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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JMOULA, L. (2025) · Communication technologies et développement · doi:10.4000/155wp
Synthesizing Insights on AI in Africa: A Comprehensive Qualitative Analysis
Ramanathan, A., Huang, D., & Katz, J. E. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-11733-5_29
Undoing Gracia: queering the self in the algorithmic borderlands
Turtle, G. L., Giaccardi, E., & Bendor, R. (2025) · AI & Society · doi:10.1007/s00146-025-02661-8 · Cited by 2
Roman, M. (2025) · Frontiers of Architectural Research · doi:10.1016/j.foar.2025.10.001
Digital tools in evaluation: Addressing ethical risks and policy gaps in South Africa
Matlala, L. S. (2025) · Evaluation · doi:10.1177/13563890251378471
“Birhane (2021) warns that when evaluation software is applied uncritically, it can erase the very people evaluations aim to empower—an outcome that the Ethics of Technology framework sees as a failure of both design and governance.”
From Waste to Legitimacy in Volatile Contexts: ESG Practices, AI Governance, and Legitimacy PathwaysPreprint
Ahworegba, A. H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5877046
An Intersectional Approach to Generative AI
Marques, J. (2025) · doi:10.1093/9780198945215.003.0063
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Moleka, P. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5373572
Mai, L. T., Lin, M.-L., & Huang, J.-P. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5509091
Solar, G. V. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-05727-3_20
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Innocenti, M., & Redaelli, R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5769563
Beyond Universal Metrics: A Global South-Centered Framework for Ethical AI DevelopmentPreprint
Abdullah, X., & Siddiqui, F. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5792282
Mariyono, D., Yunus, M., & Hidayatullah, A. N. A. (2025) · Research Square · doi:10.21203/rs.3.rs-8417921/v1
Abdulmajid, M., Alali, N., & Alsharrah, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5983388
Huang, H. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-13164-5_23
Shake With Your Right Hand and Tweet With Your Left: Rethinking the Diplomatic Stage
Massa, A. (2026) · Springer eBooks · doi:10.1007/978-3-032-13937-5_4 · Cited by 2
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Ogden, M. R. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6066080
Devina, S. (2026) · Oxford University Press eBooks · doi:10.1093/9780198945215.003.0209
Multimodal generative AI: remodelling the sensorium
Kerruish, E. (2026) · The Senses and Society · doi:10.1080/17458927.2025.2609447
“Multimodal GenAI’s dependence on existing media continues the prejudices and biases such media harbors, with internet-scraped datasets brimming with political, social and cultural prejudices (Birhane 2021; Crawford 2021).”
Navigating Ethical Dilemmas in AI
Rahman, H. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2597-2.ch001
Impact of fine-tuning large language model in society: a comprehensive study
Chandra, J., Joseph, A., Joseph, J., Upadhyay, P., & Kumar, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33592-1.00008-5
Beaulieu, I. M., Saeed, H. M., & Hunt, M. R. (2026) · Military and humanitarian health ethics · doi:10.1007/978-3-032-11331-3_13
Quilantang, R. F., & Mendoza, K. P. R. (2026) · Communication Teacher · doi:10.1080/17404622.2025.2609611
“In doing so, we modeled what Birhane (2021) calls “relational ethics”—acknowledging that meaning, like technology, is made through encounter.”
Dev, N., & Joy, S. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2615218
“Without contextual understanding, outputs risk being insensitive, harmful, or reductively oversimplified (Birhane, 2021).”
Artificial intelligence: a neoliberal tool to better manage social inequalities?
Garrett, P. M. (2026) · European Journal of Social Work · doi:10.1080/13691457.2025.2607705
“In the US and writing in the social work profession’s flagship journal, Goldkind (2021, p. 372) correctly maintains the ‘data that powers outcomes, and the social systems in which these tools are deployed all inevitably reflect existing structural inequalities’.”
