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 101–200 of 708
Barrett, T., Chen, Q. Z., & Zhang, A. X. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594114 · Cited by 25
“people become more frequent and well-known, CV practitioners and researchers have a responsibility to investigate and address the racialized performance gaps in their systems [11].”
BackgroundBenovic, S., Ajlani, A. H., Leinert, C., Fotteler, M., Wolf, D. A., Steger, F., Kestler, H. A., Dallmeier, D., Denkinger, M. D., Eschweiler, G. W., et al. (2024) · Age and Ageing · doi:10.1093/ageing/afae101 · Cited by 21
“To sustain awareness that experience-based clinical knowledge is essential to navigate the ‘complex and inherently indeterminable phenomena’ that occur in human interaction [ 39], we decided to relate the estimated risk in the format of a percentage via Platt Scaling.”
MethodArzberger, A., Lupetti, M. L., & Giaccardi, E. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3689042 · Cited by 26
“…processing and algorithm refinement, and thus often fail to address the power structures and prejudices embedded in the training data, while the societal, cultural, institutional, and economic factors that are required to understand biases and their effect on the world remain overlooked [16, 72].”
Taking Algorithms to Courts: A Relational Approach to Algorithmic Accountability
Metcalf, J., Singh, R., Moss, E., Tafesse, E., & Watkins, E. A. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3593013.3594092 · Cited by 21
McStay, A. (2023) · doi:10.1093/oso/9780197615546.001.0001 · Cited by 26
Farazouli, A. (2024) · Postdigital science and education · doi:10.1007/978-3-031-58622-4_12 · Cited by 21
U.S. Job-Seekers' Organizational Justice Perceptions of Emotion AI-Enabled Interviews
Pyle, C., Roemmich, K., & Andalibi, N. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3686993 · Cited by 23
“Our relational ethics approach [18, 38, 124, 157] contributes to efforts examining perspectives of groups impacted by emerging algorithmic technologies [6, 10, 19, 23, 124, 161] by studying job-seekers’ perspectives — including those embodying marginalized identities — on emotion AI-enabled…”
McCoy, L. G., Bihorac, A., Celi, L. A., Elmore, M., Kewalramani, D., Kwaga, T., Martinez‐Martin, N., Prôa, R., Schamroth, J., Shaffer, J. D., et al. (2025) · BMC Global and Public Health · doi:10.1186/s44263-025-00158-6 · Cited by 24
Making Ethics at Home in Global CS Education: Provoking Stories from the Souths
Wong-Villacrés, M., Kutay, C., Lazem, S., Ahmed, N., Abad, C. L., Collazos, C. A., Elbassuoni, S., Islam, F., Singh, D., Mayeesha, T. T., et al. (2023) · ACM Journal on Computing and Sustainable Societies · doi:10.1145/3608113 · Cited by 20
“…researchers have sought to shift the focus of international codes and guidelines for ethics toward tackling societal inequalities and empowering those who have been previously disadvantaged [ 23 ] and offered alternative informing philosophies, such as the African ethic of Ubuntu [ 14 , 46 , 63 ].”
BackgroundVeletsianos, G., Houlden, S., & Johnson, N. E. (2024) · TechTrends · doi:10.1007/s11528-024-00942-5 · Cited by 23
“If AI must be used in education, developers ought to train models with better (i.e., less biased) datasets, and as Birhane (2021), Lewis (2021), and others suggest, involve diverse communities, especially those most likely to be impacted, in the development of these models and other types of AI…”
Subverting machines, fluctuating identities: Re-learning human categorization
Lu, C., Kay, J., & McKee, K. R. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533161 · Cited by 26
“ciotechnical systems [3], which center the interconnectedness of all entities and define personhood in terms of an individual’s relationships to others.”
