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.
Integrity signals
Registry
Cites
Statistics
Verification
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
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.
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
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.
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
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.
None of the sources cited for this claim have been checked yet.
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
hedgedcorrelational- 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.
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
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.
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
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.
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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
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.
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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could 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. 62
- It may state two separate claims as one. 19
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
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
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
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.
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
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: 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
“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
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
“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
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: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
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.
- 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.
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
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.
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.
- 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.
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.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
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
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.
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.
- 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.
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
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.
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.
- 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.
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.
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.
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 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
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: 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
“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.
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: 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
“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
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
“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
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. 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
“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
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: 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
“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.
- 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.
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
“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
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.
None of the sources cited for this claim have been checked yet.
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
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.
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
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.
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
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.
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
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.
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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could 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.
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
“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
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
“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
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.
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
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.
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
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. 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
“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
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: 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
“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.
- 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
“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.
- 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
“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.
- 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
“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.
- 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
“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.
- 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
“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.
- 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
“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.
- Checkable: fails — It may be too vague to check 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. 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.
- 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.
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.
- 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.
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.
- 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
“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.
- 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.
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.
- 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
“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.
- 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
“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.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not 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’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.
- 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
“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.
- 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.
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.
- Based: fails — The paper may give no reason why it holds.
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.
- 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.
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.
- 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
“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]
Citations
Cites
Black Feminist Thought: Knowledge, Consciousness, and the Politics of Empowerment.
Brewer, R. M., & Collins, P. H. (1992) · Contemporary Sociology · doi:10.2307/2074808 · Cited by 19,445
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137
Problems of Dostoevsky's Poetics
Bakhtin, M. (1984) · doi:10.5749/j.ctt22727z1 · Cited by 5,909
Sorting Things Out: Classification and Its Consequences
(2000) · The Journal of Academic Librarianship · doi:10.1016/s0099-1333(00)00155-5 · Cited by 5,212
The Black Box Society: The Secret Algorithms That Control Money and Information
Pasquale, F. (2015) · doi:10.4159/harvard.9780674736061 · Cited by 3,796
On understanding computers and cognition: A new foundation for design
Winograd, T. A., & Flores, F. (1987) · Artificial Intelligence · doi:10.1016/0004-3702(87)90026-9 · Cited by 3,685
(2018) · Cited by 3,510
Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor
Lebovits, H. (2018) · Public Integrity · doi:10.1080/10999922.2018.1511671 · Cited by 3,320
Bayes, T. (1763) · Philosophical Transactions of the Royal Society of London · doi:10.1098/rstl.1763.0053 · Cited by 3,023
Algorithms of oppression: how search engines reinforce racism
Polynczuk-Alenius, K. (2019) · Information, Communication & Society · doi:10.1080/1369118x.2019.1593485 · Cited by 2,934
Research works printed but not linked: 2
- Descartes, 1984, Vol. 2
- Birhane, 2017, Descartes was wrong:‘a person is a person through other persons’, Aeon
Cited by
Sorted by citations
Bias and Fairness in Large Language Models: A Survey
Gallegos, I. O., Rossi, R. A., Barrow, J., Tanjim, M. M., Kim, S., Dernoncourt, F., Yu, T., Zhang, R., & Ahmed, N. K. (2024) · Computational Linguistics · doi:10.1162/coli_a_00524 · Cited by 1,245
Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021) · Applied Sciences · doi:10.3390/app11115088 · Cited by 629
Reflexivity in quantitative research: A rationale and beginner's guide
Jamieson, M. K., Govaart, G. H., & Pownall, M. (2023) · Social and Personality Psychology Compass · doi:10.1111/spc3.12735 · Cited by 398
Using large language models in psychology
Demszky, D., Yang, D., Yeager, D. S., Bryan, C. J., Clapper, M., Chandhok, S., Eichstaedt, J. C., Hecht, C., Jamieson, J., Johnson, M., et al. (2023) · Nature Reviews Psychology · doi:10.1038/s44159-023-00241-5 · Cited by 369
Power to the People? Opportunities and Challenges for Participatory AI
Birhane, A., Isaac, W., Prabhakaran, V., Diaz, M., Elish, M. C., Gabriel, I., & Mohamed, S. (2022) · Equity and Access in Algorithms Mechanisms and Optimization · doi:10.1145/3551624.3555290 · Cited by 330
“It is a process that prioritizes the net benefit of participants, especially those disproportionately impacted by oppressive social structures, who often carry the burdens of negative impacts of technology [11, 20, 57, 60] and reflecting a fair or proportionate”
BackgroundSelf-citationEthical principles for artificial intelligence in K-12 education
Adams, C., Pente, P., Lemermeyer, G., & Rockwell, G. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100131 · Cited by 235
“Such relational ontologies (indigenous, humanized post-humanist) point towards adopting a more responsive and relational ethic (Birhane, 2021) to guide the deployment of AI in K-12 education.”
