The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index; 6 of the 22 sources its claims cite were checked for support.
Bias in computer systems
Open accessAbstract
From an analysis of actual cases, three categories of bias in computer systems have been developed: preexisting, technical, and emergent. Preexisting bias has its roots in social institutions, practices, and attitudes. Technical bias arises from technical constraints of considerations. Emergent bias arises in a context of use. Although others have pointed to bias inparticular computer systems and have noted the general problem, we know of no comparable work that examines this phenomenon comprehensively and which offers a framework for understanding and remedying it. We conclude by suggesting that freedom from bias should by counted amoung the select set of criteria—including reliability, accuracy, and efficiency—according to which the quality of systems in use in society should be judged.
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Claims the paper makes
6 of 22 cited sources checked · 4 supported · 0 flagged · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“As specified in Section 50-(9) of the British Nationality Act itself and imple-mented in BNAP,”
§ Bias in Computer Systems > 3.3 The British Nationality Act Program (BNAP)
What the sources say
- Supportedcites Sergot et al. 1986 source
The “Formalization by Trial and Error” passage states the same distinction: a man is the father only of his legitimate children, while a woman is the mother of all her children. It also says incorporating that provision requires an adjustment to the existing rules, supporting the claim that it was implemented in the logic program. (LLM, conf=0.94)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“At this point, it was discovered that the original Admissions Algorithm placed married couples at a disadvantage in achieving an optimal placement as compared with their single peers [Roth 1984;1990].”
What the sources say
- Partly supportedcites Roth 1984 sourceExperimental — accuracy not yet measured
Section IV, “Contemporary Issues” (page not supplied), says the couples algorithm used until 1983 could not accept preferences over pairs of positions and could produce outcomes unstable with respect to couples. That supports a disadvantage for couples, but the paper does not establish that couples achieved less optimal placements than single applicants. (LLM, conf=0.84)
- Not checked — no full text of the sourcecites Roth 1990 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In this manner the MLSA gives processing attention to all processes as quickly as possible that are beginning a new command.”
§ Bias in Computer Systems > 3.2 A Multilevel Scheduling Algorithm (MLSA)
What the sources say
- Supportedcites Corbato et al. 1962 source
The “A Multi-Level Scheduling Algorithm” section says programs entering the system are placed in priority queues and that scheduling begins with the lowest occupied level, switching to a newly occupied lower level. The IBM 7090 system section also says the supervisor gives top priority to initiating waiting commands. Together, these passages support the paraphrase about prompt attention to processes beginning a command. (LLM, conf=0.88)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“At the time of the BNAP's initial implementation (1983) no mechanism was built into the program to incorporate relevant case law as it came into being [Sergot et al. 1986].”
§ Bias in Computer Systems > 3.3 The British Nationality Act Program (BNAP)
What the sources say
- Supportedcites Sergot et al. 1986 source
The introduction says the Act was free of case law when the original implementation began in summer 1983. It also says the authors deliberately avoided incorporating rules derived from previous cases, leaving treatment of case law for further investigation. These passages support the claim that the initial program had no built-in means of incorporating emerging case law. (LLM, conf=0.88)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
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 Admissions Algorithm does not take into account geographic distribution when determining a match, considering only the ranked preferences of hospitals and students.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“Although others have pointed to bias in particular computer systems and have noted the general problem, we know of no comparable work that examines this phenomenon comprehensively and which offers a framework for understanding and remedying it.”
- Also stated in: § INTRODUCTION · hedged
“Although others have pointed to bias in particular computer systems and have noted the general problem [Johnson and Mulvey 1993;Moor 1985], we know of no comparable work that focuses exclusively on this phenomenon and examines it comprehensively.”
§ Bias in Computer Systems > Abstract
What the sources say
- Not matchedcites Johnson and Mulvey 1993
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Moor 1985 source
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclear- Also stated in: § INTRODUCTION · hedged
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
“From an analysis of actual cases, three categories of bias in computer systems have been developed: preexisting, technical, and emergent.”
- Also stated in: § INTRODUCTION · plain
“From an analysis of actual computer systems, we have developed three categories: preexisting bias, technical bias, and emergent bias.”
- Also stated in: § 2. FRAMEWORK FOR ANALYZING BIAS IN COMPUTER SYSTEMS · plain
“The framework that emerged from this methodology is comprised of three overarching categories-preexisting bias, technical bias, and emergent bias.”
§ Bias in Computer Systems > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: The 17 computer systems examined are representative enough to generalize the framework to computer systems generally.
- Also stated in: § INTRODUCTION · plain
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
“Emergent bias arises in a context of use.”
- Also stated in: § 2.3 Emergent Bias · plain
“emergent bias arises only in a context of use.”
§ Bias in Computer Systems > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § 2.3 Emergent Bias · plain
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
“Thus, in terms of overall service from the processor, these individuals with long-running programs are systematically disadvantaged.”
§ Bias in Computer Systems > 3.2 A Multilevel Scheduling Algorithm (MLSA)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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
“Thus the British Nationality Act is biased against the illegitimate descendants of British men.”
§ Bias in Computer Systems > 3.3 The British Nationality Act Program (BNAP)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- Probably not a substantial claim. 18
- It may state two separate claims as one. 15
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“For in the same text the authors also state (as noted above) that their goal is to translate the British Nationality Act so that”
§ Bias in Computer Systems > 5. CONCLUSION
What the sources say
- Partly supportedcites Sergot et al. 1986 sourceExperimental — accuracy not yet measured
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Partly supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“One charges that the Admissions Algorithm systematically favors hospital programs over medical students in cases of conflict [Graettinger and Peranson 1981b;Roth 1984;Sudarshan and Zisook 1981;Williams et al. 1981].”
