cites this paper as [8]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“AI fairness scholarship has already underscored the need to examine power distribution and structures of exclusion inhabited by AI technologies [15, 93].”
From AI and Non-Western Art Worlds: Reimagining Critical AI Futures through Artistic Inquiry and Situated Dialogue (2025) · cites this paper as [15]
In the citing paper
“These systems have been shown to reinforce social and historical human prejudices and people who are traditionally marginalized in our society are disproportional negatively impacted [8].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [8]
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Beyond mitigation: building a case for collective rights and remedies for digital ID-enabled harms
Cioffi, K. (2024) · Information Communication & Society · doi:10.1080/1369118x.2024.2420023 · Cited by 3
A Preliminary Framework for Intersectionality in ML Pipelines
Turcios, M. N., Boyd, A. E., Smith, A. D. R., & Johnson, B. (2025) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3696630.3728692 · Cited by 4
Digital Footprint and Education: Some Remarks
Balyakin, A. A., Мамонов, М. В., Nurbina, M. V., & Taranenko, S. B. (2021) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-5063-5_40 · Cited by 3
Moriarty, C. M. (2022) · Studies in History and Philosophy of Science Part A · doi:10.1016/j.shpsa.2022.08.009 · Cited by 3
“See Birhane (2021).”
Introducing Prolog in Language-Informed Ways
Dahl, V., & Cecchi, L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35254-6_13 · Cited by 3
Data Ethics Club: Creating a collaborative space to discuss data ethics
Cara, N. H. D., Zelenka, N., Day, H., Bennet, E. D., Hanschke, V. A., Maggio, V., Michalec, O., Radclyffe, C. J. T., Shkunov, R., Tonkin, E. L., et al. (2022) · Patterns · doi:10.1016/j.patter.2022.100537 · Cited by 6
Aristotle and AI in Education: Virtue, Wisdom, Human Flourishing and the Common Good
Makrakis, V. (2026) · Encyclopedia · doi:10.3390/encyclopedia6060116 · Cited by 4
'The plan is just survival': Data Work in Kenya and the Regime of Entrapment
Kapania, S., Yang, T., Abdelkadir, N. A., Scheuerman, M. K., Miceli, M., Taylor, A. S., & Fox, S. E. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791097 · Cited by 2
“While critical AI studies scholarship has devoted extensive attention to the harms that surface at the point of deployment [13, 37, 117], an emerging line of research has begun to interrogate the infrastructures [16, 81], labor [43, 51, 120], and organizational processes that make AI systems…”
Sumpter, L., & Sumpter, D. J. T. (2024) · Journal of Humanistic Mathematics · doi:10.5642/jhummath.kjeo2817 · Cited by 2
Bode, I., & Huelss, H. (2024) · Open Research Europe · doi:10.12688/openreseurope.16536.2 · Cited by 2
“This development raises crucial regulatory questions because of the societal harms that such automated and algorithmic ‘solutions’ can produce, often reinforcing existing power asymmetries and hierarchies (Birhane, 2021b).”
Karimov, F. E., & Saidolimov, S. (2024) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite60783.2024.10616762 · Cited by 2
Boundary-making practices: LLMs and an artifactual production of objectivity
An, M. (2025) · AI & Society · doi:10.1007/s00146-025-02409-4 · Cited by 4
“AI ethics frameworks often presume that algorithmic bias can be optimized away (Birhane 2021; Selbst et al. 2019; Waseem et al. 2021), thus achieving a form of technological objectivity.”
Shi, L. P. (2025) · Learning Media and Technology · doi:10.1080/17439884.2025.2565244 · Cited by 2
Cultural diversity and artificial intelligence
Siddiqi, M. (2025) · American Journal of STEM Education · doi:10.32674/rnayrn64 · Cited by 3
“(Birhane, 2021; Mohamed et al., 2020).”
InfluentialAuala, S., Mbinge, U., Ndjao, L., van Niekerk, K. C., Nathingo, N., Nehoya, R., Smith, R. C., Rodil, K., Kapuire, G. K., Maasz, D., et al. (2025) · Proceedings of the Fifth Biennial African Human-Computer Interaction Conference · doi:10.1145/3757232.3757268 · Cited by 2
AI Empire: The Rise of Technocolonialism
Uddin, M. M. (2025) · The Journal of Imperial & Commonwealth History · doi:10.1080/03086534.2025.2595285 · Cited by 2
Toward substantive intersectional algorithmic fairness: desiderata for a feminist approach
Mirsch, M., Wegner, L., Strube, J., & Leicht-Scholten, C. (2026) · AI and Ethics · doi:10.1007/s43681-025-00926-y · Cited by 3
“Transformative Potential 10 Recognizing algorithmic systems’ opportunity for intersectionality beyond critique.”
