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
Items 201–300 of 708
Public Computing Intellectuals in the Age of AI Crisis
Connolly, R. W. (2024) · ACM Transactions on Computing Education · doi:10.1145/3703162 · Cited by 7
“Abeba Birhane (Senior Advisor in AI Accountability at Mozilla Foundation as well as Assistant Professor in Computer Science at Trinity College, Dublin) has documented the discriminatory malfeasance which lurks within many AI models, products, and research [16–18].”
Erll, A. (2025) · doi:10.1093/oso/9780197767733.001.0001 · Cited by 9
The role of LLMs in theory building
Astobiza, A. M. (2025) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2025.101617 · Cited by 8
Relational Ethics in the Administration of Healthcare Technology: AI, Automation and Proper Distance
Shaw, F., & McCosker, A. (2025) · Sociology of Health & Illness · doi:10.1111/1467-9566.70055 · Cited by 11
“For all types of automation in healthcare, there is a need to develop an associated ‘relational ethics’ (Birhane 2021) that can help to assess their impact on healthcare relationships.”
Learning AI Auditing: A Case Study of Teenagers Auditing a Generative AI Model
Morales-Navarro, L., Gan, M. A., Yu, E., Vogelstein, L., Kafai, Y., & Metaxa, D. (2025) · Proc. ACM Hum. Comput. Interact. · doi:10.1145/3757620 · Cited by 9
“Limited research has examined how young people actively investigate issues of algorithmic justice—how they understand, explain, and investigate the potential for algorithmic systems to perpetuate harm [6].”
Grant, J. H., Scharpenberg, D., & Manning, L. (2025) · Agricultural Systems · doi:10.1016/j.agsy.2025.104630 · Cited by 7
Massive Omission of Consent (MOOC): Ethical Research in Educational Big Data Studies
Costello, E., Brunton, J., Bolger, R., Soverino, T., & Juillerac, C. (2023) · Online Learning · doi:10.24059/olj.v27i2.3759 · Cited by 11
“(Birhane, 2021, p. 6) Further work in this area could usefully revolve around approaches to building greater forms of research traceability (like we are used to with our food), where research committees and academic journals work together to create a chain of accountability for researchers.”
BackgroundHernández, A. D., Ramokapane, K. M., Chowdhury, P. D., Michalec, O., Johnstone, E., Godwin, E., Cork, A., & Rashid, A. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610179 · Cited by 8
“Others stem from people’s everyday interactions with a variety of digital infrastructures and data-driven services where ethical issues manifest in injustices caused by automated decisionmaking; misuse, extraction, and exploitation of people’s personal data; or the ever more pervasive forms of surveillance impinging o…”
BackgroundSpatio-Temporal Graph Analytics on Secondary Affect Data for Improving Trustworthy Emotional AI
Uddin, M. T., Yin, L., & Canavan, S. J. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3296695 · Cited by 7
“For example, some works have developed grounding assumptions that allow ethical debate to fairly occur [17].”
BackgroundBeyond the ML Model: Applying Safety Engineering Frameworks to Text-to-Image Development
Rismani, S., Shelby, R. M., Smart, A., Santos, R. D., Moon, A., & Rostamzadeh, N. (2023) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3600211.3604685 · Cited by 10
“Similarly, ML and Responsible AI scholars have examined how image generation models could perpetuate existing systematic biases [4, 5, 12, 75, 77, 78], including stereotype amplification.”
BackgroundData Ethics Emergency Drill: A Toolbox for Discussing Responsible AI for Industry Teams
Hanschke, V. A., Rees, D., Alanyali, M., Hopkinson, D., & Marshall, P. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642402 · Cited by 10
“In response to some of the questions raised by responsible AI researchers, solutions have been ofered to support ethical AI governance, including ethical frameworks [8, 26], AI regulation [45], audits [14] and checklists for practitioners [37].”
Pink, S. (2022) · Routledge eBooks · doi:10.4324/9781003170884-4 · Cited by 7
Transformation of education through Big Data: digital twins case study
Andreasyan, A. A., & Balyakin, A. A. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2210/1/012003 · Cited by 7
“Some researchers claim that there is a pessimistic development scenario when the foundations of our world perception will disappear and cede ground to the decision-making based on nontransparent algorithms [7,8] which can lead to digital dictatorship.”