Can HR adapt to the paradoxes of artificial intelligence?
Charlwood, A., & Guenole, N. (2022) · Human Resource Management Journal · doi:10.1111/1748-8583.12433 · Cited by 271
“These concerns reflect broader insider critiques of the technology industry, which argue that AI developers are overly focussed on technical and commercial priorities and neglect the ethical and societal impact of their work (Birhane, 2021; Crawford, 2021; Whittaker, 2021).”
BackgroundInfluentialBonini, T., & Treré, E. (2024) · The MIT Press eBooks · doi:10.7551/mitpress/14329.001.0001 · Cited by 247
Zajko, M. (2022) · Sociology Compass · doi:10.1111/soc4.12962 · Cited by 268
The Impossibility of Automating Ambiguity
Birhane, A. (2021) · Artificial Life · doi:10.1162/artl_a_00336 · Cited by 198
“A robust body of research on algorithmic injustice (Benjamin, 2019; Birhane, 2021; Eubanks, 2018) shows that predictive systems perpetuate societal and historical injustice.”
BackgroundSelf-citationAnnotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection
Sap, M., Swayamdipta, S., Vianna, L., Zhou, X., Choi, Y., & Smith, N. A. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.431 · Cited by 224
Multimodal datasets: misogyny, pornography, and malignant stereotypes
Birhane, A., Prabhu, V. U., & Kahembwe, E. (2021) · arXiv · doi:10.48550/arxiv.2110.01963 · Cited by 160
“It fails to account for the rights, welfare, and interests of vulnerable individuals and communities, many of whom are likely to suffer worst from the downstream impacts of this dataset and the models trained on it [69].”
BackgroundSelf-citationHow AI can distort human beliefs
Kidd, C., & Birhane, A. (2023) · Science · doi:10.1126/science.adi0248 · Cited by 151
Self-citationDigital Twins in agriculture: challenges and opportunities for environmental sustainability
Purcell, W., Neubauer, T., & Mallinger, K. (2023) · Current Opinion in Environmental Sustainability · doi:10.1016/j.cosust.2022.101252 · Cited by 160
“This situation can be further aggravated through the combination of multiple and codepended machine learning models that focus on individual but inter-connected aspects of the replicated entity and its environment [50].”
BackgroundHand Tracking for Immersive Virtual Reality: Opportunities and Challenges
Buckingham, G. (2021) · Frontiers in Virtual Reality · doi:10.3389/frvir.2021.728461 · Cited by 140
“Challenge 4–Inclusivity Inclusivity is an increasingly important ethical issue in technology (Birhane, 2021), and the development of hand tracking and visualization in iVR throws up a series of unique challenges in this regard.”
BackgroundThe ethics of AI business practices: a review of 47 AI ethics guidelines
Attard-Frost, B., Ríos, A. D. L., & Walters, D. (2022) · AI and Ethics · doi:10.1007/s43681-022-00156-6 · Cited by 105
“Birhane [72] argues that instead of centering algorithmic decision-making processes, AI ethics guidelines should center the relations between algorithms and their social, political, and economic contexts, particularly their relations with vulnerable groups in those contexts.”
BackgroundArtificial Intelligence in the Colonial Matrix of Power
Muldoon, J., & Wu, B. A. (2023) · Philosophy & Technology · doi:10.1007/s13347-023-00687-8 · Cited by 148
“Following the emerging critical scholarship on values embedded in machine learning and the broader AI industry, we argue that imaginaries of AI’s generality and neutrality constitute a reproduction of hegemonic Western knowledge and epistemology (Birhane, 2021).”
Emotion AI at Work: Implications for Workplace Surveillance, Emotional Labor, and Emotional Privacy
Roemmich, K., Schaub, F., & Andalibi, N. (2023) · International Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580950 · Cited by 130
“Workers’ location on the weaker end of the power spectrum render them best suited to identify its harms and injustices [17, 49, 111].”
