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)”
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Items 601–700 of 1,155
Algorithms for All: Has Digitalization in the Mortgage Market Expanded Access to Homeownership?Preprint
Perry, V. G., & Martin, K. E. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4126409 · Cited by 7
Fostering fairness in image classification through awareness of sensitive data
Colakovic, I., & Karakatič, S. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113090 · Cited by 6
Quantifying Bias in Text-to-Image Generative Models
Vice, J., Akhtar, N., Hartley, R. I., & Mian, A. S. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3572115 · Cited by 18
Responses to the Initial Hype: ChatGPT Supporting Teaching, Learning, and Scholarship?
Cefa, B., Macgilchrist, F., ElGamal, H., Bai, J. Y. H., Zawacki‐Richter, O., & Loglo, F. S. (2025) · Open Praxis · doi:10.55982/openpraxis.17.2.872 · Cited by 10
Understanding how users may work around algorithmic bias
Overbye-Thompson, H. E., & Rice, R. E. (2025) · AI & Society · doi:10.1007/s00146-025-02498-1 · Cited by 12
Sorato, D., Lundsteen, M., Ventura, C. C., & Zavala‐Rojas, D. (2024) · Journal of Computational Social Science · doi:10.1007/s42001-023-00243-6 · Cited by 8
Employing Hybrid AI Systems to Trace and Document Bias in ML Pipelines
Russo, M., Chudasama, Y., Purohit, D., Sawischa, S. F., & Vidal, M.-E. (2024) · IEEE Access · doi:10.1109/access.2024.3427388 · Cited by 9
“Bias refers to a systematic and consistent deviation from the true value or objective reality in decision-making, judgment, or data analysis that can lead to detrimental or discriminatory outcomes [1].”
BackgroundProperties of Group Fairness Measures for Rankings
Schumacher, T., Lutz, M., Sikdar, S., & Strohmaier, M. (2024) · ACM Transactions on Social Computing · doi:10.1145/3674883 · Cited by 10
“In that context, it is worth noting that there are more informative and fine-grained classification frameworks available to assist in this process [26, 54].”
The Digital Siren’s Call: Accepting Unethical AI Advice
Xu, C., Xu, H., Sun, Y.-Q., & Xiong, W. (2024) · International journal of human computer interactions · doi:10.1080/10447318.2024.2400396 · Cited by 9
“Entrepreneurs developing AI applications should incorporate ethical considerations into their design and development processes (Friedman & Nissenbaum, 1996).”
User-Centered Design in AI Applications: A Systematic Literature Review
Wongso, B., Lienaka, K. N., Firstian, V., & Magdalena, Y. (2024) · International Conference on Information Management and Technology (ICIMTech) · doi:10.1109/icimtech63123.2024.10780823 · Cited by 6
“For AI developers to put into effect UCD correctly, several guidelines can be made [36].”
InfluentialGender Effects in Mobile Application Development
Lopes, M. R., & Vogel, C. M. (2020) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms49158.2020.9209325 · Cited by 6
“Those issues are caused by the lack of user research [1], [2], [8], the misrepresentation of female users during user research [9], and bias in development [3].”
BackgroundAssessing Gender Bias in Wikipedia: Inequalities in Article Titles
Faleńska, A., & Çetinoğlu, Ö. (2021) · Workshop on Gender Bias in Natural Language Processing (GeBNLP) · doi:10.18653/v1/2021.gebnlp-1.9 · Cited by 6
“Gender bias can be defined as a systematic preference or discrimination against people of a particular gender (Friedman and Nissenbaum, 1996).”
BackgroundBewertung in und durch digitale Infrastrukturen
Krüger, A. K., Heßelmann, F., & Hartstein, J. (2021) · Organisationssoziologie · doi:10.1007/978-3-658-31549-8_5 · Cited by 6
Häußler, H. (2021) · Libri · doi:10.1515/libri-2021-0095 · Cited by 6
Kaufmann, M. (2022) · Wiley Interdisciplinary Reviews Forensic Science · doi:10.1002/wfs2.1470 · Cited by 6
Zabel, S., & Otto, S. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642404 · Cited by 6
What About Justice and Power Imbalances? A Relational Approach to Ethical Risk Assessments for AI
Krijger, J. (2024) · Digital Society · doi:10.1007/s44206-024-00139-6 · Cited by 9
“That is, there is the potential for systematic discrimination against certain marginalized individuals or groups of individuals based on the inappropriate use of certain traits, characteris - tics or attributes such as disabilities, race, gender, and sexual orientation (Friedman & Nissenbaum, 1996).”