BackgroundThe Games We Play: Critical Complexity Improves Machine Learning
Birhane, A., & Sumpter, D. J. T. (2022) · Frontiers in artificial intelligence and applications · doi:10.3233/faia220186 · Cited by 7
Self-citationGood Scientific Practice in MEEG Research: Progress and PerspectivesPreprint
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. (2021) · doi:10.31219/osf.io/n2ryp · Cited by 7
Facial recognition surveillance and public space: protecting protest movements
Žalnieriūtė, M. (2024) · International Review of Law Computers & Technology · doi:10.1080/13600869.2023.2295690 · Cited by 6
“These regulatory approaches can be supported by introducing increased tax for such companies – which has already been implemented at the national level by the Organization for Economic Co-operation and Development’s landmark international tax deal which became effective in 2023 and implements a minimum corporate tax r…”
InfluentialA technical democracy design experiment: Making the UK exam algorithm controversy game
Swist, T., Gulson, K. Ν., Benn, C., Kitto, K., Knight, S., & Zhang, V. (2024) · Design Studies · doi:10.1016/j.destud.2024.101245 · Cited by 6
Beyond Big Tech: Advocacy Technologists within Mission-Driven Civil Society Organizations
Chambers, L. M. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757559 · Cited by 7
“Scholars like Birhane [18] and Robinson et al. [103] have pushed the field to embrace critical questions of ethics and responsibility in technology as infrastructural, relational, contextual and subjective as opposed to individualistic, rational, binarized, and objective.”
Is AI a Problem for Forward Looking Moral Responsibility? The Problem Followed by a Solution
Tollon, F. (2022) · Communications in computer and information science · doi:10.1007/978-3-030-95070-5_20 · Cited by 6
Responsible Innovation of Touchless Haptics: A Prospective Design Exploration in Social Interaction
Cornelio, P., Hughes, S., Georgiou, O., Frier, W., Maunsbach, M., Vasudevan, M. K., & Obrist, M. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581228 · Cited by 6
“new technologies align with social values, we need to reflect on what kind of social worlds our innovations create and whether they are fair, equitable and inclusive [14, 28, 107].”
BackgroundAI in Africa. Preliminary Notes on Design and Adoption
Adamu, M. S., & Nkwo, M. S. (2023) · DIID · doi:10.30682/diid8023d · Cited by 6
AL-Shboul, M. A. (2024) · Journal of Information Communication and Ethics in Society · doi:10.1108/jices-05-2023-0063 · Cited by 6
Quyoum, A., & Wong, M. (2024) · Information Communication & Society · doi:10.1080/1369118x.2024.2331781 · Cited by 6
“Our contribution thereby adds a new perspective to what Birhane (2021) theorises as a ‘ relational ethics ’ approach; addressing algorithmic injustice by and for those who are disproportionately impacted.”
Levine, M. (2025) · British Journal of Social Psychology · doi:10.1111/bjso.12896 · Cited by 5
“…away from just trying to make data bias and inequality transparent, to a more radical path of trying to tackle the underlying relations of power (see for example Bender and Friedman (2018), Buolamwini and Gebru (2018), Mitchell et al. (2019), Gebru (2020), Birhane (2021), Birhane et al. (2022)).”
From friction to synergy: The complex interplay of human creativity and AI
Deschryver, M., Henriksen, D., & Leahy, S. (2025) · Possibility Studies & Society · doi:10.1177/27538699251350483 · Cited by 8
“These moments highlight both the limits of GenAI and the contextual, emotional, and social dimensions of human creativity (Birhane, 2021; DeSchryver et al., in press).”
Adebisi, F. I. (2022) · Law Technology and Humans · doi:10.5204/lthj.2507 · Cited by 6
The Problem-Ladenness of Theory
Levenstein, D., Santo, A. D., Heijnen, S., Narayan, M., Maatman, F. O., Rawski, J., & Wright, C. (2024) · Computational Brain & Behavior · doi:10.1007/s42113-024-00219-3 · Cited by 6
“…ways in which they're solved) has societal applications that are misaligned with some social/ethical issues—e.g., AI fairness and discrimination using AI systems (Birhane, 2021), energy usage and global warming (Strubell et al., 2019), and the production of misinformation (Goldstein et al., 2023).”