Chougule, M. (2026) · Transportation Research Interdisciplinary Perspectives · doi:10.1016/j.trip.2025.101801 · Cited by 2
Aboderin, O. S. (2025) · Journal of Educational Studies · doi:10.59915/jes.2025.si1.1 · Cited by 3
Maccaro, A. (2026) · AI & Society · doi:10.1007/s00146-026-02854-9 · Cited by 5
“Participatory design approaches—well established in contemporary critical data studies (Lehtiniemi and Haapoja 2020; Birhane 2021)—offer one pathway by ensuring that communities and clinicians can shape the categories and priorities embedded in algorithmic systems.”
Navigating Digital Colonialism: A Postcolonial Approach to AI and Global Justice
Choi, M. (2026) · Religious Education · doi:10.1080/00344087.2026.2627112 · Cited by 2
“However, AI systems do not emerge in a vacuum; they are shaped by human decisions, cultural assumptions, historical inequalities, and economic interests (2021).”
InfluentialFousiani, K., Cremer, L., Çetin, M., & Raeder, S. (2026) · PLoS ONE · doi:10.1371/journal.pone.0340860 · Cited by 2
“Finally, Birhane emphasized the related ethical implications, advocating for a relational and context-sensitive approach to algorithmic management to counteract inherent biases and ensure fairer outcomes for workers [28].”
LLM Alignment should go beyond Harmlessness–Helpfulness and incorporate Human Agency
Naseem, U., Chakraborty, T., Chang, K.-W., Dras, M., Nakov, P., Peng, N., & Poria, S. (2026) · Cognitive Computation · doi:10.1007/s12559-026-10568-9 · Cited by 2
From 'objectivity' to obedience: LLMs as discourse, discipline, and power
Kouros, T. (2026) · AI & Society · doi:10.1007/s00146-026-02994-y · Cited by 3
“Against this backdrop, scholars such as Birhane (2021) and Benjamin (2019) call for relational, contextual, and justice-oriented epistemologies: ways of knowing that foreground interdependence, situatedness, and care.”
Generative artificial intelligence for inclusive research
Doan, T. V., & Woosnam, K. M. (2026) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-07-2025-1128 · Cited by 2
Udechukwu, L. M. (2026) · AI and Ethics · doi:10.1007/s43681-026-01069-4 · Cited by 2
Perrin, P. B. (2026) · Digital Society · doi:10.1007/s44206-026-00254-6 · Cited by 2
Alfaro, E. M., Schelotto, M., Zamaro, H. M., Rosa, C. G. S. L., Guitter, M. R., Romero, C., Galluzzo, L., Pinto, N., Peruzzo, L. B., Pagés, C., et al. (2026) · Leukemia & lymphoma/Leukemia and lymphoma · doi:10.1080/10428194.2026.2662496 · Cited by 2
“Walking on bifur-cating paths, thus coming away from imposed algorithms, is still one of the promising anti-entropic qualities of humankind [24,25].”
Mendoza, K. P. R. (2026) · Disability & Society · doi:10.1080/09687599.2026.2667539 · Cited by 3
“I was acutely aware of the tool’s dependence on global data infrastructures powered by precarious labor and energy consumption (Birhane 2021; Crawford 2021).”
InfluentialBig tech, the state, or you? Who is responsible for generative AI addiction?
