The ethics of algorithms: Mapping the debate

Brent Mittelstadt, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter, Luciano Floridi · Big Data & Society · 2016 · ai-ethics · 10.1177/2053951716679679 · record mittelstadt2016-the-ethics-of-algorithms-mapping-t
tag layer · index · concepts · authors · עברית
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Other fields define these differently the same word, defined another way in another discipline
bias 38 fields · machine learning 14 fields · transparency 5 fields · algorithm 5 fields · systematic-review 4 fields · privacy 3 fields · and 3 more →

Tags on this paper

Every tag the record carries, each linking to every other paper in the corpus that carries it. A tag appears here whether or not its words occur in the article — the spans below are only the ones a sentence in this paper licenses. Of the 20 concept tags on this paper, all 20 are grounded in a quoted sentence.
algorithm actionable insights transparency accessibility comprehensibility traceability data analytics practice profiling information asymmetry level of abstraction correlations causal connection machine learning opacity bias discrimination responsibility implementation privacy 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 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 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 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 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 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 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` systematic-review
Method, as described: a conceptual map proposed a priori from how algorithms operate, then a systematic review of the literature explicitly discussing ethics of algorithms, mapped onto it

Legend

relation to other workclaim_relationshow this paper stands to another: agrees, contradicts, extends, qualifies
claim typeclaim_typethe shape of the claim: existence, mechanism, method, critique, prediction, null
concept edgeconcept_edgesan asserted relation between two concepts, with the sentence asserting it
conceptconceptsan idea the paper works with, quoted where the paper uses it
content tagcontent_tagsa finding, definition, quantity, statistic or variable named in the paper
contributioncontributionwhat the paper claims to add
controlcontrolsan experiment run to eliminate a rival explanation
limitationlimitationswhat the paper says its evidence cannot support
mechanismmechanismshow the reported effect is said to come about
methodmethodhow the paper produced its evidence
object of studyobject_of_studywhat the paper is about — the thing it investigates
phenomenonphenomenonthe effect or regularity under study
research questionresearch_questionthe question the paper sets itself
sensesensesa distinct use of a term, quoted where the paper fixes it

The paper

The full text is not reproduced here. This article is not cleared for redistribution, so the page carries the tag layer only. Its evidence sentences are withheld; the grounding status remains with each tag, and the article itself is at https://doi.org/10.1177/2053951716679679. The reading view exists for the papers whose licence permits it.

Every tag on this page

claim_relations 5 · claim_type 1 · concept_edges 7 · concepts 20 · content_tags 11 · contribution 1 · controls 2 · limitations 7 · mechanisms 7 · method 1 · object_of_study 1 · phenomenon 1 · research_question 1 · senses 8

Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; none of 7 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.