§ Bias in Computer Systems > 3.1 The National Resident Match Program (NRMP)
What the sources say
- Not matchedcites Graettinger and Peranson 1981b
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Supportedcites Roth 1984 source
Section III (Theoretical Analysis; page unavailable) states that the NRMP algorithm produces the best stable outcome for hospitals and the worst for students when preferences are submitted as strict rank-order lists. Section IV (Contemporary Issues; page unavailable) also identifies whether the algorithm unfairly favors hospitals as an issue that had been raised, while noting a qualification when participants may be indifferent between matches. (LLM, conf=0.92)
- Not checked — no full text of the sourcecites Williams et al. 1981 source
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✗ Asserted: fails✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The authors may not put it forward as true. The paper’s conclusions may not depend on it.
- Not matched
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
“Technical bias arises from technical constraints or considerations.”
- Also stated in: § 2.1 Preexisting Bias · plain
“Technical bias arises from technical constraints or technical considerations.”
§ Bias in Computer Systems > Abstract
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
- Also stated in: § 2.1 Preexisting Bias · plain
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
“Preexisting bias has its roots in social institutions, practices, and attitudes.”
- Also stated in: § INTRODUCTION · plain
“Preexisting bias has its roots in social institutions, practices, and attitudes.”
§ Bias in Computer Systems > Abstract
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
- Also stated in: § INTRODUCTION · plain
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 this case, we ourselves would shy away from viewing this as a bias in the system because we think this discrimination can be defended as having a reasonable basis.Namely, the discrimination reflects the preferences of match participants, and it is reasonable in our view for employment decisions to be determined largely by the choices of employers and employees.”
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The discrimination against rural hospitals reflects the preferences of match participants.
- Rests on an assumption: The best teaching hospitals tend to be in urban areas, and urban areas tend to fill their positions far more effectively, and with better students, than rural hospitals.
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
“Emergent bias poses a potential problem for any legal expert system, especially in a society where legal systems depend on evolving case law.”
§ Bias in Computer Systems > 3.3 The British Nationality Act Program (BNAP)
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably 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
“We thus infer that in this case the biased designs were unintended and, instead, reflected gender-biases deeply rooted in the culture at large.”
§ Bias in Computer Systems > 4. CONSIDERATIONS FOR MINIMIZING BIAS IN COMPUTER SYSTEM DESIGN
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
“As Sabre and Apollo illustrate, biases in computer systems can be difficult to identify let alone remedy because of the way the technology engages and extenuates them.Computer systems, for instance, are comparatively inexpensive to disseminate, and thus, once developed, a biased system has the potential for widespread impact.If the system becomes a standard in the field, the bias becomes pervasive”
§ Bias in Computer Systems > INTRODUCTION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Huff and Cooper report that along a number of dimensions the designs proposed by subjects in the gender-unspecified group closely resembled the designs proposed by subjects who designed for boys and were significantly different from the designs proposed by subjects who designed for girls.”
§ Bias in Computer Systems > 4. CONSIDERATIONS FOR MINIMIZING BIAS IN COMPUTER SYSTEM DESIGN
What the sources say
- Not checked — no full text of the sourcecites Huff and Cooper 1987 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
“One source of this alleged bias lies in Sabre's and Apollo's algorithms for controlling search and display functions.In the algorithms, preference is given to”
§ Bias in Computer Systems > INTRODUCTION
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. 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
“Thus, a shift in context of use may well create difficulties for a new set of users.”
§ Bias in Computer Systems > 2.2 Technical Bias
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Thus, the computer systems systematically downgrade and, hence, are biased against international carriers who fly few, if any, internal U.S. flights, and against internal carriers who do not fly international flights [Fotos 1988;Ott 1988].”
§ Bias in Computer Systems > INTRODUCTION
What the sources say
- Not matchedcites Fotos 1988
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Ott 1988
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
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
“This bias typically emerges some time after a design is completed, as a result of changing societal knowledge, population, or cultural values.”
§ Bias in Computer Systems > 2.2 Technical Bias
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“One is that the interface design compounds the bias in the reservation systems.Lists of ranked flight options are displayed screen by screen.Each screen displays only two to five options.The advantage to a carrier of having its flights shown on the first screen is enormous since 90% of the tickets booked by travel agents are booked by the first screen display [Taib 1990].”
§ Bias in Computer Systems > INTRODUCTION
What the sources say
- Not matchedcites Taib 1990
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
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
“Or consider again the case of technical bias with the MLSA (which favors users with short jobs over long jobs).While technical bias is embedded in the program, the bias is a phenomenon of the system in use, in a context wherein users with short and long jobs outpace the system's capacity.”
§ Bias in Computer Systems > 4. CONSIDERATIONS FOR MINIMIZING BIAS IN COMPUTER SYSTEM DESIGN
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“In their study, subjects were asked to propose designs for software to teach seventh graders the correct use of commas.One group of subjects was asked to design the software for seventh-grade boys, the second group to design for seventh-grade girls, and the third group to design for seventh-graders, gender unspecified.”
§ Bias in Computer Systems > 4. CONSIDERATIONS FOR MINIMIZING BIAS IN COMPUTER SYSTEM DESIGN
What the sources say
- Not checked — no full text of the sourcecites Huff and Cooper 1987 source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
“This type of bias can enter a system either through the explicit and conscious efforts of individuals or institutions, or implicitly and unconsciously, even in spite of the best of intentions.”
