phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text| phenomenon | the gap between how algorithms are designed and operated and how their ethical implications are understood— “This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of… | |
| claim-type | method— “The map is thus not intended as a tool to help solve ethical dilemmas arising from problematic actions driven by algorithms, but rather is posed as an organising structure based on how algorithms operate that can structure future discussion of ethical issues.” | |
| method — evidence | — “We first propose a conceptual map based on six kinds of concerns that are jointly sufficient for a principled organisation of the field.” | |
| object of study — evidence | — “The paper answers two questions: what kinds of ethical issues are raised by algorithms? And, how do these issues apply to algorithms themselves, as opposed to technologies built upon algorithms?” | |
| research question — evidence | — “The paper answers two questions: what kinds of ethical issues are raised by algorithms? And, how do these issues apply to algorithms themselves, as opposed to technologies built upon algorithms?” | |
| contribution — evidence | — “This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of… | |
| mechanism | OUTPUT CANNOT EXCEED INPUT: a conclusion inherits the reliability and the non-neutrality of the data it was computed from, so an algorithm cannot repair evidence that was misguided before it arrived— “the informal ‘garbage in, garbage out’ principle clearly illustrates what is at stake here, namely that conclusions can only be as reliable (but also as neutral) as the data they are based on.” | mechanisms[0] |
| mechanism | A LEARNING ALGORITHM REWRITES ITS OWN DECISION STRUCTURE WHILE RUNNING, so the rationale for any particular classification is not fixed at design time and cannot be read off the program— “The algorithm modifies its behavioural structure during operation ( Markowetz et al., 2014). This alteration of how the algorithm classifies new inputs is how it learns” | mechanisms[1] |
| mechanism | ONCE TRAINED, CLASSIFICATION PROCEEDS WITHOUT AN OPERATOR - the structure produced by training is what new inputs meet, which is why oversight has no natural point of entry— “Training produces a structure (e.g. classes, clusters, ranks, weights, etc.) to classify new inputs or predict unknown variables. Once trained, new data can be processed and categorised automatically without operator intervention” | mechanisms[2] |
| mechanism | POPULATION KNOWLEDGE APPLIED TO AN INDIVIDUAL: correlations hold of groups while the action lands on a person, and even population-level action spills over - an insurance premium set for a sub-population is paid by each member— “even if both actions and knowledge are at the population-level, our actions may spill over into the individual level. For example, this happens when an insurance premium is set for a sub-population, and hence has to be paid by each member.” | mechanisms[3] |
| mechanism | VALUES ENTER THROUGH PARAMETERS: developers specify and users configure operational parameters with desired outcomes in mind, so the algorithm privileges some interests before it processes anything— “Operational parameters are specified by developers and configured by users with desired outcomes in mind that privilege some values and interests over others” | mechanisms[4] |
| mechanism | SECRECY IS COMMERCIALLY MOTIVATED, NOT INCIDENTAL: disclosure would enable gaming and threaten viability, so inaccessibility is a position taken in a conflict of interests rather than a technical limit— “This struggle is marked by information asymmetry and an “imbalance in knowledge and decision-making power” favouring data processors” | mechanisms[5] |
| mechanism | CORRELATIONS ARE ACTED ON WITHOUT CAUSAL KNOWLEDGE because a sufficient volume of data is treated as credible enough to drive action - the paper's name for that lowered bar is `actionable insights`— “In this sense data mining and profiling algorithms often need only establish a sufficiently reliable evidence base to drive action, referred to here as actionable insights.” | mechanisms[6] |
| critiques | the treatment of distinct epistemic and normative concerns as a single cluster, so that fixing one is taken to address the rest— “the map demonstrates that solving problems at one level does not address all types of concerns; a perfectly auditable algorithmic decision, or one that is based on conclusive, scrutable and well-founded evidence, can nevertheless cause unfair and transformative effects, without obvious ways to trace blame among the network of contributing… | claim_relations[0] |
| critiques | the assumption that better evidence reduces discrimination - the paper holds that it can be used to discriminate more efficiently— “Better methods to produce evidence for some actions need not rule out all forms of discrimination for example, and can even be used to discriminate more efficiently.” | claim_relations[1] |
| predicts | the expectation that increasingly better algorithmic tools will eliminate the ethical problems of algorithms— “we cannot in principle avoid epistemic and ethical residues.” | claim_relations[2] |
| critiques | the naive treatment of transparency as a panacea for ethical issues arising from new technologies— “transparency is often naïvely treated as a panacea for ethical issues arising from new technologies.” | claim_relations[3] |
| qualifies | the assumption that a more discerning algorithm is the ethically preferable one— “one may even conceive of situations where less discerning algorithms may have fewer objectionable effects.” | claim_relations[4] |
| sense | algorithm - THE PAPER HOLDS TWO SENSES AT ONCE AND SAYS SO. The formal sense is Hill's mathematical construct; the sense it actually maps is the public-discourse one, which includes the implementation into a technology and the configuration of that technology to a task. Its own point is that a map of the ethics cannot use the formal sense alone— “Lay usage of ‘algorithm’ also includes implementation of the mathematical construct into a technology, and an application of the technology configured for a particular task.” | senses[0] |