BackgroundThe Forgotten Margins of AI Ethics
Birhane, A., Ruane, E., Laurent, T., Brown, M. S., Flowers, J., Ventresque, A., & Dancy, C. L. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533157 · Cited by 160
“objectivity, the idea that there exists a “purely objective” dataset or a “neutral” representation of people, groups, and the social world independent of the observer/modeller [7, 8].”
BackgroundSelf-citationLiesenfeld, A., Lopez, A., & Dingemanse, M. (2023) · Proceedings of the 5th International Conference on Conversational User Interfaces · doi:10.1145/3571884.3604316 · Cited by 111
“But work that documents data provenance and traces harmful impacts [4, 49] deserves major scholarly and societal credit.”
BackgroundGwagwa, A. E., Kazim, E., & Hilliard, A. (2022) · Patterns · doi:10.1016/j.patter.2022.100462 · Cited by 95
Care and Scale: Decorrelative Ethics in Algorithmic Recommendation
Seaver, N. (2021) · Cultural Anthropology · doi:10.14506/ca36.3.11 · Cited by 94
Evidence, ethics and the promise of artificial intelligence in psychiatry
McCradden, M., Hui, K., & Buchman, D. Z. (2023) · Journal of Medical Ethics · doi:10.1136/jme-2022-108447 · Cited by 97
“36 Birhane 40 notes that relying on ML-generated predictions is particularly tenuous in contexts such as psychiatry where considerable complexity and ambiguity characterise the taxonomies.”
BackgroundWang, A., Ramaswamy, V., & Russakovsky, O. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533101 · Cited by 122
“We also note that despite the language we employ, we do not suggest that fairness can be treated as a purely algorithmic problem that neglects the sociotechnical frame [12, 50, 51, 109].”
BackgroundFrança, T. J. F., Mamede, H. S., Barroso, J., & Santos, V. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e14694 · Cited by 121
Decolonizing AI Ethics: Relational Autonomy as a Means to Counter AI Harms
Mhlambi, S., & Tiribelli, S. (2023) · Topoi · doi:10.1007/s11245-022-09874-2 · Cited by 102
“…allow to center in the design of AI those people that are most at the margins, marginalized, and vulnerable, and thus far the most impacted by AI (Birhane 2021; Mohamed et al. 2020; Mhlambi 2020), along with the need to decolonize AI and AI ethics and policy via non-Western approaches (Mhlambi…”
BackgroundInfluentialNavigating LLM ethics: advancements, challenges, and future directions
Jiao, J., Afroogh, S., Xu, Y., & Phillips, C. (2025) · AI and Ethics · doi:10.1007/s43681-025-00814-5 · Cited by 171
“This transformation involves acknowledging the critical role of data work, incentivizing thorough documentation of data origins, and addressing the need to identify and mitigate any adverse effects [91].”
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets
Wang, A., Liu, A., Zhang, R., Kleiman, A., Kim, L., Zhao, D., Shirai, I., Narayanan, A., & Russakovsky, O. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01625-5 · Cited by 95
“the deployment of a machine learning system (Green & Hu, 2018; Birhane, 2021).”
BackgroundCreating Design Resources to Scaffold the Ideation of AI Concepts
Yildirim, N., Oh, C., Sayar, D., Brand, K., Challa, S., Turri, V., Walton, N., Wong, A. E., Forlizzi, J., McCann, J., et al. (2023) · Designing Interactive Systems Conference · doi:10.1145/3563657.3596058 · Cited by 93
“HCI literature has explored AI’s design challenges, including explainability [53], trust [46, 73], algorithmic bias that creates harm [6, 87], privacy [18], and inference errors [36, 59].”
BackgroundHenriksen, D., Creely, E., Gruber, N., & Leahy, S. (2025) · Journal of Teacher Education · doi:10.1177/00224871251325058 · Cited by 94
“Bias in AI-driven emotional recognition remains a concern, given the afore-mentioned issue about training data lacking racial/cultural diversity (Benjamin, 2019; Birhane, 2021), and reinforcing inequities in SEL interventions.”
A synergistic future for AI and ecology
Han, B. A., Varshney, K. R., LaDeau, S. L., Subramaniam, A., Weathers, K. C., & Zwart, J. A. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2220283120 · Cited by 86
How Experienced Designers of Enterprise Applications Engage AI as a Design Material
Yildirim, N., Kass, A., Tung, T., Upton, C., Costello, D., Giusti, R., Lacin, S., Lovic, S., O'Neill, J. M., Meehan, R. O., et al. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517491 · Cited by 71
“The HCI community studied the issues around fairness, accountability, transparency, and ethics as they relate to AI systems [6, 25, 49, 75, 78, 85].”