§ Bias in Computer Systems > 2.1 Preexisting Bias
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. 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
“Sources of technical bias can be found in several aspects of the design process, including limitations of computer tools such as hardware, software, and peripherals; the process of ascribing social meaning to algorithms developed out of context; imperfections in pseudorandom number generation; and the attempt to make human constructs amenable to computers, when we quantify the qualitative, discret”
§ Bias in Computer Systems > 2.2 Technical Bias
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The constraint inherent in the NRMP algorithm, in which preference is given to hospital choices when conflicts in rankings occur, duplicates what happens in an actual admissions process without a computerized matching program”
§ Bias in Computer Systems > 3.1 The National Resident Match Program (NRMP)
What the sources say
- Not matchedcites Graettinger and Peranson 1981b
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Not matched
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
“Whatever ranking algorithm is used, if it systematically places certain airlines' flights on initial screens and other airlines' flights on later screens, the system will exhibit technical bias.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. 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
“They can also reflect the personal biases of individuals who have significant input into the design of the system, such as the client or system designer.”
§ Bias in Computer Systems > 2.1 Preexisting Bias
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“those responsible for maintaining the system responded conscientiously to this societal change and modified the system's algorithm to place couples more fairly [Roth 1990].”
§ Bias in Computer Systems > 4. CONSIDERATIONS FOR MINIMIZING BIAS IN COMPUTER SYSTEM DESIGN
What the sources say
- Not checked — no full text of the sourcecites Roth 1990 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
“Preexisting biases may originate in society at large, in subcultures, and in formal or informal, private or public organizations and institutions.”
§ Bias in Computer Systems > 2.1 Preexisting Bias
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably 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
“Should the accumulation of case law lead to changes in the way the Act is interpreted-say, by granting new subgroups British citizenship-BNAP would systematically misinform members of this subgroup regarding their status as British citizens [Leith 1986].”
§ Bias in Computer Systems > 3.3 The British Nationality Act Program (BNAP)
What the sources say
- Not checked — no full text of the sourcecites Leith 1986 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“In total, we examined 17 computer systems from diverse fields including banking, commerce, computer science, education, medicine, and law.”
§ Bias in Computer Systems > 2. FRAMEWORK FOR ANALYZING BIAS IN COMPUTER SYSTEMS
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We derived our framework by examining actual computer systems for bias.Instances of bias were identified and characterized according to their source, and then the characterizations were generalized to more abstract categories.These categories were further refined by their application to other instances of bias in the same or additional computer systems.”
§ Bias in Computer Systems > 2. FRAMEWORK FOR ANALYZING BIAS IN COMPUTER SYSTEMS
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
“although we have talked about bias in computer systems, the presence of bias is not so much a feature inherent in the system independent of the context of use, but an aspect of a system in use.”
§ Bias in Computer Systems > 4. CONSIDERATIONS FOR MINIMIZING BIAS IN COMPUTER SYSTEM DESIGN
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
The paper relies on this without support.
In cases of conflict, there is no good rationale for favoring hospital programs at the expense of medical students.
“We are inclined to answer yes because in cases of conflict there does not appear to be a good rationale for favoring hospital programs at the expense of students.”
The paper relies on this without support.
The discrimination against rural hospitals reflects the preferences of match participants.
“Namely, the discrimination reflects the preferences of match participants, and it is reasonable in our view for employment decisions to be determined largely by the choices of employers and employees.”
The paper relies on this without support.
The 17 computer systems examined are representative enough to generalize the framework to computer systems generally.
“In total, we examined 17 computer systems from diverse fields including banking, commerce, computer science, education, medicine, and law.”
The paper relies on this without support.
The illustrative examples in Table I portray plausible cases of bias.
“The illustrative examples portray plausible cases of bias.”
The paper relies on this without support.
The best teaching hospitals tend to be in urban areas, and urban areas tend to fill their positions far more effectively, and with better students, than rural hospitals.
“Because the best teaching hospitals tend to be in urban areas, the urban areas tend to fill their positions far more effectively, and with better students, than rural hospitals.”
The authors state this as an assumption.
The MLSA system is not saturated with too many users.
“assuming the system is not saturated with too many users”
The paper relies on this without support.
BNAP was designed in a research environment among people with sophisticated knowledge of immigration law.
“First, the system was designed in a research environment, among people with sophisticated knowledge of immigration law.”
The paper relies on this without support.
BNAP's users are likely to be at best paralegal or immigration counselors in Britain, if not lay persons in foreign countries with only limited access to British legal expertise.
“Its users, however, are likely to be at best paralegal or immigration counselors in Britain, if not lay persons in foreign countries with only limited access to British legal expertise.”
The paper relies on this without support.
Some of BNAP's queries require expert knowledge to answer.
“Some of the program's queries, for example, require expert knowledge to answer.”
The paper relies on this without support.
Nonexperts advising British citizen hopefuls are not alerted to alternative legal frameworks that complement the British Nationality Act.
“More generally, nonexperts advising British citizen hopefuls are not alerted to alternative legal frameworks that complement the British Nationality Act.”
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In the citing paper
“The multi-metric approach (Figure S1) makes explicit potential trade-offs and helps ensure the nonaccuracy desiderata are not treated as second-class citizens to accuracy (see Ref. 13).”
From Holistic Evaluation of Language Models (2023) · cites this paper as [13]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“research has repeatedly shown that these systems can in fact encode biases into decisions 124 .”
From Administrative law and the machines of government: judicial review of automated public-sector decision-making (2019) · cites this paper as B Friedman and H Nissenbaum 'Bias in Computer Systems' (1996)
The cited source says what the claim attributes to it.
In the citing paper
“The risk of”
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The cited source says what the claim attributes to it.
In the citing paper
“Bias or opacity are far from newly observed characteristics of computer systems generally [26, 11, 24], but these issues have been exacerbated by the rapidly expanding numbers of data-driven information systems entering decision-making processes.”
From Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making (2018) · cites this paper as [26]
In the citing paper
“These decisions may be based on factors of ethical importance (e.g., ethnic, gender, or religious grounds) and irrelevant to the decision-making at hand, or they may be relevant but legally protected as a nondiscriminatory characteristic (Friedman and Nissenbaum 1996).”