BackgroundCharacterizing the perception of urban spaces from visual analytics of street-level imagery
de Freitas, F. S. S., Berreth, T., Chen, Y., & Jhala, A. H. (2022) · AI & Society · doi:10.1007/s00146-022-01592-y · Cited by 18
“As Birhane (2021) states in a recent paper, “… the more socially complex the problem is, the less accurate machine learning systems are of accurately classifying, predicting …” We understand that researchers, policymakers, and stakeholders are equally challenged by the complexity of such social…”
BackgroundConceptualizing Algorithmic Stigmatization
Andalibi, N., Pyle, C., Barta, K., Xian, L., Jacobs, A. Z., & Ackerman, M. S. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580970 · Cited by 23
“algorithmic systems as a form of reparation) [43, 78], participatory algorithm design [118, 143, 173], value-sensitive algorithm design [196], and examining the perspectives of social groups most adversely impacted by algorithmic systems [7, 25, 56, 157].”
Background(Anti)-Intentional Harms: The Conceptual Pitfalls of Emotion AI in Education
Diberardino, N., & Stark, L. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594088 · Cited by 27
“These power asymmetries are already particularly acute in schools serving primarily lower-income and racialized students, and these technologies will likely exacerbate and reinforce existing forms of structural injustice [9] [44].”
BackgroundJones, J. (2024) · Journal of Communication Inquiry · doi:10.1177/01968599241235209 · Cited by 18
“On the road to 3 rd modernity, such relational thinking could be a normative anchor for media makers and users as well as the technologists and data scientists that help design our collective future (Birhane, 2021; D ’ Ignazio & Klein, 2020; Jones, 2023; Shaban, 2022).”
“Democratizing AI” and the Concern of Algorithmic Injustice
Lin, T.-A. (2024) · Philosophy & Technology · doi:10.1007/s13347-024-00792-2 · Cited by 22
“With the profound impacts that artificial intelligence (AI) 1 imposes on the contemporary world, concerns regarding various forms of algorithmic injustice have been raised (Benjamin, 2019; Birhane, 2021; Crawford, 2021; Kalluri, 2020; Mohamed et al., 2020; Noble, 2018).”
Data Science Ethos Lifecycle: Interplay of Ethical Thinking and Data Science Practice
Boenig-Liptsin, M., Tanweer, A., & Edmundson, A. (2022) · Journal of Statistics and Data Science Education · doi:10.1080/26939169.2022.2089411 · Cited by 24
“…in philosophy (Lévinas 1969; Ricøeur 1992), moral anthropology (Lambek et al. 2015), and recent work being developed by philosophers of technology (Zigon 2019) and scholars who bring African philosophy perspectives on data and computing ethics (Mhlambi 2020; Birhane 2021; Wodajo and Ebert 2021).”
BackgroundThe lost art of mathematical modelling
Gyllingberg, L., Birhane, A., & Sumpter, D. J. T. (2023) · Mathematical Biosciences · doi:10.1016/j.mbs.2023.109033 · Cited by 18
Self-citationEffective sample size: A measure of individual uncertainty in predictions
Thomassen, D., le Cessie, S., van Houwelingen, H. V., & Steyerberg, E. (2023) · Statistics in Medicine · doi:10.1002/sim.10018 · Cited by 22
Unpacking Human-AI interactions: From Interaction Primitives to a Design Space
Tsiakas, K., & Murray-Rust, D. (2024) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3664522 · Cited by 24
Hollanek, T. (2024) · AI and Ethics · doi:10.1007/s43681-024-00545-z · Cited by 21
“Future toolkit creators are encouraged to consider, for instance, feminist ethics, ethics of care, decolonial theory, and Indigenous epistemologies [4, 35]–a recommendation that echoes a growing number of calls for AI ethicists to draw on relational philosophies in their work [6, 9].”
BackgroundGranDIHC-BR 2025-2035 - GC5 - Human-Data Interaction Data Literacy and Usable Privacy
Coleti, T. A., Divino, S. B. S., Salgado, A. D. L., Zacarias, R. O., Saraiva, J. D. A. G., Gonçalves, D. A., Morandini, M., & dos Santos, R. P. (2024) · Brazilian Symposium on Human Factors in Computing Systems (IHC) · doi:10.1145/3702038.3702058 · Cited by 17
“Another relevant challenge is that the lack of transparency in automated systems can lead to abuse, discrimination [82] and injus-tice [11, 85], increasing divisions and conflicts in society.”