InfluentialYurtseven, M. (2025) · DergiPark (Istanbul University)
The Transformative Potential of Artificial Intelligence in Smart Cities
Nukpezah, J. A., Ntow, M. A. O., Adu, E. P., & Apalowo, J. T. (2026) · Springer eBooks · doi:10.1007/978-3-032-12344-2_12
AI in and as space: Rethinking transparency and accountability in AI through critical spatial theoryPreprint
Azmy, N. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6068447
Uştuk, Ö., & Selvi, A. F. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6253747
THE ROLE OF GREEN-AI IN SUSTAINABLE PLANNING: AN EXPLORATION ON THE CASE OF TARANTO, ITALY
Camarda, D. (2025) · International Multidisciplinary Scientific GeoConference SGEM ... · doi:10.5593/sgem2025v/6.2/s26.23
Jun, H., & Lee, H. J. (2026) · Studies in Philosophy and Education · doi:10.1007/s11217-026-10044-9 · Cited by 1
“While these systems promise the classical merito-cratic virtues of objectivity and efficiency, they often operationalize what Abeba Birhane calls a “veneer of objectivity” that frequently reproduces and amplifies existing forms of exclusion (Birhane 2021 ).”
Balancing Fairness and Performance Under Multiple Sensitive Attributes
Zhang, M., Di, Y., & Zhang, M. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-7081-2_2
Prophet Passion Java: Artificial Intelligence, Digital Platforms and Entrepreneurship
Musariri, M., & Marongedze, R. (2026) · Springer eBooks · doi:10.1007/978-3-032-11592-8_7
Circular Bioeconomy and Regenerative Resource Systems
Moleka, P. (2026) · Springer eBooks · doi:10.1007/978-3-030-67776-3_84-1
Ibitoye, A., Johnson, D. R., Sorinolu, B. G., Orji, R., & Abikoye, O. C. (2026) · AI and Ethics · doi:10.1007/s43681-026-01072-9
Miklian, J., Hoelscher, K., & Katsos, J. E. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6210099
A Systematic Review of the Dark Side of AI in Education: A Critical Social Theory Perspective
Adu, K. K., & Owusu‐Agyeman, Y. (2026) · Frontiers of digital education. · doi:10.1007/s44366-026-0084-0 · Cited by 3
AI as Political Infrastructure: A Diamond Model of Political AI Ethics
Bozdağ, A. A. (2026) · Philosophy & Technology · doi:10.1007/s13347-026-01058-9
“…when synthetic datasets inherit structural biases or when model-generated outputs are recursively reused in training pipelines (Shumailov et al., 2024; Long et al., 2025), reinforcing broader concerns that technical fixes in AI often leave underlying power asymmetries intact (Birhane, 2021).”
Exploring demographic influences on fairness perceptions across the ML development lifecycle
Mishra, A., Khazanchi, D., & Tripathi, A. (2026) · AI and Ethics · doi:10.1007/s43681-026-01034-1 · Cited by 1
Queer Representations in Motion: Documenting Identity through Animation
Pattinson, T. J., & Shaw, J. (2026) · Animation · doi:10.1177/17468477261425455
“GenAI, trained on vast datasets scraped from the internet, often reproduces and amplifies societal stereotypes (Birhane, 2021), effectively automating ‘symbolic annihilation’.”