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In the citing paper
“On this account, the apparent problem to which data science is a solution determines whether it happens to serve morally defensible ends.”
From Problem Formulation and Fairness (2019) · cites this paper as [17]
In the citing paper
“Corresponding with [14] this outcome systematically disadvantages individuals of one gender by preferring the other at the topk positions.”
From FA*IR: A Fair Top-k Ranking Algorithm (2017) · cites this paper as [14]
In the citing paper
“While technical factors, such as biased data and design choices, play an important role, biases can also arise from the context of use, for example, how human users interpret system outputs or from a mismatch between the capabilities and values assumed in the design of the system and those of its actual users (Danks & London, 2017; Friedman & Nissenbaum, 1996).”
From Digital ageism: challenges and opportunities in artificial intelligence for older adults (2022) · cites this paper as Friedman & Nissenbaum, 1996
In the citing paper
“and to data protection, privacy, and other human rights issues 11”
From Administrative Law and the Machines of Government: Judicial Review of Automated Public-Sector Decision-Making (2018) · cites this paper as [11]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Table II gives conflicting years for its source
MinorThe table caption dates the Williams et al. source to 1991, while the paper's reference entry for that work dates it to 1981.
“Table II.The Simplest Case of Rank-Order Lists in which the Desires of Students and Programs are in Conflict (Reprinted from Williams et al. [1991], by permission of the New England Journal of Medicine)”
“18. WILLIAMS, K. J., WERTH, V. P., AND WOLFF, J. A. 1981. An analysis of the resident match.”
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Items 301–400 of 1,155
Lazzeretti, L. (2020) · European Planning Studies · doi:10.1080/09654313.2020.1836133 · Cited by 34
Fairness in Ranking: A SurveyPreprint
Zehlike, M., Yang, K., & Stoyanovich, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.14000 · Cited by 114
ChatGPT, the perfect virtual teaching assistant? Ideological bias in learner-chatbot interactions
Poucke, M. V. (2024) · Computers & composition/Computers and composition · doi:10.1016/j.compcom.2024.102871 · Cited by 46
Fairness for machine learning software in education: A systematic mapping study
Pham, N., Ngoc, H. P., & Nguyen‐Duc, A. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112244 · Cited by 48
ASTRAEA: Grammar-based Fairness Testing
Soremekun, E., Udeshi, S., & Chattopadhyay, S. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2022.3141758 · Cited by 41
“nate certain individuals or groups over others [25], [34].”
BackgroundMulti-disciplinary fairness considerations in machine learning for clinical trials
Chien, I., Deliu, N., Turner, R. E., Weller, A., Villar, S. S., & Kilbertus, N. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533154 · Cited by 35
“3 Fairness in Machine Learning The study of fairness in machine learning aims to ensure that ML models do not “systematically and unfairly discriminate against certain individuals or groups of individuals in favor of others” [88].”
BackgroundInformational Privacy, A Right to Explanation, and Interpretable AI
Kim, T. W., & Routledge, B. (2018) · IEEE Symposium on Privacy-Aware Computing (PAC) · doi:10.1109/pac.2018.00013 · Cited by 30
Milestones in the History of Information and Computer Ethics
Bynum, T. W. (2008) · doi:10.1002/9780470281819.ch2 · Cited by 30
Data Responsibly: Fairness, Neutrality and Transparency in Data AnalysisPreprint
Stoyanovich, J., Abiteboul, S., & Miklau, G. (2016) · HAL (Le Centre pour la Communication Scientifique Directe) · doi:10.5441/002/edbt.2016.103 · Cited by 28
“Big data technology holds incredible promise of improving people’s lives, accelerating scientific discovery and innovation, and bringing about positive societal change.”
BackgroundCramer, H. S. M., Garcia-Gathright, J. I., Reddy, S., Springer, A., & Bouyer, R. T. (2019) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3290607.3299057 · Cited by 27
“In our case, we evaluated a selection of bias frameworks (including Friedman & Nissenbaum [6], Olteanu et al [7], Baeza-Yates [1], and Crawford [3]), and translated them into a summary of bias types.”
BackgroundMethodCritical Perspectives on Governance Mechanisms for AI/ML Systems
Stark, L., Greene, D., & Hoffmann, A. L. (2020) · Springer eBooks · doi:10.1007/978-3-030-56286-1_9 · Cited by 32
Brubaker, J. R., & Hayes, G. R. (2011) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/1958824.1958881 · Cited by 28
“Often these biases can be invisible or go unnoticed by the users or even the developers [4,18,19].”
BackgroundSimulations for Urban Planning: Designing for Human Values
Davis, J., Lin, P., Borning, A. H., et al. (2006) · Computer · doi:10.1109/mc.2006.324 · Cited by 28
Self-citationTowards Environmentally Equitable AI via Geographical Load Balancing
Li, P., Yang, J., Wierman, A., & Ren, S. (2024) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3632775.3661938 · Cited by 47
Racial bias in AI-generated images
Yang, Y. (2025) · AI & Society · doi:10.1007/s00146-025-02282-1 · Cited by 31
Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective
Castaneda, J., Jover-Leal, A., Calvet, L., Torrado, S. Y., Juan, A. A., & Sáinz, M. (2022) · Algorithms · doi:10.3390/a15090303 · Cited by 25
“Despite this, neither unfair discrimination alone gives rise to bias unless it occurs systematically, nor does systematic discrimination generate bias unless it is accompanied by an unfair outcome [29].”
BackgroundInfluentialNLPositionality: Characterizing Design Biases of Datasets and Models
Santy, S., Liang, J. T., Bras, R. J. L., Reinecke, K., & Sap, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.505 · Cited by 74
Kannabiran, G., & Petersen, M. G. (2010) · Nordic Conference on Human-Computer Interaction · doi:10.1145/1868914.1869007 · Cited by 27
“It has been well established that biases are always inbuilt into the designed systems [6, 10].”