Epistemic authority and medical AI: epistemological differences and challenges in medical practice
Kerasidou, A., & Kerasidou, C. (2025) · Medicine Health Care and Philosophy · doi:10.1007/s11019-025-10306-2 · Cited by 20
“In his two essays, “Doctor Knows Best”: On the Epis-temic Authority of the Medical Practitioner (2021) and The Epistemic Authority of Practice (2024 ), Popowicz assesses Zagzebski’s normative position of preemption, and suggests that it is not suitable for a non-domain specific context that is medicine and clinical ca…”
InfluentialSievers, B., & Thornton, M. A. (2024) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsae014 · Cited by 16
“An important and widely discussed ethical challenge for using ANNs is their propensity to learn and perpetuate human social biases (Birhane, 2021).”
BackgroundA method for the ethical analysis of brain-inspired AI
Farisco, M., Baldassarre, G. D., Cartoni, E., Leach, A., Petrovici, M. A., Rosemann, A., Salles, A., Stahl, B. C., & van Albada, S. J. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10769-4 · Cited by 20
“Brain differences between individuals of the same species can be explained by the interplay of genetic and epigenetic factors (Changeux et al. 1973 , 2021 ), further developmental factors (Bonduriansky and Day 2018 ), and factors reflecting the interaction with the environment like learning,…”
BackgroundMethodHow chatbots perceive sexting by adolescents
Ricon, T. (2024) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2024.100068 · Cited by 15
Makhortykh, M. (2023) · Memory Studies · doi:10.1177/17506980231202849 · Cited by 16
“AI-driven systems are by default amoral and follow a set of values coded into them by their developers, who often represent privileged communities and, thus, are not necessarily sensitive to the risks these systems can pose to underprivileged and marginalised groups (Birhane, 2021).”
BackgroundInfluentialParticipatory AI Considerations for Advancing Racial Health Equity
Parker, A. G., Vardoulakis, L., Alla, J., & Harrington, C. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713165 · Cited by 20
“In addition to designing for fourishing, through our review of prior work we fnd that health AI can be more fully responsive to the multidimensionality of humanity by going beyond a focus on rationalism–something that has largely driven health technology and AI innovations to date [23, 130].”
InfluentialThe silicon gaze: A typology of biases and inequality in LLMs through the lens of place
Kerche, F. W., Zook, M., & Graham, M. (2026) · Platforms & Society · doi:10.1177/29768624251408919 · Cited by 22
Lee, J.-J., Yap, C., & Roto, V. (2022) · International Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502128 · Cited by 17
“Future HCI studies dealing with AI-related technologies could beneft from integrating more system-level dimensions of service design, too, for tackling the complexity in designing fair AI systems [72, 110] and their ethical and political issues [8].”
BackgroundBennett, S., Claisse, C., Luger, E. A., & Durrant, A. C. (2023) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3600211.3604684 · Cited by 16
“We contribute to a growing body of literature in the AI Ethics and Society (AIES) and Fairness, Accountability and Transparency (FAccT) communities that investigate socio-political dimensions of AI and suggests conceptual tools to inform equitable design practice [4] [6] [12][48] [67].”
BackgroundTerms-we-serve-with: Five dimensions for anticipating and repairing algorithmic harm
Rakova, B., Shelby, R. M., & Ma, M. (2023) · Big Data & Society · doi:10.1177/20539517231211553 · Cited by 20
“Continuously engaging communities to surface potential frictions before and after a system is deployed (Birhane, 2021; Costanza-Chock, 2020; Krafft et al., 2021) enables better anticipating how such frictions may materialize into downstream harms in a world shaped by intersecting power dynamics.”