Stahl, B. C., Ali, R., Hitcham, L., Liebherr, M., Murray, R., Vallejos, E. P., & Yankouskaya, A. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2026.2663509 · Cited by 2
Students’ AI relational identity: conceptualization and applications
Darling‐Aduana, J., Rahimi, M., Margulieux, L. E., & Liao, Y. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1873025 · Cited by 2
HR in the Era of AI Paradoxes: Balancing Automation and Human Touch
Suneetha, S., Sudheer, A. K., Shankar, C., & Metta, S. (2024) · Advances in economics, business and management research/Advances in Economics, Business and Management Research · doi:10.2991/978-94-6463-433-4_40 · Cited by 8
AL-Shboul, M. A. (2024) · International Journal of Social Economics · doi:10.1108/ijse-10-2023-0763 · Cited by 2
Schöpke-Gonzalez, A., Wyss-Gallifent, J., Brissey, C., Jordan, S. B., & Hemphill, L. (2024) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3678884.3687140 · Cited by 2
Robinson, K. P., Garvin, M. B., Eglash, R., Robert, L., Guzdial, M., & Bennett, A. (2024) · Journal of integrated global STEM · doi:10.1515/jigs-2024-0007 · Cited by 2
Using critical race mixed methodology to study multiply marginalized youth of color in schools
DeCuir‐Gunby, J. T., & Johnson, R. M. (2024) · Journal of School Psychology · doi:10.1016/j.jsp.2024.101416 · Cited by 2
Brakel, R. E. V. (2022) · Tijdschrift voor Mensenrechten · doi:10.21825/tvmr.93350 · Cited by 2
Digital Narratives and AI in Modern Media: A Systematic Literature Review
Harahap, K., Muhtar, Sakarina, S., Shofwani, S. A., & Hariyadi, A. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-031-84636-6_16 · Cited by 1
Estimating divergent moral and diversity preferences between AI builders and AI users
Purcell, Z. A., Charbit, L., Borst, G., & Nußberger, A.-M. (2025) · Cognition · doi:10.1016/j.cognition.2025.106198 · Cited by 2
“…who develop Artificial Intelligence (AI) technologies, AI builders, influence systems throughout the product development life-cycle, shaping outcomes through design, data selection, and fine-tuning (Birhane, 2021; Capraro et al., 2024; Lazar, 2024; Messeri & Crockett, 2024; Weidinger et al., 2023).”
Racine, E. E. (2025) · Emerging Media · doi:10.1177/27523543251372031 · Cited by 2
“…tend to narrowly focus on immediate risk mitigation and technical fairness, prioritizing algorithmic performance over actively advancing equity, justice, bene fi t-sharing, or community power and capability (Racine, 2024; see also Birhane, 2021, 2022a; Birhane et al., 2022; Davis et al., 2021).”
Salo-Pöntinen, H., & Saariluoma, P. O. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2551049 · Cited by 3
“In addition, calls for the objectivity of AI applications do not recognize that all AI applications are based on numerous operationalization procedures that are affected by the people involved (Birhane, 2021; Gitelman, 2013; Jasanoff, 2016; Shilton, 2018; Zicari et al., 2021).”
Strategies for Culturally Responsive AI in Education: Mitigating Bias and Enhancing Student Outcomes
Oyetade, K., Harmse, A., & Zuva, T. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6932-5_2 · Cited by 3
Liesenfeld, A., Parti, G., & Huang, C. (2021) · Mensch und Computer (MuC) · doi:10.1145/3473856.3474012 · Cited by 2
“Consequently, this technology needs to be part of discussions of what representations of gender and age are desirable, how to mitigate the risks of bias, and how to design more inclusive language technology [1].”
BackgroundDigital Twins in Contemporary Education: Virtual Workshop
Balyakin, A. A., Нурахов, Н. Н., & Nurbina, M. V. (2021) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-5063-5_39 · Cited by 2
Digital Twins in Real Economy: Unobvious Effects
Balyakin, A. A., Nurbina, M. V., & Taranenko, S. B. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-8829-4_68 · Cited by 2
Some Features of Social Structures and Institutions Transformation in the Digital Age
Balyakin, A. A., Nurbina, M. V., & Taranenko, S. B. (2022) · Journal of Digital Science · doi:10.33847/2686-8296.4.1_3 · Cited by 2
The ethics of data self-reporting: important issues and best practices
Tempini, N. (2023) · F1000Research · doi:10.12688/f1000research.128911.1 · Cited by 2
Editorial: Data driven and model based computational futures in cardiovascular practice
Bauer, R., Cicero, A. F. G., & Manca, M. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1336340 · Cited by 2
Towards a Feminist Decolonial Governance of AI
Mesa-Vélez, L. (2025) · Oxford University Press eBooks · doi:10.1093/9780198945215.003.0043 · Cited by 3
Žalnieriūtė, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5336216 · Cited by 2
What If Algorithmic Fairness Is a Category Error?
Narayanan, A. (2025) · Wiley eBooks · doi:10.1002/9781394258840.ch6 · Cited by 2
McStay, A. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197615546.003.0010 · Cited by 1
Wielinga, B. (2023) · AI and Ethics · doi:10.1007/s43681-023-00384-4 · Cited by 1
“For example, [4, 7] argue that it would be more productive to focus machine learning systems on understanding the causal mechanism underlying the predictions, rather than merely making predictions.”