BackgroundAssessing and Addressing Algorithmic Bias - But Before We Get TherePreprint
Garcia-Gathright, J. I., Springer, A., & Cramer, H. S. M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.03332 · Cited by 27
“Algorithmic fairness and the understanding of its outcomes was anticipated as a research topic as much as twenty years ago [3].”
BackgroundMethodPorlezza, C. (2023) · Journalism · doi:10.1177/14648849231190232 · Cited by 32
“Similarly, algorithms pose speci fi c challenges regarding accountability: not only are there risks of biases (Friedman and Nissenbaum, 1996) that can impact data analysis and therefore also news reporting (Gillespie, 2014), but the issue also concerns the way to handle these risks in terms of…”
BackgroundSeniors extend understanding of what constitutes universal values
Burmeister, O. K., Weckert, J., & Williamson, K. (2011) · Journal of Information Communication and Ethics in Society · doi:10.1108/14779961111191048 · Cited by 26
Zero-Mean Convolutions for Level-Invariant Singing Voice Detection
Schlüter, J., & Lehner, B. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1492412 · Cited by 26
How Do People Change Their Technology Use in Protest?
Li, H., Vincent, N., Tsai, J., Kaye, J., & Hecht, B. J. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359189 · Cited by 26
“This is an approach that would further supplement existing user-centered design approaches, such as participatory design [42] and value-sensitive design [14], and relates to the notion of “heuristic preventive design” introduced at CSCW last year [32].”
MethodAn Investigation of Biases in Web Search Engine Query Suggestions
Bonart, M., Samokhina, A., Heisenberg, G., & Schaer, P. (2019) · Online information review (Print) · doi:10.1108/oir-11-2018-0341 · Cited by 24
“Friedman and Nissenbaum (1996) were among the first who studied the phenomenon of biases in computer systems in general.”
BackgroundA “psychopathic” Artificial Intelligence: the possible risks of a deviating AI in Education
Zanetti, M., Iseppi, G., & Cassese, F. P. (2019) · Research on Education and Media · doi:10.2478/rem-2019-0013 · Cited by 32
The Concept of Fairness in Relation to Women Transport Users
Hail, Y., & McQuaid, R. W. (2021) · Sustainability · doi:10.3390/su13052919 · Cited by 33
“Although referring to computer processes, Friedman and Nissenbaum [18] set out a useful definition of a process being biased (and so potentially unfair) “if it systematically and unfairly discriminate against certain individuals or groups of individuals in favor of others.”
MethodSchelenz, L., Bison, I., Busso, M., GÖTZEN, A. D., Gática-Pérez, D., Giunchiglia, F., Meegahapola, L., & Ruíz-Correa, S. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462595 · Cited by 25
“First, designers acknowledge that values are embedded consciously or unconsciously in technologies (Friedman and Nissenbaum 1996).”
BackgroundYour face is not new to me – Regulating the surveillance power of facial recognition technologies
Mobilio, G. (2023) · Internet Policy Review · doi:10.14763/2023.1.1699 · Cited by 26
Cameron, S., Franks, P., & Hamidzadeh, B. (2023) · Journal on Computing and Cultural Heritage · doi:10.1145/3594728 · Cited by 25
“Computer says no”: Algorithmic decision support and organisational responsibility
Adensamer, A., Gsenger, R., & Klausner, L. D. (2021) · Journal of Responsible Technology · doi:10.1016/j.jrt.2021.100014 · Cited by 32
Kraft, A., & Soulier, E. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658981 · Cited by 25
“Social bias is observed when language models " systematically and unfairly discriminate against certain individuals or groups of individuals in favor of others" [23, p. 332].”
BackgroundFairness in the Eyes of the Data: Certifying Machine-Learning Models
Segal, S., Adi, Y., Benny Pinkas, B., Baum, C., Ganesh, C., & Keshet, J. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462554 · Cited by 31
“Related work Fairness in algorithms was first investigated by Friedman and Nissenbaum [12].”
MethodEthics and AI Issues: Old Container with New Wine?
Niederman, F., & Baker, E. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10305-1 · Cited by 21
“Before the onslaught of the current waves of AI and analytics applications, Friedman and Nissenbaum (1996) defined three types of bias: preexisting based on social practices and attitudes likely embedded in data used for ‘training’; technical based on design constraints in hardware and software…”
Teaching Inclusive Design Skills with the CIDER Assumption Elicitation Technique
Oleson, A., Solomon, M., Perdriau, C., et al. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3549074 · Cited by 25
“Designers, being human, inherently embed their biases and values into the artifacts they create [28], as well as their assumptions about users’ capabilities, resources, and interaction styles [10, 21].”
BackgroundEvaluating Age Bias In E-commerce
McIntosh, J., Du, X., Wu, Z., Truong, G., Ly, Q., How, R., Viswanathan, S., & Kanij, T. (2021) · International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE) · doi:10.1109/chase52884.2021.00012 · Cited by 29
Sarra, C. (2020) · Global Jurist · doi:10.1515/gj-2020-0003 · Cited by 24
“The very vagueness of that expression can be exploited to offer a sort of remedy to the problem of algorithmic potential discriminatory capacity (Sweeney 2013; Henderson 2017; Friedman and Nissenbaum 1996).”