BackgroundEglash, R., Robinson, K. P., Bennett, A., Robert, L., & Garvin, M. (2024) · Big Data & Society · doi:10.1177/20539517231221732 · Cited by 15
“This has been extended to examine how that level of identi fi cation could make more meaningful connections between speci fi c makers and buyers: The potential of AI to develop a relational economy (Birhane, 2021; Mhlambi, 2020; Nayebare et al., 2023).”
A Remedy to the Unfair Use of AI in Educational Settings
Lundin, J., Modén, M. U., Lindell, T. L., & Fischer, G. (2023) · Interaction design & architecture(s)/ID&A Interaction design & architecture(s) · doi:10.55612/s-5002-059-002 · Cited by 15
Emotion AI Use in U.S. Mental Healthcare: Potentially Unjust and Techno-Solutionist
Roemmich, K., Corvite, S., Pyle, C., Karizat, N., & Andalibi, N. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637324 · Cited by 15
Algorithmic Harms and Algorithmic Wrongs
Diberardino, N., Baleshta, C., & Stark, L. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3659001 · Cited by 15
“Like-wise, the notion of “algorithmic pollution” is a way to denote the “unjustified, unfair, discriminatory, and other harmful effects of automated algorithmic decision-making” for individuals, groups, and societies [44:9].”
BackgroundInfluentialNaudts, L. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658934 · Cited by 19
Herzog, C., & Blank, S. (2024) · Journal of Responsible Innovation · doi:10.1080/23299460.2024.2431350 · Cited by 17
Ethical Implications of Artificial Intelligence in University Education
Mauti, J. M., & Ayieko, D. S. (2025) · East African Journal of Education Studies · doi:10.37284/eajes.8.1.2583 · Cited by 15
Donatz-Fest, I., Wieringa, M., & Wagner, B. (2022) · Patterns · doi:10.1016/j.patter.2022.100604 · Cited by 16
Hiring, Algorithms, and Choice: Why Interviews Still Matter
Bhargava, V. R., & Assadi, P. (2023) · Business Ethics Quarterly · doi:10.1017/beq.2022.41 · Cited by 14
“…(Creel & Hellman, 2022), eliciting organizational monocultures (Kleinberg & Raghavan, 2021), or disproportionately harming marginalized groups (Birhane, 2021); worries related to the legitimacy and trustworthiness of algorithms (Benn & Lazar, 2022; Martin & Waldman, 2022; Tong, Jia, Luo, &…”
BackgroundAutomated decision-making as domination
Burrell, J. (2024) · First Monday · doi:10.5210/fm.v29i4.13630 · Cited by 14
Digitized and Datafied Embodiment: A More-than-Human Approach
Lupton, D. A., Clark, M., & Southerton, C. (2022) · Springer eBooks · doi:10.1007/978-3-030-42681-1_65-1 · Cited by 14
Sounderajah, V., McCradden, M. D., Liu, X., Rose, S., Ashrafian, H., Collins, P. G. S., Anderson, J. A., Bossuyt, P. M., Moher, D., & Darzi, A. W. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00479-3 · Cited by 14
Neo-colonial intelligence: How AI risks reinforcing spatial injustices in a digitally divided world
Baresi, U. (2025) · Cities · doi:10.1016/j.cities.2025.106232 · Cited by 21
Mapping (in)visibility and structural injustice in the digital space
Wodajo, K. K. (2022) · Journal of Responsible Technology · doi:10.1016/j.jrt.2022.100024 · Cited by 13
Makhortykh, M. (2024) · Memory Mind & Media · doi:10.1017/mem.2024.2 · Cited by 14
“The epistemic uncertainty about the consequences of robot-to-robot memory communication is worsened by the multiple evidence of AI systems being prone to bias (e.g. Birhane 2021; Noble 2018).”