Prakash, A., & Dé, R. (2007) · Journal of the Association for Information Systems · Cited by 6
Smith, K. L., McPhail, B., Ferenbok, J., et al. (2011) · Surveillance & Society · doi:10.24908/ss.v9i1/2.4108 · Cited by 6
What Users of Virtual Social Networks Value about Social Interaction: The Case of GreyPath
Burmeister, O. K. (2009) · Palgrave Macmillan UK eBooks · doi:10.1057/9780230250888_7 · Cited by 6
Banerji, R. (2018) · Human-computer interaction series · doi:10.1007/978-3-319-73356-2_4 · Cited by 7
“Nevertheless, as numerous critics of science and technology research have suggested (Friedman and Nissenbaum 1996; Nissenbaum 2001; Edwards 1997; Lewis 2000; Helmreich 2001; Forsythe 2002; Suchman 2006; Seaver 2012; Wilf 2013a, b; Burrell 2016; Seyfert and Roberge 2016), AI systems often reproduce…”
BackgroundWolf, M. J., Grodzinsky, F. S., & Miller, K. W. (2012) · Philosophy of engineering and technology · doi:10.1007/978-94-007-4292-5_2 · Cited by 6
“For example, an ATM that relies heavily on written instructions may be deployed in a neighborhood with an illiterate population (Friedman and Nissenbaum 1996 ) .”
BackgroundEngaging and Informing Citizens with Household Indicators
Davis, J. (2008) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2008.145 · Cited by 7
“To mitigate pre-existing biases, Friedman and Nissenbaum suggest identifying groups who suffer from societal biases and including them in empirical investigations [10].”
BackgroundInfluentialThe value implications of the practice of paid search
Zimmer, M. T. (2006) · Bulletin of the American Society for Information Science and Technology · doi:10.1002/bult.1720320211 · Cited by 6
Values in Design - Building Bridges between RE, HCI and Ethics
Detweiler, C., Pommeranz, A., van den Hoven, J., & Nissenbaum, H. F. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23768-3_145 · Cited by 6
Self-citationMinimizing bias in computer systems
Friedman, B., & Nissenbaum, H. F. (1995) · doi:10.1145/223355.223780 · Cited by 6
Self-citationDirty Data Labeled Dirt Cheap: Epistemic Injustice in Machine Learning SystemsPreprint
Hull, G. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4137697 · Cited by 6
Bias of AI-Generated Content: An Examination of News Produced by Large Language ModelsPreprint
Fang, X., Che, S., Mao, M., Zhang, H., Zhao, M. L., & Zhao, X. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4574226 · Cited by 10
Artificial Intelligence for Climate Security: Possibilities and Challenges
Kim, K., & Vincent, B. (2023) · doi:10.55163/qdse8934 · Cited by 6
Erfanian, M., Jagadish, H. V., & Asudeh, A. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3681954.3682014 · Cited by 10
Destination (Un)Known: Auditing Bias and Fairness in LLM-Based Travel Recommendations
Andreev, H., Kosmas, P., Livieratos, A. D., Theocharous, A. L., & Zopiatis, A. (2025) · AI · doi:10.3390/ai6090236 · Cited by 11
“Representational & societal (pre-existing) biases Demographic “Pre-existing bias” Friedman & Nissenbaum, 1996 [22] and representational harms vs. allocative harms due to undetected biases in big data (Barocas & Selbst, 2016) [25] Any user-visible , explanations These reflect historical power…”
Jarrahi, M. H., Karami, A., Conway, P., Memariani, A., & Lutz, C. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.103127 · Cited by 6
“Friedman and Nissenbaum (1996) argued in their foundational research that all computer systems embody particular values and perspectives.”
InfluentialTrustworthy AI in digital health: a comprehensive review of robustness and explainability
Mamun, A., Soumma, S. B., & Ghasemzadeh, H. G. (2026) · Progress in Biomedical Engineering · doi:10.1088/2516-1091/ae4e74 · Cited by 10
“…inequities, that skew datasets, whereas explicit bias res-ults from deliberate exclusions (e.g. training only on male patients or only on female patients when the health issue under consideration affects both male and female populations), leading to poor generalization and unequal outcomes [72].”