Digital Ethics Beyond Informational Flow
Pyrrho, M. (2024) · Digital Society · doi:10.1007/s44206-024-00092-4 · Cited by 1
“…addressing them epistemically, without bearing in mind the distinction between information and knowledge, between understanding and predicting behaviour, science can leave behind important instruments such as hypothesis testing and explaining correlation without even noticing (Birhane, 2021).”
InfluentialRoen, K., Lundberg, T., & Joy, E. (2024) · Social Science & Medicine · doi:10.1016/j.socscimed.2024.117099 · Cited by 1
Meeting ABET Outcomes through Teaching Students to Analyze the Ethics of an AI System
Lane, S. T. (2024) · IEEE International Professional Communication Conference (ProComm) · doi:10.1109/procomm61427.2024.00060 · Cited by 1
Evaluation of Gender Bias in Amharic Word Embedding Model
Zenebe, B., Gizaw, S., & Abgaz, Y. M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70242-6_17 · Cited by 1
Smith, E., & Somayaji, D. (2024) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000001218 · Cited by 1
Algorithmic Fairness as an Inconsistent Concept
Hummel, P. (2025) · American Philosophical Quarterly · doi:10.5406/21521123.62.1.04 · Cited by 1
Paramasivan, K., Sujitha, A., & Haridas, A. (2025) · Journal of Applied Security Research · doi:10.1080/19361610.2025.2453309 · Cited by 1
“It is equally important to address ethical concerns, as the potential for misuse underscores the need for strict ethical standards and safeguards to prevent societal inequalities (Birhane, 2021).”
Smuha, N. A. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009367783.002 · Cited by 1
Multi-Value Alignment for Ml/Ai Development Choices
Jethwani, H., & Lewis, A. C. F. (2025) · American Philosophical Quarterly · doi:10.5406/21521123.62.2.03 · Cited by 1
Introduction: AI and Diversity in a Datafied World of Work – Will the Future of Work be Inclusive?
Vassilopoulou, J., & Kyriakidou, O. (2025) · International perspectives on equality, diversity and inclusion · doi:10.1108/s2051-233320250000012002 · Cited by 1
Vallverdú, J. (2025) · Philosophies · doi:10.3390/philosophies10030051 · Cited by 1
“A chief concern is that AI may embed or amplify stereotypes in training data [37].”
Yurtseven, M. (2025) · Eskişehir Osmangazi Üniversitesi İlahiyat Fakültesi Dergisi · doi:10.51702/esoguifd.1628249 · Cited by 3
Erll, A. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780197767733.003.0001 · Cited by 1
Erll, A. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780197767733.003.0002 · Cited by 1
Rethinking Intelligence Power and Epistemic Authority in the Age of Superhuman AI
Iseko, A. (2025) · International Journal of Science Technology and Society · doi:10.11648/j.ijsts.20251304.14 · Cited by 1
AI and Healthcare Disparities: Lessons from a Cautionary Tale in Knee Radiology
Hull, G. (2025) · The Journal of Medicine and Philosophy A Forum for Bioethics and Philosophy of Medicine · doi:10.1093/jmp/jhaf020 · Cited by 2
Crafting Research Questions, Objectives, and Aims
Almusaed, A., Almssad, A., & Yitmen, İ. (2025) · Springer eBooks · doi:10.1007/978-3-031-97393-2_4 · Cited by 1
Responsible Artificial Intelligence in Precision Psychiatry
Vesterinen, T. (2025) · Studies in applied philosophy, epistemology and rational ethics · doi:10.1007/978-3-031-99654-2_8 · Cited by 1
AI Governance: Ethics of Trustworthy AI in Healthcare
Skubis, I. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229217 · Cited by 1
“Scholars such as [2] and [3] highlight the importance of aligning AI development with societal values and inclusivity, while [4] emphasize the need for sustainability in AI practices.”
Pade, C. S., & Monga, C. (2025) · International Conference of Human-Computer Interaction Design & Research · doi:10.1145/3768633.3770139 · Cited by 1
“Gebru et al.[42]highlights that large-scale AI models can reproduce systemic biases, while Birhane et al.[43] calls for a relational ethics framework that centers contextual understanding and user subjectivity.”