BackgroundSpeculative Practicescapes of Learning Design and Dreaming
Costello, E., McDonald, J. K., Macgilchrist, F., Jandrić, P., Carbonel, H., Crighton, S., Buch, A., & Peters, M. A. (2024) · Postdigital Science and Education · doi:10.1007/s42438-024-00465-5 · Cited by 13
“Theological philosopher Rein-hold Niebuhr described this well in his oft quoted plea to be gifted ‘the serenity to accept the things I cannot change, courage to change the things I can, and the wisdom to know the difference’ (Shapiro 2023).”
Decolonisation is not a vibe: On anti-capitalist praxis, citation politics and epistemic refusal
Bosch, T. E. (2025) · Media Culture & Society · doi:10.1177/01634437251360382 · Cited by 13
Hanandeh, R., Alkhazali, Z., Alhyasat, K. M. K., Mistarihi, A., & Kilani, Q. (2025) · Data & Metadata · doi:10.56294/dm2025712 · Cited by 12
Books (Are Not Like People): A Postdigital Fable
Costello, E., Soverino, T., & Girme, P. (2021) · Postdigital Science and Education · doi:10.1007/s42438-021-00256-2 · Cited by 12
Detecting energy injustices: Climbing the ladder of “hidden morality”
van Uffelen, N., & ten Caat, S. (2024) · Energy Policy · doi:10.1016/j.enpol.2024.114465 · Cited by 14
Why do we Need Norm Sensitive Design? A WEIRD Critique of Value Sensitive Approaches to Design
Martin, D. A., Clancy, R. F., Zhu, Q., & Bombaerts, G. J. T. (2023) · Global Philosophy · doi:10.1007/s10516-023-09689-9 · Cited by 16
“Ubuntu also acknowledges its distinct understanding of morality, which is relational rather than rational (Birhane 2021).”
Morales-i-Gras, J., Terradillos, J. O., Ureta, A. L., & Peña-Fernández, S. (2021) · Big Data and Cognitive Computing · doi:10.3390/bdcc5040069 · Cited by 14
At the intersection of human and algorithmic decision-making in distributed learning
Prinsloo, P., Slade, S., & Khalil, M. (2022) · Journal of Research on Technology in Education · doi:10.1080/15391523.2022.2121343 · Cited by 15
“Research on the ethical implications of AIEd has been addressed by several authors (see, e.g., Aiken & Epstein, 2000; Birhane, 2021; Holmes et al., 2021; Jobin et al., 2019).”
BackgroundQadri, R., Mirowski, P., & Denton, R. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714049 · Cited by 12
“AI fairness scholarship has already underscored the need to examine power distribution and structures of exclusion inhabited by AI technologies [15, 93].”
Understanding AI and power: situated perspectives from Global North and South practitioners
Brown, V., Larasati, R. A., Kwarteng, J., & Farrell, T. (2025) · AI & Society · doi:10.1007/s00146-025-02731-x · Cited by 11
“…scholars have critiqued the universality of Western moral reasoning and Eurocentric ethical principles and frameworks, instead advocating for more pluralistic and culturally grounded approaches that reflect diverse epistemologies and local contexts (Birhane 2021; Mhlambi 2020; Okolo, 2024).”
InfluentialHernández, A. D., & Owen, R. (2024) · Journal of Responsible Innovation · doi:10.1080/23299460.2024.2331655 · Cited by 11
AI, universal basic income, and power: symbolic violence in the tech elite's narrative
Bélisle-Pipon, J.-C. (2025) · Frontiers Artif. Intell. · doi:10.3389/frai.2025.1488457 · Cited by 10
“Framing the benefits of AI within the privileged contexts of already-advantaged nations disregards the inequitable realities of the Global North and Global South divide (Birhane, 2021; Mohamed et al., 2020), particularly from the perspective of an AI-driven UBI.”
Materiality and risk in the age of pervasive AI sensors
Sloane, M., Moss, E., Kennedy, S., Stewart, M. P., Warden, P., Plancher, B., & Reddi, V. J. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01017-7 · Cited by 16
“1 This work has focused crucial attention on how datasets, algorithms, and machine learning systems deployed at scale produce significant impacts for human rights, equity, already-disadvantaged populations and communities, and society at large [11, 16, 42, 72, 81, 95, 97, 99, 115].”