BackgroundAmugongo, L. M., Kriebitz, A., Boch, A., & Lütge, C. (2025) · AI and Ethics · doi:10.1007/s43681-023-00331-3 · Cited by 68
“Birhane [51] argues that relationality demands focussing on the disproportionately impacted, i.e. the most marginalised and underrepresented communities.”
High-reward, high-risk technologies? An ethical and legal account of AI development in healthcare
Corfmat, M., Martineau, J. T., & Régis, C. (2025) · BMC Medical Ethics · doi:10.1186/s12910-024-01158-1 · Cited by 87
“These impediments to autonomy speak to the primordial moral and ethical choices of what constitutes a dignified, free, or satisfying human life, and several authors have exhorted us to deeply reflect on them [83].”
Lyu, W., Zhang, S., Chung, T., Sun, Y., & Zhang, Y. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100383 · Cited by 68
Sustainable AI and the third wave of AI ethics: a structural turn
Bolte, L., & van Wynsberghe, A. (2024) · AI and Ethics · doi:10.1007/s43681-024-00522-6 · Cited by 77
“We furthermore suggest that the same can be said for approaches like the aforementioned Data Ethics of Power [31], the relational AI ethics of Abeba Birhane [6] and some decolonial AI approaches [45], although we cannot defend this claim within the limits of this paper.”
Epistemic Injustice in Generative AI
Kay, J., Kasirzadeh, A., & Mohamed, S. (2024) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1609/aies.v7i1.31671 · Cited by 76
An elemental ethics for artificial intelligence: water as resistance within AI’s value chain
Lehuedé, S. (2024) · AI & Society · doi:10.1007/s00146-024-01922-2 · Cited by 78
“Sub-Saharan African Ubuntu ethics has been highlighted as a means to advance a less extractive and individualistic approach to the conception and application of technologies such as machine learning (Birhane 2021; Mhlambi 2020).”
Investigating Gender and Racial Biases in DALL-E Mini Images
Cheong, M., Abedin, E., Ferreira, M., Reimann, R., Chalson, S., Robinson, P., Byrne, J., Ruppanner, L., Alfano, M., & Klein, C. (2024) · ACM Journal on Responsible Computing · doi:10.1145/3649883 · Cited by 57
“Gaining a clearer understanding of the extent to which multi-modal generative models are biased, what sorts of biases they perpetuate, and who sufers most at the hands of biased representation is of critical import [12, 73].”
BackgroundGood scientific practice in EEG and MEG research: Progress and perspectives
Niso, G., Krol, L. R., Combrisson, E., Dubarry, A., Elliott, M., Clément, F., Héjja‐Brichard, Y., Herbst, S. K., Jerbi, K., Ković, V., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119056 · Cited by 68
“When increasing sample sizes, are must also be taken to increase diversity in terms of participants, enironments, and recording systems, in order to prevent the so-called “alorithmic injustice ” ( Birhane, 2021 ) which may otherwise result from ata-driven methods.”
BackgroundMethodRobots Enact Malignant Stereotypes
Hundt, A., Agnew, W. S., Zeng, V., Kacianka, S., & Gombolay, M. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533138 · Cited by 59
“We also recognize the immense obstacle posed by the manner in which current academic and industrial environments are often toxic for marginalized populations [2, 9, 11, 28, 52, 72, 73, 78].”
BackgroundDisruptive Technologies for e-Diasporas: Blockchain, DAOs, Data Cooperatives, Metaverse, and ChatGPT
Calzada, I. (2023) · Futures · doi:10.1016/j.futures.2023.103258 · Cited by 57
“Technical limitations: ChatGPT is limited by its programming and training, which may not capture all the nuances of language and cultural context, potentially leading to algorithmic bias or inaccurate responses (Birhane, 2021).”
BackgroundAutomating epistemology: how AI reconfigures truth, authority, and verification
Shin, D. (2026) · AI & SOCIETY · doi:10.1007/s00146-025-02560-y · Cited by 57
“The epistemic framework becomes aligned with mainstream discourses, rendering alternative perspectives anomalous or illegible (Birhane 2021; Crawford 2021).”
Invigorating Ubuntu Ethics in AI for healthcare: Enabling equitable care
Amugongo, L. M., Bidwell, N. J., & Corrigan, C. C. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594024 · Cited by 53
“For example, Birhane [9] proposed a fundamental shift from rational to relational thinking when it comes to personhood, data, justice and ethics.”