(Dis)Obedience in Digital Societies
Quadflieg, S., Neuburg, K., & Nestler, S. (2022) · Digitale Gesellschaft · doi:10.1515/9783839457634 · Cited by 8
Chen, T. T.-Y. (2024) · Big Data & Society · doi:10.1177/20539517241296038 · Cited by 5
“Following the theories of “ formatting ” (Koopman, 2019) and bias production (Friedman and Nissenbaum, 1996), which posit that the politics of information can be analyzed from inputs, technical systems, to outputs, I will analyze the implementation of open government data policy through its three…”
Algorithms under the Reign of Probability
Dryer, T. (2018) · IEEE Annals of the History of Computing · doi:10.1109/mahc.2018.012171275 · Cited by 5
The Many Facets of Data Equity
Jagadish, H. V., Stoyanovich, J., & Howe, B. (2022) · Journal of Data and Information Quality · doi:10.1145/3533425 · Cited by 9
“To appreciate this, consider another useful framing proposed by Friedman and Nissenbaum [28], in which they identify three classes of bias: preexisting bias, technical bias, and emergent bias.”
BackgroundMethodThird Space, Information Sharing, and Participatory Design
Hansen, P., Fourie, I., & Meyer, A. (2021) · Synthesis lectures on information concepts, retrieval, and services · doi:10.2200/s01096ed1v01y202105icr074 · Cited by 5
Fish, B., & Stark, L. (2022) · ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) · doi:10.1145/3551624.3555296 · Cited by 7
“Moreover, the turn to computational definitions of values like fairness as mechanisms to solve problems of social injustice presents problems in itself [2, 37, 45, 62, 72].”
BackgroundIeracitano, F., Vigneri, F., & Comunello, F. (2023) · Information Communication & Society · doi:10.1080/1369118x.2023.2205928 · Cited by 8
“Moreover, search engine results – and related biases (Friedman & Nissenbaum, 1996) – are often analyzed from a global perspective (Allen, 2016), even when evidence is based on local results, mainly from the US.”
BackgroundStudy of the Need for Effective Cyber Security Trainings in India
Chawla, R. K., Sodhi, J. S., & Singh, T. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-1414-2_50 · Cited by 5
Detecting intersectionality in NER models: A data-driven approach
Lassen, I. M. S., Almasi, M., Enevoldsen, K., & Kristensen‐McLachlan, R. D. (2023) · Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL) · doi:10.18653/v1/2023.latechclfl-1.13 · Cited by 6
“According to one influential definition, bias in computer systems can be defined as systems which ‘sys-tematically and unfairly discriminate against certain individuals or groups of individuals in favour of others’ (Friedman and Nissenbaum, 1996).”
BackgroundArkhurst, B. K., Hawthorne, W., Ferrall, I. L., Fu, K. K., & Anderson, K. H. (2024) · Cell Reports Sustainability · doi:10.1016/j.crsus.2024.100018 · Cited by 5
“111 Traditional approaches to make research more equitable include attempts to diversify research teams, engage a broader range of stakeholders, promote inclusive policies and practices, and disseminate research findings more widely.”
BackgroundInfluentialAlgorithm Bias and Perceived Fairness: A Comprehensive Scoping Review
Saryazdi, A. H. (2024) · ACM SIGCPR Conference on Computer Personnel Research (SIGCPR) · doi:10.1145/3632634.3655848 · Cited by 7
“Algorithm bias, also referred to as algorithmic bias or algorithmic discrimination, denotes the systematic production of discriminatory outcomes by algorithms [1].”
BackgroundBrauner, P., Glawe, F., Liehner, G. L., Vervier, L., & Ziefle, M. (2025) · AI & Society · doi:10.1007/s00146-026-03023-8 · Cited by 90
“Hence, their output and the decisions they inform are not value-neutral and may lead to unintended or harmful consequences, such as perpetuating inequality or reinforcing existing biases (Friedman and Nissenbaum 1996; Nissenbaum 2001; Sadek et al. 2024).”
On the value of "useless data": Infrastructures, biodiversity, and policy.
Slota, S. C., & Bowker, G. C. (2015) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 5
Orwat, C., & Bless, R. (2016) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2935634.2935640 · Cited by 5
“In contrast, measures of censorship and pervasive surveillance violate fundamental human rights [31, 32].”