“That is scary!”: consumer perceptions and discourses on ChatGPT
Fares, O. H. (2025) · Qualitative Market Research An International Journal · doi:10.1108/qmr-07-2024-0122 · Cited by 13
Accounting for Offensive Speech as a Practice of Resistance
Díaz, M., Amironesei, R., Weidinger, L., & Gabriel, I. (2022) · Workshop on Online Abuse and Harms (WOAH) · doi:10.18653/v1/2022.woah-1.18 · Cited by 16
An exploration of intersectionality in software development and use
Winchester, H., Boyd, A. E., & Johnson, B. (2022) · doi:10.1145/3524501.3527605 · Cited by 11
“…around intersectionality has traveled across the academic landscape, scholars have emphasized how various communities located on the margins have suffered harm from technologies that replicate bias and stereotypes [1, 5, 34] and the need to explicitly consider their experiences [29, 40].”
BackgroundA multidomain relational framework to guide institutional AI research and adoption
Straub, V. J., Morgan, D., Hashem, Y., Francis, J., Esnaashari, S., & Bright, J. (2023) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3600211.3604718 · Cited by 11
“In prior work examining trends in recent proceedings, scholars have offered in-depth analyses of why and how certain topics, such as fairness [2, 7, 14, 21, 26], have been researched more than others [3, 5, 15, 33].”
BackgroundTESCREAL hallucinations: Psychedelic and AI hype as inequality engines
Devenot, N. (2023) · Journal of Psychedelic Studies · doi:10.1556/2054.2023.00292 · Cited by 16
Artificial Intelligence and the ethics of navigating ambiguity
Bennett, S. (2025) · Big Data & Society · doi:10.1177/20539517251347594 · Cited by 13
“Drawing from these relational theories, Birhane (2021) suggested moving towards an ethics of AI centred on human relations, on the communities who are disproportionately impacted, and acknowledging structural inequalities and the hidden labour inherent in how technologies are developed.”
Shahid, A., Khatri, N., Azhar, Z., Rauf, T., Ishaq, E. E., Kansal, G., Oduoye, M. O., Adeniji, O. W., & Conteh, J. Y. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1763884 · Cited by 10
Multiple Neighborhood Cellular Automata as a Mechanism for Creating an AGI on a Blockchain
Sgantzos, K., Grigg, I., & Hemairy, M. A. (2022) · Journal of risk and financial management · doi:10.3390/jrfm15080360 · Cited by 10
How Colonial Continuities Underlie Algorithmic Injustices in Education
Karumbaiah, S., & Brooks, J. (2021) · Research on Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT) · doi:10.1109/respect51740.2021.9620605 · Cited by 10
“Instead of making data race-less, we need to acknowledge the history of racial inequities, and shift the requirement from reducing harm to providing justice [20].”
BackgroundBarnett, S., Brand, N., Odom, W., & Andres, K. (2022) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3546155.3547286 · Cited by 10
“Consequences resulting from these socio-technical shifts also include the proliferation of social injustices [7,15,28,73], such as predictive policing [56,61] and predatory marketing [47].”
BackgroundCuration and collaboration as activism: emerging critical practices of #FemEdTech
Beetham, H., Drumm, L., Bell, F., Mycroft, L., & Forsythe, G. (2022) · Learning Media and Technology · doi:10.1080/17439884.2021.2018607 · Cited by 12
“Feminist relational ethics (Gilligan 1977; Noddings 2003; Birhane 2021) encourage us to be aware of our privileges in having the resources to participate in FemEdTech and to bring this paper together.”