facettagfieldconf.whose words
object_of_studythe ethical concerns raised by decision-making algorithms, as they appear in the academic literature that discusses algorithms explicitly
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object_of_studythe paper's words
research_questionwhat kinds of ethical issues algorithms raise, and which of them belong to the algorithm rather than to the technology built on it
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research_questionthe paper's words
contributiona prescriptive six-part map of ethical concerns - three epistemic, two normative, one overarching - together with a review of the literature against it and an assessment of where the field is thin
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contributionthe paper's words
claim_typemethod
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claim_typethe paper's words
phenomenonthe gap between how algorithms are designed and operated and how their ethical implications are understood
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phenomenonthe paper's words
methodsystematic-review
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methodthe paper's words
mechanismsmechanisms
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status: the informal ‘garbage in, garbage out’ principle clearly illustrates
mechanisms[0]0.95the tagger's words
mechanismsmechanisms
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mechanisms[1]0.95the tagger's words
mechanismsmechanisms
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mechanisms[2]0.95the tagger's words
mechanismsmechanisms
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mechanisms[3]0.95the tagger's words
mechanismsmechanisms
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mechanisms[4]0.95the tagger's words
mechanismsmechanisms
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mechanisms[5]0.9the tagger's words
mechanismsmechanisms
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mechanisms[6]0.95the tagger's words
limitationsthe review covers only literature that discusses algorithms explicitly, so neighbouring fields - AI ethics, surveillance studies, computer ethics, machine ethics - are touched on only briefly
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limitations[0]0.95the tagger's words
limitationsthe authors accept there may be gaps in coverage for topics discussed only in relation to specific algorithm types rather than to algorithms as such
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limitations[1]0.95the tagger's words
limitationssummarising every field in which algorithms are used is stated to be beyond reach, and the narrowing is justified on that ground
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limitations[2]0.95the tagger's words
limitationsdiscussion 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
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limitations[3]0.95the tagger's words
limitationsthe authors state that a mature ethics of algorithms does not yet exist, and attribute that partly to the breadth of the concept itself
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limitations[4]0.95the tagger's words
limitationsconfiguration 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
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limitations[5]0.9the tagger's words
limitationsalgorithms that automate mundane tasks are excluded by declaration rather than by a criterion, and the class of interest is given without a precise definition
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limitations[6]0.95the tagger's words
controlsthe 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
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eliminates: 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 · outcome: held
controls[0]0.95the tagger's words
controlsthe map is declared not to derive from any particular ethical theory, which is what lets readers who disagree about ethics still use it
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eliminates: the objection that the map only works for readers who already share the authors' ethical theory - it is declared to derive from none · outcome: held
controls[1]0.95the tagger's words
conceptsalgorithm
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also in: algorithm
concepts[0]0.95the paper's words
conceptsactionable insights
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concepts[1]0.95the paper's words
conceptstransparency
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also in: transparency
concepts[2]0.95the paper's words
conceptsaccessibility
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also in: accessibility
concepts[3]0.95the paper's words
conceptscomprehensibility
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concepts[4]0.95the paper's words
conceptstraceability
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also in: traceability
concepts[5]0.95the paper's words
conceptsdata analytics
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conceptspractice
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also in: practice
concepts[7]0.85the paper's words
conceptsprofiling
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also in: profiling
concepts[8]0.9the paper's words
conceptsinformation asymmetry
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concepts[9]0.95the paper's words
conceptslevel of abstraction
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concepts[10]0.95the paper's words
conceptscorrelations
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also in: correlations
concepts[11]0.95the paper's words
conceptscausal connection
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concepts[12]0.95the paper's words
conceptsmachine learning
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concepts[13]0.95the paper's words
conceptsopacity
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also in: opacity
concepts[14]0.9the paper's words
conceptsbias
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also in: bias
concepts[15]0.9the paper's words
conceptsdiscrimination
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concepts[16]0.95the paper's words
conceptsresponsibility
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concepts[17]0.9the paper's words
conceptsimplementation
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concepts[18]0.95the paper's words
conceptsprivacy
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also in: privacy
concepts[19]0.95the paper's words
sensesalgorithm - 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
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senses[0]0.95the tagger's words
sensesalgorithm - 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
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senses[1]0.95the tagger's words
sensestransparency - 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
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senses[2]0.95the tagger's words
sensesfairness - EXPLICITLY OBSERVER-DEPENDENT HERE, a generic label for whatever ethical criteria an observer brings to the action rather than a standard the paper supplies
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senses[3]0.95the tagger's words
sensesinconclusive 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
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senses[4]0.9the tagger's words
sensesinscrutable 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
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senses[5]0.9the tagger's words
sensesmisguided evidence - defined from a limit shared by ALL data processing rather than anything specific to algorithms: the output can never exceed the input
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senses[6]0.9the tagger's words
sensestransformative 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
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senses[7]0.9the tagger's words
claim_relationscritiques → the treatment of distinct epistemic and normative concerns as a single cluster, so that fixing one is taken to address the rest
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claim_relations[0]0.95the tagger's words
claim_relationscritiques → the assumption that better evidence reduces discrimination - the paper holds that it can be used to discriminate more efficiently
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claim_relations[1]0.95the tagger's words
claim_relationspredicts → the expectation that increasingly better algorithmic tools will eliminate the ethical problems of algorithms
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claim_relations[2]0.9the tagger's words
claim_relationscritiques → the naive treatment of transparency as a panacea for ethical issues arising from new technologies
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claim_relations[3]0.95the tagger's words
claim_relationsqualifies → the assumption that a more discerning algorithm is the ethically preferable one
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claim_relations[4]0.9the tagger's words
concept_edgescorrelations —distinct-from→ causal connection
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strength of the relation: 0.95
concept_edges[0]the tagger's words
concept_edgesaccessibility —part-of→ transparency
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strength of the relation: 0.95
concept_edges[1]the tagger's words
concept_edgescomprehensibility —part-of→ transparency
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strength of the relation: 0.95
concept_edges[2]the tagger's words
concept_edgestransparency —contrasted-with→ privacy
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strength of the relation: 0.9
also in: transparency · privacy
concept_edges[3]the tagger's words
concept_edgesmachine learning —causes→ opacity
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strength of the relation: 0.9 Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span says interpretability problems indicate opacity; causes is loose.
concept_edges[4]the tagger's words
concept_edgesimplementation —part-of→ algorithm
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strength of the relation: 0.9 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.
concept_edges[5]the tagger's words
concept_edgesdata analytics —is-a→ practice
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strength of the relation: 0.9
concept_edges[6]the tagger's words
content_tagsThree epistemic and two normative kinds of ethical concern arise from the use of algorithms.
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content_tags.findings[0]0.95the tagger's words
content_tagssolving a problem at one level of the map leaves the other kinds untouched
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content_tags.findings[1]0.95the tagger's words
content_tagsthe concerns are interdependent, and two links were already present in the literature: bias with discrimination, and opacity with the attribution of responsibility
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content_tags.findings[2]0.95the tagger's words
content_tagsEven strong correlations or causal knowledge may concern only populations, while actions are aimed at individuals.
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content_tags.findings[3]0.95the tagger's words
content_tagsalgorithm
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also in: algorithm
content_tags.definitions[0]0.95the tagger's words
content_tagstransparency
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also in: transparency
content_tags.definitions[1]0.95the tagger's words
content_tagsdata analytics
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content_tags.definitions[2]0.95the tagger's words
content_tagsactionable insights
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content_tags.definitions[3]0.95the tagger's words
content_tagstraceability
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also in: traceability
content_tags.definitions[4]0.9the tagger's words
content_tagsaccessibility
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also in: accessibility
content_tags.variables[0]0.95the tagger's words
content_tagscomprehensibility
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content_tags.variables[1]0.95the tagger's words

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