BackgroundALGORITHMIC BIAS IN AI-ENHANCED EDUCATION: CULTURAL DIMENSIONS AND PEDAGOGICAL IMPACT
Fadil, H. O. C. A., & Nuredin, A. (2025) · Symposium Proceedings · doi:10.55843/isl2025symp163h · Cited by 25
Lu, A. J., Marcu, G. M., Ackerman, M. S., & Dillahunt, T. R. (2021) · Designing Interactive Systems Conference · doi:10.1145/3461778.3462084 · Cited by 30
Consumer Artificial Intelligence Mishaps and Mitigation Strategies
Saeedi, S., Fong, A. C. M., Mohanty, S. P., Gupta, A. K., & Carr, S. (2021) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2021.3075329 · Cited by 25
Context- and Fairness-Aware In-Process Crowdworker Recommendation
Wang, J., Yang, Y., Wang, S., Hu, J., & Wang, Q. (2022) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3487571 · Cited by 24
Towards Implementing Responsible AI
Sanderson, C., Lu, Q., Douglas, D., Xu, X., Zhu, L., & Whittle, J. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10021121 · Cited by 28
“There is considerable overlap between the two: non-AI software may also contain biases, infringe individual privacy, and be used for harmful purposes [9], [14].”
BackgroundGrep-BiasIR: A dataset for investigating gender representation bias in information retrieval results
Krieg, K., Parada-Cabaleiro, E., Medicus, G., Lesota, O., Schedl, M., & Rekabsaz, N. (2023) · Proceedings of the 2023 Conference on Human Information Interaction and Retrieval · doi:10.1145/3576840.3578295 · Cited by 31
Kristensen, L. M., & Hartley, J. M. (2023) · Media and Communication · doi:10.17645/mac.v11i2.6388 · Cited by 30
Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned
Bird, S., Hutchinson, B., Kenthapadi, K., Kıcıman, E., & Mitchell, M. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308560.3320086 · Cited by 26
Artificial Intelligence and Discrimination: Discriminating Against Discriminatory Systems
Tischbirek, A. (2019) · Springer eBooks · doi:10.1007/978-3-030-32361-5_5 · Cited by 28
Information and Institutional Change: The Case of Digital Libraries
Agre, P. E. (2003) · The MIT Press eBooks · doi:10.7551/mitpress/2424.003.0013 · Cited by 24
“Section 6 concludes by summarizing some of the positive contributions by which digital libraries might be evaluated.”
BackgroundAttention: How High-Frequency Trading Improves Price Efficiency Following Earnings AnnouncementsPreprint
Chakrabarty, B., Moulton, P. C., & Wang, X. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2634621 · Cited by 24
Do I Look Like a Criminal? Examining how Race Presentation Impacts Human Judgement of Recidivism
Mallari, K., Inkpen, K., Johns, P., Tan, S., Ramesh, D., & Kamar, E. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376257 · Cited by 24
New Digital Inequalities. Algorithms Divide
Ragnedda, M. (2020) · Springer eBooks · doi:10.1007/978-3-030-49079-9_4 · Cited by 28
Goodin, R. E. (2023) · doi:10.1093/oso/9780192888204.001.0001 · Cited by 25
A design perspective on how to tackle gender biases when developing AI-driven systems
González, A. S., & Rampino, L. (2024) · AI and Ethics · doi:10.1007/s43681-023-00386-2 · Cited by 31
“If the system is complex, and most are, biases can remain hidden in the code, difficult to pinpoint or explicate, and not necessarily disclosed to users or their clients [14, p. 331].”
Identifying and Categorizing Bias in AI/ML for Earth Sciences
McGovern, A., Bostrom, A., McGraw, M. C., Chase, R. J., Gagne, D. J., Ebert‐Uphoff, I., Musgrave, K. D., & Schumacher, A. (2024) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-23-0196.1 · Cited by 30
“For in-depth discussions on such biases in society in general (and why we chose the names), see March and Olsen (1984), Friedman and Nissenbaum (1996), Henry (2010), Suresh and Guttag (2021), and Schwartz et al. (2022).”
BackgroundTrautwein, Y., Zechiel, F., Coussement, K., Meire, M., & Büttgen, M. (2025) · Journal of Business Research · doi:10.1016/j.jbusres.2025.115298 · Cited by 31
A case study of post-deployment user feedback triage
Ko, A. J., Lee, M. J., Ferrari, V., Ip, S., & Tran, C. (2011) · International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE) · doi:10.1145/1984642.1984644 · Cited by 23
“For example, Naur described programming as building a theory of how a solution relates to the world [22]; design theory perspectives view software designs as value judgements [12], projecting “ideal” users [3] and expecting users to conform to them.”
BackgroundA Formative Study of Interactive Bias Metrics in Visual Analytics Using Anchoring Bias
Wall, E., Blaha, L. M., Paul, C. L., & Endert, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29384-0_34 · Cited by 23
Friedman, B., & Nissenbaum, H. F. (1996) · doi:10.1145/257089.257434 · Cited by 23
Self-citationMaking sense of data ethics. The powers behind the data ethics debate in European policymaking
Hasselbalch, G. (2019) · Internet Policy Review · doi:10.14763/2019.2.1401 · Cited by 26
ORES: Lowering Barriers with Participatory Machine Learning in WikipediaPreprint
Halfaker, A., & Geiger, R. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.05189 · Cited by 22
“However, CSCW and HCI scholarship has often addressed issues of algorithmic governance in a broader socio-technical lens, which is in line with the field’s longstanding orientation to participatory design, value-sensitive design, values in design, and collaborative work [29, 30, 55, 91, 92].”
Dirty Data Labeled Dirt Cheap: Epistemic Injustice in Machine Learning Systems
Hull, G. (2022) · Ethics and Information Technology · doi:10.1007/s10676-023-09712-y · Cited by 32
Enabling Accountability of Algorithmic Media: Transparency as a Constructive and Critical Lens
Diakopoulos, N. A. (2017) · Studies in big data · doi:10.1007/978-3-319-54024-5_2 · Cited by 25
Analyzing Demographic Bias in Artificially Generated Facial Pictures
Salminen, J. O., Jung, S., Chowdhury, S. A., & Jansen, B. J. J. (2020) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3334480.3382791 · Cited by 27
“Artificial image creation can manifest forms of algorithmic bias, defined as systematic and unfair discrimination against certain groups in favor of others [8].”