Ubuntu meets algorithms: Indigenous knowledge systems as a foundation for ethical AI
Mugunzva, F. I., & Rankhumise, E. M. (2026) · Strategic Business Research · doi:10.1016/j.sbr.2026.100119
Park, C. S., & Akintade, T. (2026) · New Media & Society · doi:10.1177/14614448261429426 · Cited by 3
The Light at the Door: MAP and the Interaction-Visible Governance of the Black BoxPreprint
segeren, H. E. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6316458
Ethical Considerations of Artificial Intelligence and Automation in Agricultural Decision Making
Ahmad, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7257-0.ch012
Murikah, W., Nthenge, J. K., & Musyoka, F. M. (2026) · Telematics and Informatics Reports · doi:10.1016/j.teler.2026.100318
Modelling Together for AI-Based Social Services
Sabater, A., López, B., Calzado, S. P., & de Palau, I. (2026) · Artificial intelligence, simulation and society · doi:10.1007/978-3-032-15283-1_3
Policy Learnings and Policy Change for AI-Based Social Services
Sabater, A., López, B., & Campdepadrós, R. (2026) · Artificial intelligence, simulation and society · doi:10.1007/978-3-032-15283-1_8
Kumru, F. B. A. (2026) · Journal of Research in Childhood Education · doi:10.1080/02568543.2026.2640990
Artificial intelligence and climate migration equity
Palinkas, L. A., Özbilgin, M. F., Aczel, M. R., Ortar, N., Monteleoni, C., Sethi, S. S., Rice, E., Dilkina, B., & Barak, M. E. M. (2026) · Humanities and Social Sciences Communications · doi:10.1057/s41599-026-07087-1 · Cited by 1
The Affective Reservoir: From Transactional Rules to Relational Rhythms
de Petris, L., Khatibi, S., & Zhou, Y. (2026) · Systems · doi:10.3390/systems14040360
Who Controls the Algorithm? Data Sovereignty, Digital Extractivism, and AI Governance Gaps in AfricaPreprint
Okediji, S., & Oyinkansola, G. (2026) · Research Square · doi:10.21203/rs.3.rs-9997871/v1
Reframing AI Policies for Inclusive Growth in the Global South
Rahman, H., & Kumar, A. V. S. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-6132-1.ch008
Racine, E. E. (2026) · Springer eBooks · doi:10.1007/978-3-032-07830-8_11 · Cited by 1
AI Ethics for the Global Majority: lessons from Decolonial Feminist Bioethics
Teixeira, A. R. (2026) · Springer eBooks · doi:10.1007/978-3-032-07830-8_12
Decoding traditional knowledge: AI-powered solutions for preservation and interpretation
Bhat, G. S., Pauline, S., Chemalamudi, S., Srinivas, P., & Jain, K. K. (2026) · AI & Society · doi:10.1007/s00146-026-03010-z
“Scholars have argued that this dynamic mirrors earlier extractive practices in genomics, bioprospecting, and ethno-botany, now amplified by data-driven technologies (Ricaurte 2019; Birhane 2021).”
Yogarajah, P., Metwally, A. M., Chaurasia, P., Elshaarawy, G. A., Khateeb, S. M. E., Ashaat, E. A., Elsaeid, A., Elghareeb, N. A., & ElRifay, A. S. (2026) · Journal of Medical Internet Research · doi:10.2196/82564
“…AI models therefore reflects a call for AI tools to follow and extend the same principles of careful translation, local validation, and reflexive use that are increasingly expected of human-administered instruments, rather than holding AI to a fundamentally different standard [16,28,29,41-48].”
Policy analysis of generative AI in artistic expression: innovation and regulation
Lin, T., Zhang, S., Ma, Z., You, W., Kang Zhang, A., & Liang, C. (2026) · AI & Society · doi:10.1007/s00146-026-03001-0
“California AB602 (2021) further targeted deepfake pornography.”
Understanding Gen Z Social Behavior in Virtual Classrooms
Ponciano, L. H., Greer, C., Marsh, M.-J. O., & Ponciano, D. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-0934-5.ch009
Okeke, N. M. (2026) · Society · doi:10.1007/s12115-026-01172-6
“Expanding such models could empower marginalized groups and ensure equitable development across Africa, embodying what Birhane (2021) calls a move away from “algorithmic injustice.””
InfluentialSurveilling Suitability: How AI Hiring Interviews Impact Job Seekers with Disabilities
Kameswaran, V., Hong, V., Clark, J., Hou, Y., Daumé, H., & Shilton, K. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791516 · Cited by 1
“…hiring interviews, this framing allows us to bring to light the role of AI in reconfiguring social relations by shifting power dynamics, and recognizes that those at the weaker end of power relations are often best positioned to reveal the injustices embedded within technologies [16] (RQ1 and RQ2).”