BackgroundInfluentialVaranasi, R. A., & Goyal, N. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580903 · Cited by 52
“critical for models, such as fairness [16, 32, 75], explainability [22], non-malfeasance [60, 77], and justice [8, 46, 61], reflecting societal norms.”
BackgroundCorvite, S., Roemmich, K., Rosenberg, T., & Andalibi, N. (2023) · Proc. ACM Hum. Comput. Interact. · doi:10.1145/3579600 · Cited by 66
“Our paper contributes to this understanding by centering data subjects in the workplace through a relational ethics lens [22].”
BackgroundMethodInfluentialEmotional AI and the rise of pseudo-intimacy: are we trading authenticity for algorithmic affection?
Babu, J., Joseph, D., Kumar, R., Alexander, E. M., Sasi, R. K., & Joseph, J. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1679324 · Cited by 67
“Postcolonial and Global South scholarship similarly warns against projecting autonomy-forward and individualist assumptions onto contexts where relational ontologies and communal forms of care dominate (Birhane, 2021; Rhee, 2023; Ayana et al., 2024).”
Resisting Dehumanization in the Age of “AI”
Bender, E. M. (2024) · Current Directions in Psychological Science · doi:10.1177/09637214231217286 · Cited by 77
“Birhane (2021, p. 3) observed and elucidated the deep commitment to rationalism and the supposed potential of a “God’s eye view” built into these systems—in which the “God’s eye view” aligns with the perspective of those with power in society.”
BackgroundAlgorithmic bias, fairness, and inclusivity: a multilevel framework for justice-oriented AI
Panarese, P., Grasso, M. M., & Solinas, C. (2025) · AI & Society · doi:10.1007/s00146-025-02451-2 · Cited by 66
Ovalle, A., Subramonian, A., Gautam, V., Gee, G. C., & Chang, K.-W. (2023) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3600211.3604705 · Cited by 76
“A few papers have reimagined intersectionality in AI [9, 23, 86], pushing for intersectional practices to be woven into the full AI pipeline, and arguing for a joint interrogation of culture, technology, and solutionist framings of fairness (e.g., critical technocultural discourse analysis [15]).”
Lavorgna, A., & Ugwudike, P. (2021) · Big Data & Society · doi:10.1177/20539517211049670 · Cited by 50
Ghasemaghaei, M., & Kordzadeh, N. (2024) · Information & Management · doi:10.1016/j.im.2024.103921 · Cited by 69
Martins, A., Londral, A. R. M., Nunes, I. L., & Lapão, L. V. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105385 · Cited by 58
Procedural fairness in algorithmic decision-making: the role of public engagement
Decker, M. C., Wegner, L., & Leicht-Scholten, C. (2024) · Ethics and Information Technology · doi:10.1007/s10676-024-09811-4 · Cited by 69
“Further, AI technologies are predominantly developed (and researched) in Western democracies, leading to insuffi - cient consideration of stakeholders’ interests from the global south (Birhane, 2021 ).”
Data Governance in the Age of Large-Scale Data-Driven Language Technology
Jernite, Y., Nguyen, H., Biderman, S., Rogers, A., Masoud, M., Danchev, V., Tan, S., Luccioni, A. S., Subramani, N., Johnson, I., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3534637 · Cited by 75
“acknowledging both the need for respecting local expertise and conceptualization of human rights [5] and for more relational and community-oriented notions of justice [17, 98], we complement our initial set of values as outlined in Table 1 to explicitly include the prioritization of local knowledge when realizing shar…”
MethodData justice and data solidarity
Braun, M., & Hummel, P. (2022) · Patterns · doi:10.1016/j.patter.2021.100427 · Cited by 47
“39 Against this background, Taylor, similar to scholars such as Abeba Birhane more recently, 40 highlights that justice in dealing with datafied lifeworlds and automated decision making must essentially be measured by the extent to which those who are marginalized or made less visible by structural…”
BackgroundEhsan, U., Singh, R., Metcalf, J., & Riedl, M. O. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533186 · Cited by 59
“Exemplars includes accounts of organizational roles and values in AI/ML ethics [1, 5, 69], studies of how data technologies redistribute social power [35, 85, 93], how algorithmic systems can reinforce extant social and political biases [79, 90], and recapitulate a worldview with harmful and undesirable histories [7,…”
BackgroundCoding energy knowledge in constructed responses with explainable NLP models
Gombert, S., Mitri, D. D., Karademir, O., Kubsch, M., Kolbe, H., Tautz, S., Grimm, A., Bohm, I., Neumann, K., & Drachsler, H. (2022) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12767 · Cited by 57
Designing ecosystems of intelligence from first principles
Friston, K. J., Ramstead, M. J. D., Kiefer, A. B., Tschantz, A., Buckley, C. L., Albarracin, M., Pitliya, R. J., Heins, C., Klein, B., Millidge, B., et al. (2024) · Collective Intelligence · doi:10.1177/26339137231222481 · Cited by 80
“More broadly and perhaps more fundamentally, we note that the approach to AI that we have presented here does not obviate the dangers associated with bias in AI technologies, especially when deployed at industrial scale in commercial settings, for example, (Birhane, 2021).”