| sense | algorithm - AND THE CONFLATION IS NAMED AS THE OBSTACLE, not glossed over: the same word carries a mathematical construct and 'any procedure or decision process, however ill-defined', and the paper says the mapping exercise must address that before it can begin— “Any attempt to map an ‘ethics of algorithms’ must address this conflation between formal definitions and popular usage of ‘algorithm’.” | senses[1] |
| sense | transparency - NOT ONE PROPERTY BUT TWO CONDITIONS THAT COME APART: information can be accessible and still not comprehensible, which is exactly the case machine learning creates— “The primary components of transparency are accessibility and comprehensibility of information.” | senses[2] |
| sense | fairness - EXPLICITLY OBSERVER-DEPENDENT HERE, a generic label for whatever ethical criteria an observer brings to the action rather than a standard the paper supplies— “ethical evaluation of algorithms can also focus solely on the action itself. Actions driven by algorithms can be assessed according to numerous ethical criteria and principles, which we generically refer to here as the observer-dependent ‘fairness’ of the action and its effects.” | senses[3] |
| sense | inconclusive evidence - THE PAPER'S OWN CATEGORY, defined as the uncertainty that is inherent rather than accidental: inferential and machine-learning methods yield knowledge that is probable and inevitably uncertain, so the deficiency cannot be engineered away— “When algorithms draw conclusions from the data they process using inferential statistics and/or machine learning techniques, they produce probable6 yet inevitably uncertain knowledge.” | senses[4] |
| sense | inscrutable evidence - defined by a REASONABLE EXPECTATION THAT IS NOT MET: the link between data and conclusion ought to be intelligible and open to scrutiny, and given how algorithms operate that is not automatically satisfied— “When data are used as (or processed to produce) evidence for a conclusion, it is reasonable to expect that the connection between the data and the conclusion should be accessible (i.e. intelligible as well as open to scrutiny and perhaps even critique).” | senses[5] |
| sense | misguided evidence - defined from a limit shared by ALL data processing rather than anything specific to algorithms: the output can never exceed the input— “Algorithms process data and are therefore subject to a limitation shared by all types of data-processing, namely that the output can never exceed the input.” | senses[6] |
| sense | transformative effects - the category for harms with NO IDENTIFIABLE VICTIM AND NO OBVIOUS FAILURE: effects that look ethically neutral because nothing was broken, yet change how the world is conceptualised and organised— “algorithms can affect how we conceptualise the world, and modify its social and political organisation” | senses[7] |
| control | the paper marks its own normative judgements as observer-dependent rather than claiming a standard: whether evidence counts as misguided, and whether an outcome counts as unfair, are both said to depend on who is evaluating — held— “Evaluations of the neutrality of the process, and by connection whether the evidence produced is misguided, are of course observer-dependent.” — rules out a reader taking the map's normative labels as the authors' own ethical standard - 'misguided' and 'unfair' are marked observer-dependent, so the framework cannot be read as smuggling in a particular ethics— “Evaluations of the neutrality of the process, and by connection whether the evidence produced is misguided, are of course observer-dependent.” | controls[0] |
| control | the map is declared not to derive from any particular ethical theory, which is what lets readers who disagree about ethics still use it — held— “The map is not proposed from a particular theoretical or methodological approach to ethics, but rather is intended as a prescriptive framework of types of issues arising from algorithms owing to three aspects of how algorithms operate.” — rules out the objection that the map only works for readers who already share the authors' ethical theory - it is declared to derive from none— “The map is not proposed from a particular theoretical or methodological approach to ethics, but rather is intended as a prescriptive framework of types of issues arising from algorithms owing to three aspects of how algorithms operate.” | controls[1] |
| weight | correlations —distinct-from→ causal connection — 0.95— “statistical methods can help identify significant correlations, but these are rarely considered to be sufficient to posit the existence of a causal connection ( Illari and Russo, 2014: Chapter 8), and thus may be insufficient to motivate action on the basis of knowledge of such a connection.” | concept_edges[0] |
| weight | accessibility —part-of→ transparency — 0.95— “The primary components of transparency are accessibility and comprehensibility of information.” | concept_edges[1] |
| weight | comprehensibility —part-of→ transparency — 0.95— “The primary components of transparency are accessibility and comprehensibility of information.” | concept_edges[2] |
| weight | transparency —contrasted-with→ privacy — 0.9— “Transparency can thus run counter to other ethical ideals, in particular the privacy of data subjects and autonomy of organisations.” | concept_edges[3] |
| audit | machine learning —causes→ opacity — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span says interpretability problems indicate opacity; causes is loose.— “The longstanding problem of interpretability in machine learning algorithms indicates the challenge of opacity in algorithms” | concept_edges[4] |
| weight | machine learning —causes→ opacity — 0.9— “The longstanding problem of interpretability in machine learning algorithms indicates the challenge of opacity in algorithms” | concept_edges[4] |
| audit | implementation —part-of→ algorithm — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span reports LAY USAGE of 'algorithm' as including implementation; part-of is loose.— “Lay usage of ‘algorithm’ also includes implementation of the mathematical construct into a technology, and an application of the technology configured for a particular task.” | concept_edges[5] |
| weight | implementation —part-of→ algorithm — 0.9— “Lay usage of ‘algorithm’ also includes implementation of the mathematical construct into a technology, and an application of the technology configured for a particular task.” | concept_edges[5] |