BackgroundDigitized and Datafied Embodiment: A More-than-Human Approach
Lupton, D. A., Clark, M., & Southerton, C. (2022) · Springer eBooks · doi:10.1007/978-3-031-04958-3_65 · Cited by 10
A toolkit of dilemmas: Beyond debiasing and fairness formulas for responsible AI/ML
Hernández, A. D., & Galanos, V. (2022) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas55053.2022.10227133 · Cited by 11
“…reformulations, open to techno-social change, such as Birhane’s proposition to shift from rational formalisms within computational sciences to a relational ethics approach [28], or the growing calls to import reflexivity and qualitative methods into the design of algorithmic systems [29]–[31].”
BackgroundFairCompass: Operationalizing Fairness in Machine Learning
Liu, J., Chen, H., Shen, J., & Choo, K. R. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3348429 · Cited by 12
“With the feedback from practitioners [6], [7], [26], [30], the design process is informed by the practitioner feedback gathered in this work with emphasis to supplement statistical definitions with social practices [31], [3].”
MethodThoughtful Adoption of NLP for Civic Participation: Understanding Differences Among Policymakers
Guridi, J. A., Cheyre, C., & Yang, Q. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711091 · Cited by 12
“However, NLP tool adoption in government remains limited, perhaps because of the concerns that careless deployment of NLP tools can harm the democratic processes and erode public trust in government [17, 23, 29, 53, 113].”
“This robot is dictating her next steps in life”: disability justice and relational AI ethics
van Toorn, G., Scully, J. L., & Gendera, S. (2025) · AI & Society · doi:10.1007/s00146-025-02224-x · Cited by 9
“Our approach draws on insights from relational ethics, which view ethics as contextualized in everyday situations and in the structural relations influencing social life (Birhane 2021; Coeckelbergh 2010; Zigon 2019).”
Elizondo-García, M. E., la Cerda, H. H. D., Benavides-García, I. G., Caratozzolo, P., & Membrillo‐Hernández, J. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1417642 · Cited by 11
Stone, J. O. H., & Mittelstadt, B. (2025) · ACM Journal on Responsible Computing · doi:10.1145/3725858 · Cited by 13
“Critical analyses of ADS, especially those from scholars working in feminist and afro-feminist traditions, often include calls for the abolition of certain types of ADS or guarantees that power is vested only in those entitled to it [9, 11, 28, 43, 66].”
Sustainable AI meets feminist African ethics
Mensah, M., & van Wynsberghe, A. (2025) · AI and Ethics · doi:10.1007/s43681-025-00705-9 · Cited by 9
Nkwo, M. S., Ikwunne, T. A., Adejoro, C. O., & Anuyah, O. (2025) · Human-computer interaction series · doi:10.1007/978-3-031-88326-2_5 · Cited by 12
Hernández, A. D., Perini, A., Hadjiloizou, S., Borda, A., Mahomed, S., & Leslie, D. (2025) · AI and Ethics · doi:10.1007/s43681-025-00902-6 · Cited by 12
“Yet concurrently, criti - cal scholars have challenged potential universalist sustain - ability paradigms arguing for more situated governance grounded in pluriversal epistemologies that recognise diverse relational ontologies between technology, humans, and the planet [ 168–171 ].”
Agentic artificial intelligence in food science: From automation to adaptation
Gavai, A., & Heringa, J. (2026) · Food and Humanity · doi:10.1016/j.foohum.2026.101132 · Cited by 9
Morandé, S., & Amini, M. (2023) · Qeios · doi:10.32388/0qi028 · Cited by 10
A Human-Centered Approach to AI Governance
Gentelet, K., & Mizrahi, S. K. (2024) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003320791-24 · Cited by 10
Sabater, A., López, B., Campdepadrós, R., & Sánchez, C. (2025) · Artificial intelligence, simulation and society · doi:10.1007/978-3-031-71678-2_4 · Cited by 8
Ezema, K., Chandler, C., Southwell, R. V., Cholendiran, N., & D’Mello, S. K. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714059 · Cited by 10
“Black teachers, who are underrepresented in the teaching workforce [72], could face additional challenges, including biases and ethical concerns relating to emerging AI tools [6, 107].”