Quantitative intersectional data (QUINTA): a #metoo case study
Boyd, A. E. (2021) · Digital Commons-DePaul (DePaul University) · Cited by 6
pyHDB - Ferramenta Heurística para a Hemeroteca Digital Brasileira
Brasil, E. (2022) · História da Historiografia International Journal of Theory and History of Historiography · doi:10.15848/hh.v15i40.1904 · Cited by 6
Artificial Intelligence for Climate Security: Possibilities and Challenges
Kim, K., & Vincent, B. (2023) · doi:10.55163/qdse8934 · Cited by 6
Oswald, M. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3812576 · Cited by 6
Speech AI for All: Promoting Accessibility, Fairness, Inclusivity, and Equity
Wu, S., Wenzel, K., Li, J., Li, Q., Pradhan, A., Kushalnagar, R., Lea, C., Koenecke, A., Vogler, C., Hasegawa‐Johnson, M., et al. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3706746 · Cited by 6
The Gaps that Never Were: Reconsidering Responsible AI’s Principle-Practice Problem
Ruster, L. P., & Davis, J. L. (2025) · Conference on Fairness, Accountability and Transparency · doi:10.1145/3715275.3732024 · Cited by 11
“…[26], an emphasis on useability instead of effectiveness [9], and a framing around problem/solution and rationality instead of relationality [10], amongst others about the gap-bridge metaphor as an object of inquiry and orienting device 2 Metaphors As Artifacts Metaphors are consequential,…”
Karumbaiah, S., Yin, Y., & Bharadwaj, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98414-3_5 · Cited by 5
Alam, N. (2025) · Journal of Evidence-Based Social Work · doi:10.1080/26408066.2025.2547219 · Cited by 8
Relational & culture-sensitive AI innovation
Herzog, C. (2025) · Journal of Responsible Technology · doi:10.1016/j.jrt.2025.100135 · Cited by 5
Liao, S., & Brinner, Z. (2026) · Philosophy Compass · doi:10.1111/phc3.70075 · Cited by 5
“First, consider Jesper Aagaard's critique of the so‐called dogma of harmony , an “idealized picture of human–technology relations in which all entities are presumed to cooperate and collaborate” (2021, 166).”
InfluentialKladko, S., Lavazza, A., Yu, X., & Farina, M. (2026) · International Journal of Innovation Studies · doi:10.1016/j.ijis.2026.100181 · Cited by 7
Hrechukha, L., Pavlenko, O., & Butko, N. H. (2026) · Dia-noesis. · doi:10.12681/dia.45793 · Cited by 5
Legal, Ethical, and Social Issues of AI and Law Enforcement in Europe
Brakel, R. E. V. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009367783.022 · Cited by 4
Deep acting attraction: Predation, masculinity and erotic labour in algorithmic romance
Khan, N. (2025) · New Media & Society · doi:10.1177/14614448251336419 · Cited by 5
“Scholars such as Ruha Benjamin (2019), Abeba Birhane (2021) and Julián Posada (2022) have discussed the materiality of AI development and implementation that is heavily infused with power dynamics that systemically oppress people of colour, workers of the Global South and gender minorities.”
La discriminación en una sociedad automatizada: Contribuciones desde América Latina
Gutiérrez, C. M. (2021) · Revista Chilena de Derecho y Tecnología · doi:10.5354/0719-2584.2021.58793 · Cited by 10
Fish, B., & Stark, L. (2022) · ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) · doi:10.1145/3551624.3555296 · Cited by 7
“Other related works include that of Birhane [15], who focuses on issues of Afro-feminist relational ethics in algorithmic systems, and Viljoen [75], who proposes a relational account of data and its governance.”
BackgroundReconsidering learning in a socio-material world. A response to Fischer et al.'s contribution
Pargman, T. C. (2022) · International Journal of Information and Learning Technology · doi:10.1108/ijilt-07-2022-0143 · Cited by 5
Opinions and Algorithms: Trust, Neutrality and Legitimacy
Razmetaeva, Y. (2023) · Philosophy of Law and General Theory of Law · doi:10.21564/2707-7039.1.275638 · Cited by 5
Harmful Terms in Computing: Towards Widespread Detection and Correction
Winchester, H., Boyd, A. E., & Johnson, B. (2023) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS) · doi:10.1109/icse-seis58686.2023.00018 · Cited by 5
Pink, S. (2022) · Buildings and Cities · doi:10.5334/bc.212 · Cited by 5
How K-12 CS Teachers Conceptualize CS Ethics
Hu, A. D., & Yadav, A. K. (2023) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3545945.3569775 · Cited by 5
“When these systems that use biased algorithms and data are deployed into the world, they create algorithmic injustice [7], like the documented cases of Black men being incorrectly arrested due to bias in the facial recognition algorithm used by the police [17].”