| weight | data analytics —is-a→ practice — 0.9— “The discipline of data analytics is a standout example, defined here as the practice of using algorithms to make sense of streams of data.” | concept_edges[6] |
| outcome | control — held— “Evaluations of the neutrality of the process, and by connection whether the evidence produced is misguided, are of course observer-dependent.” | controls[0] |
| outcome | control — held— “The map is not proposed from a particular theoretical or methodological approach to ethics, but rather is intended as a prescriptive framework of types of issues arising from algorithms owing to three aspects of how algorithms operate.” | controls[1] |
| evidential status | mechanism — the informal ‘garbage in, garbage out’ principle clearly illustrates— “the informal ‘garbage in, garbage out’ principle clearly illustrates what is at stake here, namely that conclusions can only be as reliable (but also as neutral) as the data they are based on.” | mechanisms[0] |
| limitation | the review covers only literature that discusses algorithms explicitly, so neighbouring fields - AI ethics, surveillance studies, computer ethics, machine ethics - are touched on only briefly— “The review undertaken here was primarily limited to literature explicitly discussing algorithms. As a result, much relevant work performed in related fields is only briefly touched upon, in areas such as ethics of artificial intelligence, surveillance studies, computer ethics and machine ethics.” | limitations[0] |
| limitation | the authors accept there may be gaps in coverage for topics discussed only in relation to specific algorithm types rather than to algorithms as such— “We must therefore accept that there may be gaps in coverage for topics discussed only in relation to… | limitations[1] |
| limitation | summarising every field in which algorithms are used is stated to be beyond reach, and the narrowing is justified on that ground | limitations[2] |
| limitation | discussion of 'algorithm' runs into a level-of-abstraction problem: without fixing the level, parties talk past each other and the set of relevant observables is never bounded | limitations[3] |
| limitation | the authors state that a mature ethics of algorithms does not yet exist, and attribute that partly to the breadth of the concept itself | limitations[4] |
| limitation | configuration is excluded from changing the object: tuning an algorithm to a task is said not to alter its mathematical representation or its implementation, which bounds what the map is about | limitations[5] |
| limitation | algorithms that automate mundane tasks are excluded by declaration rather than by a criterion, and the class of interest is given without a precise definition Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | limitations[6] |
finding · quantity · variable · definition · key-term — grounded in the full text| finding | Three epistemic and two normative kinds of ethical concern arise from the use of algorithms. Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[0] |
| finding | solving a problem at one level of the map leaves the other kinds untouched Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[1] |
| finding | the concerns are interdependent, and two links were already present in the literature: bias with discrimination, and opacity with the attribution of responsibility Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[2] |
| finding | Even strong correlations or causal knowledge may concern only populations, while actions are aimed at individuals. Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[3] |
| variable | accessibility Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.variables[0] |
| variable | comprehensibility Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.variables[1] |
| definition | algorithm Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.definitions[0] |
| definition | transparency Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.definitions[1] |
| definition | data analytics Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.definitions[2] |
| definition | actionable insights Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.definitions[3] |
| definition | traceability Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.definitions[4] |
| concept | algorithm Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[0] |
| concept | actionable insights Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[1] |
| concept | transparency Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[2] |
| concept | accessibility Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[3] |
| concept | comprehensibility Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[4] |
| concept | traceability Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[5] |
| concept | data analytics Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[6] |
| concept | practice Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[7] |
| concept | profiling Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[8] |
| concept | information asymmetry Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[9] |
| concept | level of abstraction Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[10] |
| concept | correlations Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[11] |
| concept | causal connection Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[12] |
| concept | machine learning Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[13] |
| concept | opacity Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[14] |
| concept | bias Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[15] |
| concept | discrimination Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[16] |
| concept | responsibility Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[17] |
| concept | implementation Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[18] |
| concept | privacy Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[19] |
When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.
Tag layer © the Meta-Tagging Project, CC BY 4.0. The paper text is © its authors/publisher
and is linked, not reproduced. Excerpts shown are short quotations for identification and verification.
· back to index
· concepts · authors
· עברית
© 2026 Shir Sivroni · ORCID 0009-0000-2597-2824