Titus, S., Carolus, G., O’Neill, J., McNulty, B., Anthony, P., & Hendricks, L. (2026) · doi:10.12688/verixiv.3072.1
Guenier, A. W., & Mao, Y. (2026) · doi:10.2196/preprints.96062
Decolonizing AI Ethics and Governance
Tiribelli, S. (2026) · Oxford University Press eBooks · doi:10.1093/9780198945215.003.0078
Wallengren-Lynch, M. (2026) · Social Work Education · doi:10.1080/02615479.2026.2660786 · Cited by 1
Health and Stress and Artificial Intelligence
Shoss, M. K., Behrend, T. S., Crandell, H. A., & Tracy, M. M. (2026) · Oxford University Press eBooks · doi:10.1093/9780197807309.003.0015
Benhoumane, A. (2026) · doi:10.3917/ems.frimo.2026.01.0155
Cultural Intelligence in AI Governance
Okonkwo, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/407611
From Non-Maleficence to Beneficence: Expanded Ethical Computing in the Era of Large Language Models
Togia, E., Wallace, M., & Liaperdos, J. (2026) · Societies · doi:10.3390/soc16050134
Miklian, J., Hoelscher, K., & Katsos, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6644227
Culture in the code: Anthropological Concepts Decoding AI’s Hidden Assumptions
Schmer-Galunder, S. (2026) · AI and Ethics · doi:10.1007/s43681-026-01122-2 · Cited by 1
“Recent work in AI & Ethics has identified fundamental challenges in value alignment and cultural representation [1–3].”
Larsson, C. W. (2026) · Mobile Media & Communication · doi:10.1177/20501579261443596
Blockchain in Smart Governments
Trejo-Macotela, F. R. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-5742-3.ch005
Alanazi, B., & Alsaleh, A. (2026) · Philosophies · doi:10.3390/philosophies11030073
“Post-colonial analyses of AI adoption emphasize the risk of reinforcing dependency structures in emerging digital economies, a concern particularly relevant to Central and Eastern European contexts such as Romania [43,44].”
Légitime, S., & Suresh, H. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3817010 · Cited by 1
“This centralized governance paradigm has been shown to deepen power asymmetries and disproportionately impact marginalized groups [21, 86, 93], who are constantly subjected to disinformation campaigns, censorship, erasure, and surveillance [32, 100, 113].”
On the Value of the Nitty–Gritty Details: A Problem for Powerful AI Tools
Herzog, C., & Sarikaya, D. (2026) · Springer eBooks · doi:10.1007/978-3-032-15112-4_24
Saleh, A. S., & Abdel-Emam, M. Z. (2026) · Springer eBooks · doi:10.1007/978-3-032-16800-9_8
New-Form Business Translation Teaching Materials: Features, Development Trends and Challenges
Lu, W. (2026) · New frontiers in translation studies · doi:10.1007/978-981-95-4374-8_2
The mirror ethic: reflection, relation, and responsibility
Lee, C. (2026) · AI and Ethics · doi:10.1007/s43681-026-01160-w
Tapping into Basotho ‘Ethical Governance Resources’ for a Decolonised AI Governance
Mofuoa, K. (2026) · Springer eBooks · doi:10.1007/978-3-032-15112-4_53
Unseen things: Computer vision and the uneven geographies of AI failure
Cinnamon, J., & Dai, J. (2026) · Digital Geography and Society · doi:10.1016/j.diggeo.2026.100173
Human-Centered AI for Democracy
Mariani, I., Sacco, S., Rizzo, F., Kyrkou, D., Goga, E., Panagiotidou, G., Chadjipadelis, T. G., Gogos, C., Tzallas, A. T., Papageorgiou, X. S., et al. (2026) · Springer series in design and innovation · doi:10.1007/978-3-032-23948-8_5
Building AI companions that prioritise learning over performance
Khosravi, H., Gašević, D., Sadiq, S. W., Yan, L., Lodge, J. M., Tangen, J. M., Denny, P. C., DiCerbo, K. E., Shum, S. B., & Baker, R. S. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100680 · Cited by 5
Olajubu, B. J., Etuka, C. E., Hussaini, M. L., & Angama, L. J. (2026) · Human Arenas · doi:10.1007/s42087-026-00619-w
“…not by overwriting the adolescent’s existing values in a single exchange, but by cumulatively framing questions of sexuality, relationships, and personal identity within a moral logic that may be foreign to the household and community in which the adolescent is being raised (Birhane, 2021).”