MethodBell, F., Campbell, L. M., Forsythe, G., Mycroft, L., & Scott, A.-M. (2023) · Open Book Publishers · doi:10.11647/obp.0363.10 · Cited by 5
Cattabriga, A., & Joler, V. (2023) · DIID · doi:10.30682/diid8023a · Cited by 4
Data Hazards: An open-source vocabulary of ethical hazards for data-intensive projects
Zelenka, N., Cara, N. H. D., Bennet, E. D., Clatworthy, P. L., Day, H., Garcia, I. K., García, S. R., Hanschke, V. A., Kuwertz, E. S., & Garcia, I. K. (2025) · Journal of Responsible Technology · doi:10.1016/j.jrt.2025.100110 · Cited by 7
Kwok, J. C. Y. (2026) · AI & SOCIETY · doi:10.1007/s00146-026-03091-w · Cited by 4
“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.”
Distribution, Recognition, and Just Medical AI
Daus, Z. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00842-3 · Cited by 4
“Relational theories of justice are being applied with increasing frequency to AI, both in healthcare (de Proost & Grey, 2024 ; Ho, 2022 ) and other settings (Birhane, 2021; Naudts, 2024 ).”
How predictive medicine leads to solidarity gaps in health
Braun, M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01497-2 · Cited by 4
Lay Perceptions of Algorithmic Discrimination in the Context of Systemic Injustice
Lima, G., Grgić-Hlača, N., Langer, M., & Zou, Y. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713536 · Cited by 5
“Critical algorithmic studies argue that algorithms that do not account for the context in which they are deployed can exacerbate injustice [12, 45, 54, 78, 102].”
InfluentialHollanek, T., Pi, Y., Fiorini, C., Vignali, V., Peters, D., & Drage, E. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732078 · Cited by 6
Dwivedi, A. V. (2025) · Emerging Media · doi:10.1177/27523543251373105 · Cited by 5
“This tendency is closely related to the postcolonial critique, which questions the so-called “ universal ” values and norms embedded in the designs and moderation practices of these platforms (Birhane, 2021, p. 11).”
Carmona-Galindo, V. D., Ung, H., Zeng, M. D., Broussard, C., Taranenko, E., Daneshbod, Y., Chappell, D., & Lorenz, T. (2025) · Knowledge · doi:10.3390/knowledge5030018 · Cited by 6
“Embedding these themes into technical training ensures that students are not only coders, but also critical thinkers equipped to lead ethically in AI-intensive fields [107].”
Pyle, C., & Andalibi, N. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757550 · Cited by 4
“Scholars have begun to attend to impacted groups’ perceptions (e.g., [11, 27, 28, 53, 72, 91, 123, 135, 136, 160, 181]) across a range of domains – demonstrating the importance of understanding such perceptions across contexts.”
In relation, not replacement: a reflexive case for generative AI in qualitative research
Mendoza, K. P. R., & Quilantang, R. F. (2026) · Qualitative Research Journal · doi:10.1108/qrj-11-2025-0428 · Cited by 6
Ricciardelli, L. A., Loy, A., & White, E. B. (2025) · Journal of Evidence-Based Social Work · doi:10.1080/26408066.2025.2587092 · Cited by 4
Velotto, G. (2025) · AI and Ethics · doi:10.1007/s43681-025-00880-9 · Cited by 3
“First, explanation-washing: technically correct but normatively thin rationales presented as if they settled questions of fairness, due process, or dignity [ 13 , 87 , 109 ].”
Cao, L., & Lin, A. M. Y. (2025) · Journal of English for Research Publication Purposes · doi:10.1075/jerpp.00038.cao · Cited by 4
Shabbir, M. S. (2026) · Journal of Entrepreneurship and Innovation in Emerging Economies · doi:10.1177/23939575261443024 · Cited by 5
Epistemic Compression and Symbolic Absence
Lutfi, R. (2026) · Dia-noesis. · doi:10.12681/dia.45799 · Cited by 4
Encoding the Enemy: The Politics Within and Around Ethical Algorithmic War
Moses, J., & Ford, G. (2023) · Global Society · doi:10.1080/13600826.2023.2234395 · Cited by 4
“This belief that algorithms can deliver perfect and objective knowledge is based on “ a misconception that injustice, ethics, and bias are relatively static things that we can solve once and for all ” (Birhane 2021, 6).”