BackgroundTowards Cross-Lingual Generalization of Translation Gender Bias
Cho, W. I., Kim, J., Yang, J., & Kim, N. S. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445907 · Cited by 24
“Ever since Friedman and Nissenbaum [11] gave a definition of computer system bias, researchers have examined stereotypes thereof, its influence on society, and how to remove it.”
BackgroundMethodThe Givenness of the Human Learning Experience and Its Incompatibility with Information Analytics
Lundie, D. C. (2015) · Educational Philosophy and Theory · doi:10.1080/00131857.2015.1052357 · Cited by 22
“…use depends on the effective interaction between agents and mediating technologies (Ess, 2009), if the nature of one set of agents - the human learners - is fundamentally misunderstood in design, this can lead to bias in system use, with unforeseen social consequences (Friedman & Nissenbaum, 1996).”
BackgroundBunker, D. (2001) · Information Systems Frontiers · doi:10.1023/a:1011443406476 · Cited by 22
Closing the AI Knowledge GapPreprint
Epstein, Z., Payne, B. H., Shen, J. H., Dubey, A., Felbo, B., Groh, M., Obradovich, N., Cebrián, M., & Rahwan, I. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.07233 · Cited by 39
“Due to their ubiquity and potential harm, the increasingly emergent and often unintended properties of these AI systems have garnered widespread attention in both the public and academic spheres [ O’Neil, 2017; Friedman and Nissenbaum, 1996 ] .”
BackgroundEthical and Legal Challenges of AI in Marketing: An Exploration of SolutionsPreprint
Kumar, D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4396132 · Cited by 22
From Bias to Repair: Error as a Site of Collaboration and Negotiation in Applied Data Science Work
Lin, C. K., & Jackson, S. J. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579607 · Cited by 22
Data Statements: From Technical Concept to Community Practice
McMillan-Major, A., Bender, E. M., & Friedman, B. (2024) · ACM Journal on Responsible Computing · doi:10.1145/3594737 · Cited by 21
“In their initial work, Bender and Friedman began with a conceptual investigation, drawing on the definition of bias presented in an article by Friedman and Nissenbaum [11] as “systematic” and “unfair discrimination.”
BackgroundSelf-citationMitigating Cognitive Biases in Machine Learning Algorithms for Decision Making
Harris, C. G. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366424.3383562 · Cited by 22
“However, this area of research is relatively new, finding its roots in a 1996 paper on bias in computer systems by Friedman and Nis-senbaum [4] and a 2008 paper discussing discrimination in machine learning by Pedreshi et al. in [5].”
BackgroundGanju, K. K., Atasoy, H., & Pavlou, P. A. (2021) · Management Science · doi:10.1287/mnsc.2021.4002 · Cited by 26
Gender Differences in Mouse and Cursor Movements
Yamauchi, T., Seo, J. H., Jett, N., Parks, G., & Bowman, C. D. (2015) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2015.1072787 · Cited by 21
“This assumption is problematic and even harmful because the bias that is ignored in the design phase will become pervasive in the marketplace, creating possibly detrimental side effects (Friedman & Nissenbaum, 1996).”
Disruptions, Dilemmas and Paradoxes: Ethical Matter(s) in Design Research
Nathan, L. P., Thieme, A., Tatar, D. G., & Branham, S. M. (2016) · Interacting with Computers · doi:10.1093/iwc/iww034 · Cited by 21
“For decades, scholars have articulated the importance of investigating the ethical implications of designing interactive technologies (e.g. Friedman and Nissenbaum, 1996).”
BackgroundEthical Implications and Accountability of AlgorithmsPreprint
Martin, K. E. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3056716 · Cited by 21
Building, Shifting, & Employing Power: A Taxonomy of Responses From Below to Algorithmic Harm
DeVrio, A., Eslami, M., & Holstein, K. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658958 · Cited by 21
“It can be challenging to pin down issues and productive directions forward due to the opaque nature of algorithmic systems and the emergent nature of algorithmic behavior in real-world use contexts [32, 58].”
BackgroundGhamisi, P., Yu, W., Marinoni, A., Gevaert, C. M., Persello, C., Selvakumaran, S., Girotto, M., Horton, B. P., Rufin, P., Hostert, P., et al. (2025) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2025.3529726 · Cited by 50
“Friedman and Nissenbaum [26] refer to biases that cause unfair and systematic discrimination of individuals or subgroups of the population.”
Reflexive Design for Fairness and Other Human Values in Formal Models
Fish, B., & Stark, L. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462518 · Cited by 32
Sapraz, M., & Han, S. (2021) · Sustainability · doi:10.3390/su13116240 · Cited by 20
Maschinelles Lernen und Diskriminierung: Probleme und Lösungsansätze
Hagendorff, T. (2019) · Österreichische Zeitschrift für Soziologie · doi:10.1007/s11614-019-00347-2 · Cited by 21
Transparency of Automated Decisions in the GDPR: An Attempt for SystemisationPreprint
Bayamlıoğlu, E. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3097653 · Cited by 20
Global, N., & Kazanskaia, A. N. (2025) · doi:10.64357/empowering-womens-voices-2025 · Cited by 19
Lacroux, A., & Martin‐Lacroux, C. (2021) · Management & Avenir · doi:10.3917/mav.122.0121 · Cited by 32
Addressing the Risks That Trade Secret Protections Pose for Health and Rights.
Durkin, A., Maria, A. S., Willmore, B., et al. (2021) · PubMed · Cited by 19
Image fairness in deep learning: problems, models, and challenges
Tian, H., Zhu, T., Liu, W., & Zhou, W. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07136-1 · Cited by 20
“Fortunately, studies on fairness have a long history [9], so there is much documented evidence of not only the biases that lead to unfair predictions [10–12] but also a plethora of approaches to overcome those biases.”