AI-Driven Entrepreneurship: Catalyzing Green and Inclusive Growth in Africa
Edogun, D. I. (2026) · Frontiers in African business research · doi:10.1007/978-981-95-9500-6_3
From evaluator to principal: the agentic AI literacy framework (AALF) for delegated autonomy
Nama, R. (2026) · AI and Ethics · doi:10.1007/s43681-026-01167-3 · Cited by 1
“Birhane [6] extends relational autonomy with sub-Saharan relational philosophy, specifically Ubuntu’s relational ontology, to develop a framework centered on community and embeddedness rather than individual cognition.”
Harnessing AI for Climate Resilience in the Global South
Gaikwad, A., (c1c3f107-7330-4bdf-a9bd-af37820cb070, K. M., & Mishra, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7205-1.ch009
Prototyping as rehearsal for alignment
Atkinson, R. C. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-41359-9.00010-9
AI for Climate-Smart Agriculture: Pathways for Resilience in Developing Countries
Monica, C., Goyal, N., & Vijay, N. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9978-2.ch006
Outsourced Cognition and Identity
Söilen, K. S. (2026) · Springer eBooks · doi:10.1007/978-3-032-23069-0_9
Wisdom in the Loop - Human-Centered AI and African Knowledge Systems in HCI
Badawy, W. (2025) · African Human Computer Interaction Conference (AfriCHI) · doi:10.1145/3757232.3815398
“By doing so, it repositions African episte-mologies from the periphery to the core of AI innovation, aligning with calls for epistemic justice in HCI [15].”
Rohrbach, T., Makhortykh, M., & Sydorova, M. (2026) · Political Communication · doi:10.1080/10584609.2026.2679476
Empirical AI Ethics: Grounded, relational, transformative
Helm, P., & Gerlek, S. (2026) · Cambridge Forum on AI Culture and Society · doi:10.1017/cfc.2026.10023 · Cited by 1
Kpessa-Whyte, M. (2026) · International Journal of Sociology and Social Policy · doi:10.1108/ijssp-02-2026-0198
Prototyping Regenerative Ontologies: Participatory Design for More-Than-Human Coexistence
Finardi, M. A. (2026) · Participatory Design Conference · doi:10.1145/3789492.3796460
Detecting Gender Patterns in Programming Datasets: Towards Compliance and AI Governance
Ricci, G., Luzi, F. D., Monti, F., & Mecella, M. (2026) · Lecture notes in business information processing · doi:10.1007/978-3-032-28160-9_28
Effect of Digital Mental Health Technology on Mental Healthcare Accessibility in Sub-Saharan Africa
Latey, D. A. K., & Lubajo, R. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-2238-2.ch006
Preparing for Compliance or Critical Participation? Ethics and AI Literacy in Computing Education
Paljor, Y., Proctor, C. P., Xing, G. Y., & Decker, A. (2026) · Research on Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT) · doi:10.1145/3796496.3811753 · Cited by 1
“…AI studies, science and technology studies, and justice - oriented computing education have repeatedly challenged dominant, princ iple-based approaches to AI ethics, noting that abstract ethical guidelines often obscure lived contexts, power relations, and historical inequities [1, 2, 5, 9].”
InfluentialRisk assessment tools in policing contexts: 10 key ethical challenges
Kent, H., Fazel, S., Sorell, T., Borschmann, R., Zedner, L., Hamilton, M., Prescott-Mayling, L., Olphin, T. P. A., Douglas, T., Hogarth, L., et al. (2026) · Policing A Journal of Policy and Practice · doi:10.1093/police/paag028
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
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.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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 ›
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