BackgroundInfluentialTaming Affect: On the Construction of Objectivity in Data Annotation Practices
Catanzariti, B. (2025) · Digital Society · doi:10.1007/s44206-025-00198-3 · Cited by 4
“…situated and partial knowledges that contribute to the creation of ground truths in machine learning, this paper gestures then towards a broader argu - ment: a recognition of uncertainty as constitutive to the project of automation, rather than as a statistical outlier or error (Birhane, 2021b).”
AI and disabled people’s independent living: a framework for analysis
Mladenov, T. (2025) · AI & Society · doi:10.1007/s00146-025-02642-x · Cited by 7
Responsible AI for AI Sustainable Future: Governance, Ethics, and The Reality Behind the Promise
Atianashie, M. A., Kuffour, M. K., Kyiewu, B., & Serwaa, P. (2025) · Journal of Information Technology, Cybersecurity, and Artificial Intelligence · doi:10.70715/jitcai.2025.v2.i2.012 · Cited by 7
van Maanen, G., Ducuing, C., & Fia, T. (2024) · Internet Policy Review · doi:10.14763/2024.2.1748 · Cited by 5
Judicial ethics violations: Legal aspect and the role of judicial supervision
Marzuki, S., & Ali, M. (2024) · Corporate Law & Governance Review · doi:10.22495/clgrv6i3p2 · Cited by 4
“Ethics and the professional ethics of judges can be considered both a science and a system of value (Birhane, 2021).”
BackgroundWe need better images of AI and better conversations about AI
Steen, M., Timan, T., van Diggelen, J., & Vethman, S. (2024) · AI & Society · doi:10.1007/s00146-024-02101-z · Cited by 8
“Interestingly, the current relational turn (Coeckelbergh 2020; Birhane 2021) also fore-grounds such interactions between people and technologies.”
Fairness and Artificial Intelligence
Naudts, L., & Vedder, A. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009367783.006 · Cited by 6
The Anatomy of moral agency: A theological and neuroscience inspired model of virtue ethics
Crook, N., & Corneli, J. (2021) · Cognitive Computation and Systems · doi:10.1049/ccs2.12024 · Cited by 8
“The work we have presented should be seen as, in part, a response to Abeba Birhane, who puts forward a perspective that is deeply sceptical of discourse around ‘robot rights’ [34] and proposes (instead) relational ethics [35] focussed on human welfare.”
BackgroundMachine translation in everyone’s hands - Adoption and changes among general users of MT
Orrego-Carmona, D. (2022) · Tradumàtica tecnologies de la traducció · doi:10.5565/rev/tradumatica.324 · Cited by 4
Tour d’horizon des enjeux éthiques liés à l’IA en santé
Martineau, J. T., & Godin, F. R. (2023) · Éthique Publique · doi:10.4000/ethiquepublique.7978 · Cited by 5
Multiculturalism in Indonesia: How Does Literature Affect the Development of Diversity?
Wibowo, A. P., Sadeli, E. H., Pamungkas, O. Y., Irawan, D., Muryaningsih, S., & Faridli, E. M. (2023) · Journal of Law and Sustainable Development · doi:10.55908/sdgs.v11i10.1787 · Cited by 4
“Literature teaches us to view life from various perspectives and fosters a deeper understanding of human complexity (Birhane, 2021; Costea et al., 2018).”
BackgroundDitlhokwa, G. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.113306 · Cited by 4
ViBE: A Tool for Measuring and Mitigating Bias in Image DatasetsPreprint
Wang, A., Narayanan, A., & Russakovsky, O. (2020) · arXiv (Cornell University) · Cited by 4
Open Research Data: Experimenting Towards a Publishing Infrastructure
Okune, A., Otsuki, G., Schütz, T., Dréano, C., Invernizzi, N., Khandekar, A., Kaşdoğan, D., Kenner, A., Raman, S., Vasen, F., et al. (2022) · Engaging Science, Technology, and Society · doi:10.17351/ests2022.1885 · Cited by 3
Nwafor, I. E. (2024) · Communicatio · doi:10.1080/02500167.2024.2438071 · Cited by 4
Hype versus Historical Continuity: Situating the Rise of AI in Climate and Disaster Risk Modeling
Paudel, S., Loos, S., & Soden, R. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713985 · Cited by 4
“It is now well-established that outputs from AI tend to be biased towards status quo due to the way they are trained and optimized [13, 14].”