BackgroundKassir, S., Baker, L. J., Dolphin, J., & Polli, F. E. (2022) · AI and Ethics · doi:10.1007/s43681-022-00208-x · Cited by 56
“Much like the deductive epistemology of I/O psychology, Birhane observes that such assumptions are common in computational research because of the field’s roots in the Western “rationalist” worldview [144].”
BackgroundRoemmich, K., Rosenberg, T. I., Fan, S., & Andalibi, N. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579543 · Cited by 58
“The privileged interests and concerns of EAI hiring services and the organizations for whom they build their product render these “knowers” [13] as ill-equipped to detect oppression and injustice associated with their technology under what D’Ignazio and Klein call the “privilege hazard” [31], in…”
BackgroundInfluentialUtilizing Artificial Intelligence to Enhance Health Equity Among Patients with Heart Failure
Johnson, A. E., Brewer, L. C., Echols, M. R., Mazimba, S., Shah, R. U., & Breathett, K. K. (2022) · Heart Failure Clinics · doi:10.1016/j.hfc.2021.11.001 · Cited by 41
Fifty Years of Prolog and Beyond
KÖRNER, P., LEUSCHEL, M., BARBOSA, J., COSTA, V. S., DAHL, V., HERMENEGILDO, M. V., MORALES, J. F., WIELEMAKER, J., DIAZ, D., ABREU, S., et al. (2022) · Theory and Practice of Logic Programming · doi:10.1017/s1471068422000102 · Cited by 40
“…sentence completion, and other standard benchmarking tasks, it offers a priori no way to learn meaning (Bender and Koller 2020) and relies on undocumented, un-retraceable, or otherwise partial (and therefore unaccountable) data, which tends to perpetuate harm without recourse (Birhane 2021).”
BackgroundMartin, C., DeStefano, K., Haran, H., Zink, S., Dai, J., Ahmed, D., Razzak, A. N., Lin, K. K., Kogler, A., Waller, J., et al. (2022) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2022.100073 · Cited by 40
Rismani, S., Shelby, R. M., Smart, A., Jatho, E. W., Kroll, J. A., Moon, A., & Rostamzadeh, N. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581407 · Cited by 38
“Anticipating harm involves thinking about the values [88, 111] and affordances of ML systems [17], with specific attention to how social norms and power dynamics constitutively shape adverse impacts of ML systems [12, 13].”
BackgroundEthics of AI in Africa: Interrogating the role of Ubuntu and AI governance initiatives
Yilma, K. (2025) · Ethics and Information Technology · doi:10.1007/s10676-025-09834-5 · Cited by 51
“The role of Ubuntu, in this regard, is often presented as a viable complementary African ethical framework of AI ethics (Birhane, 2021; Coecklbergh, 2022; Eke & Ogoh, 2022; Jecker et al., 2022; Mhlambi, 2020; Mhlambi & Tiribelli, 2023; Ruttkamp-Bloem, 2023).”
Mccradden, M., Odusi, O., Joshi, S., Akrout, I., Ndlovu, K., Glocker, B., Maicas, G., Liu, X., Mazwi, M., Garnett, T., et al. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3593013.3594096 · Cited by 33
“This guideline takes a position recommended by many knowledgeable scholars [1] [5] to avoid the “veneer of technical neutrality.”