BackgroundIdentity, privacy and security challenges with Ontario's enhanced driver's licence
McPhail, B., Boa, K., Ferenbok, J., et al. (2009) · IEEE Toronto International Conference Science and Technology for Humanity · doi:10.1109/tic-sth.2009.5444399 · Cited by 5
“These systems have been criticized for what has been termed “algorithmic bias” [25].”
BackgroundSpier, S. (2017) · Elsevier eBooks · doi:10.1016/b978-0-08-100567-5.00002-5 · Cited by 5
Cottam, J. A., & Blaha, L. M. (2018) · Springer eBooks · doi:10.1007/978-3-319-95831-6_4 · Cited by 5
Automated influence and the challenge of cognitive security
Rajtmajer, S. M., & Susser, D. (2020) · Symposium on Hot Topics in the Science of Security (HotSoS) · doi:10.1145/3384217.3385615 · Cited by 6
“There are no doubt many more examples of overlapping issues—e.g., discussions about algorithmic bias [e.g., 2, 16] and trust in automated systems [e.g., 41]—which would mutually benefit from dialogue between AI ethics scholars and military ethics scholars.”
BackgroundRamachandran, S., Cutchin, S., & Fu, S. (2024) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--37634 · Cited by 5
“We use the term bias to refer to computer algorithms that systematically discriminate against certain content, individuals, or groups without a sound basis [1].”
MethodHartstein, J., & Blümel, C. D. (2021) · Frontiers in Research Metrics and Analytics · doi:10.3389/frma.2021.747562 · Cited by 6
“Also, with Friedman and Nissenbaum (1996), we argue, that the infrastructure itself is shaped by assumptions from its developers about how the world is like and should be.”
BackgroundMecati, M., Vetrò, A., & Torchiano, M. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671443 · Cited by 5
“…for a good or [assigning] an undesirable outcome to an individual or groups of individuals on grounds that are unreasonable or inappropriate [9]; • we identify as social groups of category object of possible discrimination those identified by the characteristics provided in “Article 21 -…”
BackgroundData modelling as a means of power: At the legal and computer science crossroads
von Lewinski, K., Beurskens, M., & Scherzinger, S. (2023) · Computer law & security review · doi:10.1016/j.clsr.2023.105865 · Cited by 5
Human Trust Factors in Image Analysis
Williams, A., Sherman, I. N., Smarr, S. A., Posadas, B. B., & Gilbert, J. E. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-94391-6_1 · Cited by 6
Designing the Gender-Neutral User Experience
Barth, D. R. (2012) · Digital WPI · Cited by 6
Disaggregated Interventions to Reduce Inequality
Bynum, L., Loftus, J. R., & Stoyanovich, J. (2021) · ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) · doi:10.1145/3465416.3483286 · Cited by 5
“a central and longstanding concern relevant to algorithmic fairness research: pre-existing bias [9, 35].”
BackgroundATEE Spring Conference 2020-2021
Ranieri, M., Menichetti, L., Cuomo, S., Parmigiani, D., & Pellegrini, M. (2021) · Proceedings e report · doi:10.36253/978-88-5518-412-0 · Cited by 5
Using Online Reviews as Narratives to Evoke Designer’s Empathy
Grünloh, C., Walldius, Å., Hartmann, G., & Gulliksen, J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22701-6_22 · Cited by 5
“…“unstable”, the “hypochondriac”), which “has its roots in social institutions, practices, and attitudes” and “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” [12, p. 333f].”
BackgroundBorning, A. H., Friedman, B., Davis, J., et al. (2009) · Information Polity · doi:10.3233/ip-2009-0172 · Cited by 5
“We also considered freedom from bias [14], that is, the absence of systematic and unfair discrimination, as both an end in itself as well as in support of legitimation.”
Self-citationA Process for Evaluating the Gender and Professionalism of Web Design Elements
Stonewall, J. H., & Dorneich, M. C. (2016) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931213601172 · Cited by 5
An Approximate Theory for Value Sensitivity
Barn, B. S., & Barn, R. (2015) · doi:10.1109/gtse.2015.10 · Cited by 5
“A notable and sustained contribution to understanding and accounting for values in the design process is the work by Friedman and her colleagues on Value Sensitive Design (VSD)[7], [8].”
BackgroundSystematic Design for Privacy in UbicompPreprint
Camp, L. J., Shankar, K., & Connelly, K. H. (2006) · SSRN Electronic Journal · doi:10.2139/ssrn.889444 · Cited by 5
“…constructed [Fischer, 1994] [Spar, 2002] and dynamic iterative [Castells, 1997] [Douglas, 97] models of technological development have clear parallels in, respectively, the technical, preexisting, and emergent models proposed for computing systems in design for values [Friedman & Nissenbaum, 1996].”