BackgroundAI bias in Human-Robot Interaction: An evaluation of the Risk in Gender Biased Robots
Hitron, T., Megidish, B., Todress, E., Yaar, N. M., & Erel, H. (2022) · IEEE RO-MAN · doi:10.1109/ro-man53752.2022.9900673 · Cited by 26
“This phenomenon has been coined as the AI bias, and it refers to occasions where the outcome of the AI’s machine learning model encapsulates stereotypes leading to favoring or disfavoring particular segments [15], [16], [17].”
BackgroundAlgorithmic bias: Social science research integration through the 3-D Dependable AI Framework
Ukanwa, K. (2024) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2024.101836 · Cited by 24
Chen, X., Yao, L., McAuley, J., Zhou, G., & Wang, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.03540 · Cited by 23
Agile experience: communication and collaboration in agile software development teams
Whitworth, E. (2006) · Carleton University's Institutional Repository (MacOdrum Library, Carleton University) · doi:10.22215/etd/2006-08517 · Cited by 19
Abbasi, A., Dillon, R. L., Rao, H., & Sheng, O. R. L. (2024) · Information Systems Research · doi:10.1287/isre.2024.intro.v35.n2 · Cited by 19
Wearables, the Marketplace and Efficiency in Healthcare: How Will I Know That You’re Thinking of Me?
Howard, M. (2021) · Philosophy & Technology · doi:10.1007/s13347-021-00473-4 · Cited by 18
Evaluation of Gender Bias in Facial Recognition with Traditional Machine Learning Algorithms
Atay, M., Gipson, H., Gwyn, T., & Roy, K. (2021) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci50451.2021.9660186 · Cited by 20
Showkat, D., Smith, A. D. R., Wang, L., & To, A. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581010 · Cited by 25
“and those that employ similar approaches are moving us towards – establishing social justice, eradicating biases [47], bringing equality, and repairing harm to those who were historically excluded from wealth accumulation, i.”
BackgroundInfluentialLin, C., Gao, Y., Ta, N., Li, K., & Fu, H. (2023) · Telematics and Informatics · doi:10.1016/j.tele.2023.102068 · Cited by 20
Constantinides, M., Tahaei, M., Quercia, D., Stumpf, S., Madaio, M., Kennedy, S., Wilcox, L. G., Jessica Vitak, B., Cramer, H. S. M., Bogucka, E. P., et al. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3643979 · Cited by 28
“Although the significance of the human element in AI has been acknowledged for some time—as evidenced by the early works of Friedman [15] and Suchman [32]—it is only in recent years that the field of human-centered computing research has significantly engaged with integrating people and their…”
A stake in the issue of homelessness
Koepfler, J. A., Shilton, K., & Fleischmann, K. R. (2013) · International Conference on Communities and Technologies (C&T) · doi:10.1145/2482991.2482994 · Cited by 18
“Values also shape and are shaped by our information behaviors and technology adoption practices [14,18].”
BackgroundYouth Data Literacy: Teen Perspectives on Data Created with Social Media and Mobile Devices
Acker, A., & Bowler, L. (2018) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2018.243 · Cited by 18
TuringBox: An Experimental Platform for the Evaluation of AI Systems
Epstein, Z., Payne, B. H., Shen, J. H., Hong, C., Felbo, B., Dubey, A., Groh, M., Obradovich, N., Cebrian, M., & Rahwan, I. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/851 · Cited by 17
“As the proliferation of artificial intelligence continues, algorithmic bias within AI systems has become a popular topic of scientific study [O’Neil, 2017; Friedman and Nissenbaum, 1996; Sweeney, 2013; Hannak et al. , 2014].”
BackgroundTóth, A. K. (2019) · Masaryk University Journal of Law and Technology · doi:10.5817/mujlt2019-2-9 · Cited by 18
Examining user surprise as a symptom of algorithmic filtering
Rader, E. J. (2016) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2016.10.005 · Cited by 33
“Friedman and Nissenbaum (1996) defined algorithmic bias as systematically and inappropriately denying opportunities or assigning undesirable outcomes .”
BackgroundUnintentional power in the design of computer systems
Huff, C. (1996) · ACM SIGCAS Computers and Society · doi:10.1145/242889.242890 · Cited by 18
The Ethics of Online Controlled Experiments (A/B Testing)
Polonioli, A., Ghioni, R., Greco, C., Juneja, P., Tagliabue, J., Watson, D., & Floridi, L. (2023) · Minds and Machines · doi:10.1007/s11023-023-09644-y · Cited by 20
“A biased system “systematically and unfairly discriminates against certain individuals or groups of individuals in favor of others” (Friedman & Nissenbaum, 1996).”
Metadata
- Authors
- Friedman, B., & Nissenbaum, H.
- Affiliations
- Colby College (US)
- Princeton University (US)
- Venue
- ACM Transactions on Information Systems
- Published
- 1996-07
- Volume
- 14
- Issue
- 3
- Pages
- 330-347
- ISSN
- 1046-8188 (ISSN-L)
- 1558-2868
- OpenAlex
- W2006447892
- Cited by
- 1,203
- References
- 13
- Citations by year
- 2026: 102 · 2025: 149 · 2024: 151 · 2023: 121 · 2022: 126 · 2021: 133 · 2020: 79 · 2019: 70 · 2018: 50 · 2017: 21 · 2016: 29 · 2015: 18 · 2014: 18 · 2013: 16 · 2012: 8
- FWCI
- 17.25
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Transactions on Information Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Systems Theories and Implementation
- Social Sciences › Law › Artificial Intelligence in Law
- Keywords
- technical bias, social institutions, technological constraints, societal impact of technology
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 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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Statistics and arithmetic are recomputed, not judged.
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