Faye, S. L., Nkweteyim, D. L., Sow, G. H. C., Diop, B., Diongue, F. B., Cissé, B., Dickson, N. S., Dia, N., Badu, K., Ayana, G., et al. (2025) · AI and Ethics · doi:10.1007/s43681-025-00779-5 · Cited by 5
“This concentration of epistemic and infrastructural power, reinforced by control over data platforms, algorithms, and cloud services, deepens digital inequalities [ 33 ].”
Generative AI and Democratic Culture
Branford, J., Soulier, E., & Fichtner, L. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00953-x · Cited by 5
“…to enhance genAI in support of democratic culture might draw on related attempts to improve epistemic diversity and pluralism both concerning AI and other digital technologies through such things as participatory datasets and diverse development teams (cf. D’Ignazio & Klein, 2020; Birhane, 2021 ).”
Beyond the algorithm: applying critical lenses to AI governance and societal change
Hassen, M. Z. (2025) · AI and Ethics · doi:10.1007/s43681-025-00781-x · Cited by 3
“The concentration of AI expertise and resources in a few powerful nations is not accidental; it is a result of deliberate historical and economic processes that create and maintain global inequalities, often replicating colonial patterns of resource extraction and dependency [ 4, 29 ].”
Transparency in large language model (LLM)-powered digital human twins: the AI ethics perspective
Pigac, T. (2025) · Ai & Society · doi:10.1007/s00146-025-02617-y · Cited by 6
Schöpke-Gonzalez, A., Dunn, K., Bardzell, S., Lewis, M., Murray, M., & Wieczorek, C. (2025) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3715070.3748283 · Cited by 3
“For example: reflexive insights [11, 30, 32]; ability to perceive the world in different and new ways [10, 33]; turning relational [2, 37], care [24], feminist technoscience [1, 8], and new materialist [14] theories into practice; slowing down [16, 22, 31]; and imagining futures and setting…”
Baeyaert, J. (2025) · EthAIca · doi:10.56294/ai2025417 · Cited by 3
Krueger, J. W., & Osler, L. (2025) · Ethics and Information Technology · doi:10.1007/s10676-025-09871-0 · Cited by 4
Bridging the AI Divide: Approaches to Ensure Equitable Access to Computing and Data
Zewdie, G. A., Yazdinejad, A., & Kong, J. (2026) · Frontiers of artificial intelligence, ethics and multidisciplinary applications. · doi:10.1007/978-3-032-05588-0_5 · Cited by 4
Gombar, M., Topalović, A., & Bach, M. P. (2026) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer21060166 · Cited by 4
Algorithmic Status Inequality: An Integrative Perspective on AI‐Driven Social Stratification
Wu, J. (2026) · Journal of Management Studies · doi:10.1111/joms.70134 · Cited by 4
Toward Addressing Inequality and Social Exclusion by Algorithms
Wójcik, M. A. (2024) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003320791-4 · Cited by 4
Cahya, A. I., Elizar, E., Badawi, B., Masitoh, M., Rachmatia, M., & Apriza, B. (2024) · AL-ISHLAH Jurnal Pendidikan · doi:10.35445/alishlah.v16i2.4817 · Cited by 3
Steen, M., de Greeff, J., de Boer, M. D., & Veenman, C. J. (2024) · AI and Ethics · doi:10.1007/s43681-024-00571-x · Cited by 3
FanFAIR: sensitive data sets semi-automatic fairness assessment
Gallese, C., Scantamburlo, T., Manzoni, L., Giannerini, S., & Nobile, M. S. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03184-4 · Cited by 3
What Is the Point of Equality in Machine Learning Fairness? Beyond Equality of Opportunity
Kong, Y. (2025) · ACM Journal on Responsible Computing · doi:10.1145/3766539 · Cited by 6
“While prevailing fair-ML research predominantly adopts a DE-only framework, as exemplified by BHN, there have been a few exceptions exploring RE approaches, including [14, 44].”
Decolonizing the Machine: Race, Gender and Disability in Robots and Algorithmic Art
Abramovic, B., Coleman, G., Donnarumma, M., Jochum, E., & Casey, C. S. (2021) · Electronic workshops in computing · doi:10.14236/ewic/pom2021.1 · Cited by 3
DIGITAL FOOTPRINT IN EDUCATION: FROM SCIENCE TO SOCIETY
Balyakin, A. A., Мамонов, М. В., Nurbina, M. V., & Taranenko, S. B. (2022) · Pedagogical Review · doi:10.23951/2307-6127-2022-5-9-19 · Cited by 3
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