BackgroundInfluentialFuture Shock: Generative AI and the International AI Policy and Governance Crisis
Leslie, D., & Perini, A. M. (2024) · Harvard Data Science Review · doi:10.1162/99608f92.88b4cc98 · Cited by 33
Patient wisdom should be incorporated into health AI to avoid algorithmic paternalism
McCradden, M. D., & Kirsch, R. E. (2023) · Nature Medicine · doi:10.1038/s41591-023-02224-8 · Cited by 33
Responsible AI in Farming: A Multi-Criteria Framework for Sustainable Technology Design
Mallinger, K., & Baeza-Yates, R. A. (2024) · Applied Sciences · doi:10.3390/app14010437 · Cited by 37
Metaphors of AI indicate that people increasingly perceive AI as warm and human-like
Cheng, M., Lee, A. Y., Rapuano, K. M., Niederhoffer, K. G., Liebscher, A., & Hancock, J. T. (2026) · Communications Psychology · doi:10.1038/s44271-025-00376-6 · Cited by 71
“Hey SyRI, tell me about algorithmic accountability”: Lessons from a landmark case
Wieringa, M. (2023) · Data & Policy · doi:10.1017/dap.2022.39 · Cited by 35
Weapons of moral construction? On the value of fairness in algorithmic decision-making
Giovanola, B., & Tiribelli, S. (2022) · Ethics and Information Technology · doi:10.1007/s10676-022-09622-5 · Cited by 31
Negotiating Authenticity in Technological Environments
Beerends, S., & Aydin, C. (2021) · Philosophy & Technology · doi:10.1007/s13347-021-00480-5 · Cited by 30
“In addition to appreciating the positive utility of data-driven technologies, the opaque ways in which companies as well as governments weaponize big data to predict human behavior, guide human decisions, and manipulate public information spheres for economic, social, or political gain (Maras & Alexandrou, 2019; Weste…”
BackgroundInfluentialCompeting narratives in AI ethics: a defense of sociotechnical pragmatism
Watson, D., Mökander, J., & Floridi, L. (2024) · AI & Society · doi:10.1007/s00146-024-02128-2 · Cited by 45
“(Birhane 2021) Goals like “greater transparency,” “better algorithms,” or “more representative datasets” do little to resolve these issues and may even exacerbate them by normalizing neo-liberal modes of agency in which users must navigate a complex marketplace of algorithmic alternatives with…”
From the ground up: developing a practical ethical methodology for integrating AI into industry
Anderson, M. M., & Fort, K. (2022) · Ai & Society · doi:10.1007/s00146-022-01531-x · Cited by 13
“In AI ethics, a supposedly objective and neutral view, a product of the thinking of seventeenth century modern philosophy, has come to ground the working approaches of tech fields, such as data science and engineering, as Birhane notes (Birhane 2021).”
BackgroundDirty Data Labeled Dirt Cheap: Epistemic Injustice in Machine Learning Systems
Hull, G. (2022) · Ethics and Information Technology · doi:10.1007/s10676-023-09712-y · Cited by 32
Allareddy, V., Oubaidin, M., Rampa, S., Venugopalan, S. R., Elnagar, M. H., Yadav, S., & Lee, M. K. (2023) · Orthodontics and Craniofacial Research · doi:10.1111/ocr.12721 · Cited by 31
Lysen, F., & Wyatt, S. (2024) · Journal of Responsible Innovation · doi:10.1080/23299460.2023.2300161 · Cited by 32
Dealing with Diversity in Psychology: Science and IdeologyPreprint
Roberts, S. O. (2022) · doi:10.31234/osf.io/xk4yu · Cited by 28
Klumbytė, G., Draude, C., & Taylor, A. S. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533207 · Cited by 35
“Furthermore, algorithmic and design justice scholars have high-lighted the need for accountable and contextualized systems that go beyond fairness towards equity and justice [11, 26, 58] and espoused algorithmic harm rather than only bias as an organizing concept [28, 34, 71].”
BackgroundMethodHandling and Presenting Harmful Text in NLP Research
Kirk, H. R., Birhane, A., Vidgen, B., & Derczynski, L. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.35 · Cited by 47
“Harms arise from ‘hyper-visibility’ (Noble, 2013), where excessive data portraying negative attributes such as stereotypes is collected (Birhane and Prabhu, 2021).”
BackgroundSelf-citationMollema, W. J. T. (2024) · Open Journal of Social Sciences · doi:10.4236/jss.2024.122032 · Cited by 26
“This lineage of thought gained popular attention and general traction through (among others) Abeba Birhane’s seminal paper on the algorithmic colonization in Africa (Birhane, 2020; see also Birhane, 2021).”