Application of AI Tools in Film Education and the Construction of a Pedagogical Experiment Framework
Wang, S., & Ren, M. (2025) · International Symposium on Artificial Intelligence for Education (ISAIE) · doi:10.1145/3775073.3775111 · Cited by 1
A review of ethical AI frameworks in product development: taking stock and moving forward
Wecker, L., Nielsen, J. A., Nasrollahi, K., & Ploug, T. (2025) · AI and Ethics · doi:10.1007/s43681-025-00952-w · Cited by 2
“Global South perspectives often centre power asymmetries, infrastructural inequities, and postcolonial dynamics [61], which would reconfigure how concepts such as risk, harm, and respon - sibility are framed.”
Evaluating AI in Social Programs: Reframing Complex Intervention as Socio-Technical Intervention
Loy, A., & White, E. B. (2026) · Journal of Evidence-Based Social Work · doi:10.1080/26408066.2025.2583932 · Cited by 1
Algorithms on fire: leadership, power and climate collapse in the age of AI
Araújo, J. (2026) · Leadership & Organization Development Journal · doi:10.1108/lodj-06-2025-0542 · Cited by 1
The ghost in the gendered machine: AI, speculative fiction, and the illusion of inclusivity
Ajeesh, A. K. (2026) · AI & Society · doi:10.1007/s00146-026-02864-7 · Cited by 1
“This binary leaves little room for alternative visions and perpetuates a profound epistemic injustice: the exclusion of non-Western perspectives from our collective understanding of intelligence, ethics, and technological futures (Birhane 2021).”
If the archive can’t consent: Reimagining motion data and AI ethics for dance’s embodied histories
Bench, H., & Elswit, K. (2026) · Cambridge Forum on AI Culture and Society · doi:10.1017/cfc.2025.10009 · Cited by 1
“7 And yet, even such nuanced questions of representativeness cannot exist outside their relationship to uses, benefits and harms, leading to calls to move away from “building predictive tools” toward “valuing and prioritizing in-depth and contextual understanding” (Birhane 2021, p. 7).”
Brivá-Iglesias, V., & O’Brien, S. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2622588 · Cited by 4
“Comprehensive bias audits would also be conducted, examining linguistic and cultural fairness to ensure equitable performance across various patient groups and linguistic backgrounds (Birhane, 2021).”
A speculative realist typology of AI fairness: an Object-Oriented Onto-Ethics
Dhrubo, A. M., & Brohi, A. A. (2026) · AI and Ethics · doi:10.1007/s43681-026-00982-y · Cited by 1
“Additionally, ASF aligns closely with insights from critical race theory and feminist ethics, both of which emphasize that algorithmic decisions are embedded within stratified social orders and should therefore be evalu - ated against these deeper, structural backgrounds [22, 38].”
Wegner, L., Mirsch, M., & Leicht-Scholten, C. (2026) · AI and Ethics · doi:10.1007/s43681-026-01036-z · Cited by 2
Abusaada, H., & Elshater, A. M. (2026) · Planning Practice and Research · doi:10.1080/02697459.2026.2646633 · Cited by 1
Malachi, I. G., Akinola, O. O., Mordi, I. C., Umoru, N. B., & Palmer, S. A. (2025) · SAP Social AI · doi:10.62486/sai202515 · Cited by 1
Making AI Work: A Critical Theory of AI Production
Waelen, R. A., & Deranty, J. (2026) · Constellations · doi:10.1111/1467-8675.70053 · Cited by 1
Abdulmajid, M., Alali, N., & Alsharrah, A. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.102781 · Cited by 1
Attitudes, Imagined Roles, and Governance Boundaries for AI in Decentralized Social Media
Zhang, Z., Zhao, J., Wang, G., Viswanathan, S., Ross, T., Johnston, S., Liu, D., Noh, H., Kleek, M. G. V., & Shadbolt, N. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790295 · Cited by 2
“In parallel, work on data sovereignty and community-owned infrastructures argues for locally governed AI systems insulated from external processors, allowing communities to retain meaningful control over data flows and model behaviour [9, 20, 73, 97].”
Co-Disclosing the Computer: LLM-Mediated Computing through Reflective Conversation
Rost, M. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791769 · Cited by 3
“Work on prompt-as-programming and agentic LLMs demonstrates the potential of dynamic orchestration [41, 61], while critical HCI highlights the infrastructural politics of data and design [8, 16].”