Understanding Human-AI Misalignment in LLM-Based Job-Seeking Support for Neurodivergent Users
Hall, K., Ma, M., Zhang, X., Swain, V. D., & Kim, J. G. (2025) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3663547.3746361 · Cited by 8
“For example, in our study, even when personal data was available, the chatbot produced hallucinations or generic outputs, reflecting broader limitations in current models’ capabilities of higher resolution outputs [15, 24].”
Culturally responsive AI chatbots: From framework to field evidence
Naidoo, V., & CHADHA, K. K. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100224 · Cited by 13
Relational Ethics and Structural Epistemic Injustice of AI in Medicine
Herzog, C., & Branford, J. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00987-1 · Cited by 11
“Relational frameworks to the ethics of technology originate in feminist epistemol-ogies and care ethics, in which relations, reciprocal co-relations, and dependencies are of primary interest (Birhane, 2021; Heilinger, 2022).”
InfluentialGroen, E., Sharon, T., & Becker, M. (2026) · AI and Ethics · doi:10.1007/s43681-025-00955-7 · Cited by 9
“…of principles, insofar as fairness is an abstract, universal concept, it lends itself well to being addressed by translating the principle into law—namely anti-discrimination law [8]—and technology design [10], either by implementing “fairness-in-design” [43], or by “de-biassing systems” [30].”
InfluentialHulus, A. (2026) · Smart Learning Environments · doi:10.1186/s40561-026-00437-1 · Cited by 8
Olaniyan, Y. D., Martins, M. O., & Al Maqrashi, R. H. (2026) · Frontiers in Human Dynamics · doi:10.3389/fhumd.2026.1790324 · Cited by 8
Using (Un)Fair Algorithms in an Unjust World
Lippert‐Rasmussen, K. (2022) · Res Publica · doi:10.1007/s11158-022-09558-z · Cited by 9
Datafication, Dehumanisation and Participatory Development
Roberts, T., & Zheng, Y. (2022) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-19429-0_23 · Cited by 8
An (E)Affective Bind: Situated Affectivity and the Prospect of Affect Recognition
Branford, J. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3281069 · Cited by 8
ACROCPoLis: A Descriptive Framework for Making Sense of Fairness
Aler Tubella, A., Coelho Mollo, D., Dahlgren Lindström, A., Devinney, H., Dignum, V., Ericson, P., Jonsson, A., Kampik, T., Lenaerts, T., Mendez, J. A., et al. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594059 · Cited by 8
“Being explicit on this aspect is critical for a sociological view of processes [4, 43], since it is crucially intertwined with issues of fairness (who holds the power? [7]) and accountability.”
BackgroundInfluentialMahlomaholo, M. R., Israel, H., & Mahlomaholo, S. M. G. (2023) · Journal of Curriculum Studies Research · doi:10.46303/jcsr.2023.18 · Cited by 8
“We experience impact in similar ways as inanimate objects; the animate plants and animals; the non-human beings; the more-than-human and the-beyond-human beings on the planet - which we are so fortunate to collectively inhabit (Birhane, 2021).”
BackgroundInvestigating Semi-Automatic Assessment of Data Sets Fairness by Means of Fuzzy Logic
Gallese, C., Scantamburlo, T., Manzoni, L., & Nobile, M. S. (2023) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb56990.2023.10264913 · Cited by 8
“Complex social issues require historical, political, and moral awareness, and structural change” [24].”
Aiming at the good life in the datafied world: A co-productionist framework of ethics
Boenig-Liptsin, M. (2022) · Big Data & Society · doi:10.1177/20539517221139782 · Cited by 9
“Recent scholarship on relational ethics and data and computing takes its point of departure from the African philosophical tradition, and in particular of ubuntu (Birhane, 2021; Mhlambi, 2020; Wodajo and Ebert, 2021).”
BackgroundMeasurement as governance in and for responsible AIPreprint
Jacobs, A. Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.05658 · Cited by 14
“” Breaking out of this feedback loop must then be done intentionally [7].”
Influential
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