BackgroundConjuring algorithms: Understanding the tech industry as stage magicians
Nagy, P., & Neff, G. (2024) · New Media & Society · doi:10.1177/14614448241251789 · Cited by 26
“While the tech industry is often heavily criticized for their potential to perpetuate harm to historically marginalized communities (e.g., Birhane, 2021), violate citizens’ privacy rights (e.g., Gebru et al., 2017), or exacerbate existing racial discriminatory practices (e.g., Benjamin, 2019), the…”
The Ethical Dimension of Emerging Technologies in Engineering Education
de Vries, P. (2022) · Education Sciences · doi:10.3390/educsci12110754 · Cited by 34
“Birhane [23] therefore proposes a fundamental shift—from rationality to relationality—in thinking about personhood, data, justice—in short, everything, when it comes to the application of algorithmics.”
BackgroundA Sociotechnical Audit: Assessing Police Use of Facial Recognition
Radiya-Dixit, E., & Neff, G. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594084 · Cited by 36
“Part of the challenge motivating our work was that even so-called bias-free facial recognition tools can still be used in ways that harm and discriminate against marginalized communities [4, 17, 36].”
BackgroundFeminisms, technologies and learning: continuities and contestations
Atenas, J., Beetham, H., Bell, F., Cronin, C. M. G., Henry, J. V., & Walji, S. (2022) · Learning Media and Technology · doi:10.1080/17439884.2022.2041830 · Cited by 34
“More recently, data feminists such as D ’ Ignazio and Klein (2020), Eubanks (2018), Noble (2018) and Birhane (2021) have extended these insights into an understanding of data bias and the way power fl ows across platforms and networks.”
BackgroundInie, N., Falk, J., & Selvan, R. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714227 · Cited by 35
“On one hand, the technology bears promise of increased productivity and creativity [32, 62, 133, 134], and on the other, perils of bias, copyright infringement, and dehumanization [12, 18, 19, 25, 115, 223].”
Ojöng, N. (2025) · Business Strategy and the Environment · doi:10.1002/bse.70031 · Cited by 35
D’Arcy, A., & Bender, E. M. (2022) · Annual Review of Linguistics · doi:10.1146/annurev-linguistics-031120-015324 · Cited by 32
Predictive Algorithms in Justice Systems and the Limits of Tech-Reformism
Ugwudike, P. (2022) · International Journal for Crime, Justice and Social Democracy · doi:10.5204/ijcjsd.2189 · Cited by 29
Rethinking Rigor: Using Positionality and Reflexivity to Enhance Sport Management Scholarship
Sveinson, K., Delia, E. B., Melton, E. N., Dalal, K., & Cunningham, G. B. (2025) · Journal of Sport Management · doi:10.1123/jsm.2024-0155 · Cited by 24
“However, such a stance is symptomatic of a legacy of rationalist thinking, which Birhane (2021) critiqued for fostering a binary understanding of subjectivity versus objectivity.”
Attard-Frost, B., & Widder, D. G. (2025) · Big Data & Society · doi:10.1177/20539517251340603 · Cited by 49
“…cial outcomes , preexisting structural injustices in the social contexts of AI systems warrant an assumption that the same systems will also result in harmful outcomes for other actors, particularly for those who belong to historically marginalized communities (Birhane, 2021; Hind and Seitz, 2022).”
Vogl, M., Rötzel, P. G., & Homes, S. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100302 · Cited by 24
Building, Shifting, & Employing Power: A Taxonomy of Responses From Below to Algorithmic Harm
DeVrio, A., Eslami, M., & Holstein, K. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658958 · Cited by 21
Ethics of digital twins: four challenges
Braun, M. (2021) · Journal of Medical Ethics · doi:10.1136/medethics-2021-107675 · Cited by 23
Transphobia, encoded: an examination of trans-specific terminology in SNOMED CT and ICD-10-CM
Ram, A., Kronk, C. A., Eleazer, J. R., Goulet, J. L., Brandt, C., & Wang, K. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab200 · Cited by 23
AI through the looking glass: an empirical study of structural social and ethical challenges in AI
Ryan, M., de Roo, N., Wang, H., Blok, V., & Atik, C. (2024) · AI & Society · doi:10.1007/s00146-024-02146-0 · Cited by 33
“While there might be subtle differences, for simplicity, we consistently use the term 'AI system' in this paper, following the definitions of OECD (2022) and the European Commission (2021). and climate change).”
InfluentialSteen, M., van Diggelen, J., Timan, T., & van der Stap, N. (2022) · AI and Ethics · doi:10.1007/s43681-022-00168-2 · Cited by 25
“Interestingly, relational ethics has been put forward in discussions of the design and application of AI, e.g., by David Gunkel [35, 36], Mark Coeckelbergh [37, 38] and Abeba Birhane [39].”
Method
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 ›
Are you an author, or do you see a problem with this page? Request a review