AI, Normality, and Oppressive Things
Lin, T.-A., & Huang, L. T.-L. (2026) · Minds and Machines · doi:10.1007/s11023-026-09778-9 · Cited by 2
“Recent scholarship on AI has called attention to AI’s potential oppressive impacts (Benjamin, 2019 ; Birhane, 2021 ; Buolamwini & Gebru, 2018 ; Costanza-Chock, 2020; Crawford, 2021; Hof-mann et al., 2024; McClure & Wald, 2022; Mohamed et al., 2020 ; Noble, 2018 ).”
Decolonizing Algorithms: AI-Driven Media and the Reclamation of African Linguistic Identities
Aiseng, K. (2026) · Emerging Media · doi:10.1177/27523543261447913 · Cited by 1
“Resistance emerges through relational ethics and community agency (Birhane, 2021).”
InfluentialRefusal as silence: Gendered disparities in Vision-Language Model responses
Luo, S., Kim, S. J., Duan, Z., & Chen, K. (2026) · New Media & Society · doi:10.1177/14614448261441886 · Cited by 1
“Refusal, then, operates as both a technical artifact and a normative judgment concerning epistemic legitimacy (Birhane, 2021; Gillespie, 2018).”
AI and the machinization of humans: an escape from the burdens of existence
Beerends, S., Gertz, N., & Aydin, C. (2026) · AI and Ethics · doi:10.1007/s43681-026-01126-y · Cited by 2
“Not only in public debates, but also in academic papers addressing how AI intensifies cultural biases (see [13, 23, 50, 69]), human exploitation (see [26, 31, 42]), carbon emissions (see [17, 19]), water consumption, 4 copyright infringement (see [1, 56]), mass-surveillance (see [61, 70, 71]), and…”
Ziarani, M. Z. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7205-1.ch006 · Cited by 1
Theatre of the Tech-Oppressed: Exploring Algorithmic Injustice Through Participatory Performances
Babai, N., Durall, E., Iivari, N., Kinnula, M., & Iversen, O. S. (2026) · Participatory Design Conference · doi:10.1145/3796624.3796649 · Cited by 1
Tahir, I., Ensari, M. Ş., Shahzad, K., Kazmi, R. G., & Abu-Arja, A. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1808574 · Cited by 2
“At the same time, research suggests that AI systems have been trained on historical data that frequently encode and amplify structural inequalities (Birhane, 2021).”
Zheng, E. L., Cho, M. K., & Lee, S. S. (2026) · Cambridge Forum on AI Culture and Society · doi:10.1017/cfc.2026.10030 · Cited by 1
“Birhane (2021) advocates for alternative ethical frameworks grounded in relational, rather than individualistic or reductionist, conceptions of ethics, drawing from non-Western epistemologies.”
Chambers, L. M., & Davenport, D. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3806542 · Cited by 1
“FAccT has also begot substantive critiques of itself [5, 9].”
Cruz, A. D. S., Manrique, P. C. J., & Mendeja, J. M. (2026) · Discover Education · doi:10.1007/s44217-026-01890-3 · Cited by 4
“The cognitive aspect concerns conceptual understanding of AI, including machine learning and critical evaluation [2, 38].”
Bravo, G., & Bernier, L. (2026) · Canadian Journal of Bioethics · doi:10.7202/1126615ar · Cited by 1
Mayorga, C. A. E., Polanco, M. I. F., & Amaya, J. M. E. (2026) · Societies · doi:10.3390/soc16070226 · Cited by 1
“Birhane further shows that algorithmic injustice cannot be reduced to isolated bias; it must be interpreted through relational structures of power and social harm [17].”
Metadata
- Authors
- Birhane, A.
- Affiliations
- University College Dublin (IE)
- Corresponding author
- Birhane, Abeba
- ORCID
- Birhane, Abeba
- Venue
- Patterns
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 2
- Issue
- 2
- Pages
- 100205
- ISSN
- 2666-3899 (ISSN-L)
- PMC
- PMC7892355
- OpenAlex
- W3132771424
- Semantic Scholar
- a6de042d8a2baeeeb3d7e37e03c66c1fe07e7b36
- Cited by
- 707
- References
- 74
- Citations by year
- 2026: 251 · 2025: 177 · 2024: 85 · 2023: 94 · 2022: 73 · 2021: 25
- FWCI
- 12.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Patterns (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Science Foundation Ireland (13/RC/2094_P2)
- European Regional Development Fund
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Feminist Epistemology and Gender Studies
- Social Sciences › Law › Artificial Intelligence in Law
- Keywords
- algorithmic injustice, relational ethics, relationality, algorithmic harms, personhood, data ethics, justice
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