BackgroundRegulating Accuracy-Efficiency Trade-Offs in Distributed Machine Learning SystemsPreprint
Cooper, A. F., Levy, K. E. C., & De, C. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3650497 · Cited by 4
Artificial Intelligence, Machine Learning, and Gender Bias
Weidner, N. U. (2020) · The International Encyclopedia of Gender, Media, and Communication · doi:10.1002/9781119429128.iegmc316 · Cited by 5
Hyperparameter Optimization Is Deceiving Us, and How to Stop ItPreprint
Cooper, A. F., Lu, Y., Forde, J. Z., & De, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.03034 · Cited by 12
Diagnosing Gender Bias in Image Recognition SystemsPreprint
Schwemmer, C., Knight, C. R., Bello-Pardo, E. D., Oklobdzija, S. N., Schoonvelde, M., & Lockhart, J. W. (2018) · doi:10.31235/osf.io/as25q · Cited by 3
Measuring and Reducing Racial Bias in a Pediatric Urinary Tract Infection ModelPreprint
Anderson, J. W., Shaikh, N., & Visweswaran, S. (2023) · medRxiv · doi:10.1101/2023.09.18.23295660 · Cited by 5
“Research on bias, unfairness, and discrimination has a long history in several domains [10], which predates research on fairness in clinical models.”
More than the Sum of its Parts: Susceptibility to Algorithmic Disadvantage as a Conceptual Framework
Lopez, P. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658944 · Cited by 6
“Bias in technology design might emerge, for example, from invisibility or hypervisibility of certain groups in machine learning training data (see, e.g., [30, 35, 52, 72]), but should not be reduced to the technical realm [23, 53].”
BackgroundBeyond Semantics: Examining Gender Bias in LLMs Deployed within Low-resource Contexts in India
Aneja, U., Gupta, A., & Vashistha, A. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732180 · Cited by 4
“While some manifestations of gender bias, such as the gender wage gap, discriminatory laws, or under-representation of women in leadership positions, were seen as objective and widely recognised, participants noted that the other forms of bias are more nuanced and context-dependent[15].”
Pyle, C., & Andalibi, N. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757550 · Cited by 4
“Finally, participants perceived emergent biases [65] arising from algorithms used in practice in college admissions, like participants’ concerns over inaccurate and game-able proxies of demonstrated interest [77, 100] (e.g., email opens, website visits).”
ABROCA Distributions For Algorithmic Bias Assessment: Considerations Around Interpretation
Borchers, C., & Baker, R. S. (2025) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3706468.3706498 · Cited by 4
“This bias occurs when an algorithm produces results that disproportionately disadvantage or favor particular groups of people based on non-malleable characteristics like race, gender, or socioeconomic status [7].”
Heffernan, A., Ganguli, R., Sears, I., Stephen, A. H., & Heffernan, D. S. (2025) · Surgical Infections · doi:10.1089/sur.2024.288 · Cited by 5
“The increasing enthusiasm for ML in healthcare research has led to a better understanding of the limitation and fl aws of each MLMs. 26 There is an increasing understanding of the limitations and fl aws of the AUC ROC mathematically modeling in projects where the outcome is both binary and where one of the outcomes (yes…”
InfluentialFrom automation to agency: Prototype for self-owning intelligent buildings enabled by blockchain
Wang, H., Hunhevicz, J., & Hall, D. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106309 · Cited by 6
Reeve, J., Zaugg, I. A., & Tian, Z. (2022) · Journal of Information Communication and Ethics in Society · doi:10.1108/jices-12-2021-0124 · Cited by 5
Mecati, M., Torchiano, M., Vetrò, A., & Martin, J. C. D. (2023) · IEEE Access · doi:10.1109/access.2023.3252370 · Cited by 6
“, see the pioneering work proposed in [1]– got wider attention only in the mid 2010s, in connection with the large investments in Artificial Intelligence (AI) / Machine Learning (ML), digital automation of organizational processes and, in general, automated decision-making (ADM) systems.”
BackgroundFrankot, M. A., Young, M. E., & Haar, C. V. (2023) · Perspectives on Behavior Science · doi:10.1007/s40614-023-00388-9 · Cited by 4
“One drawback of ML is that algorithms can perpetuate bias when systematic tendencies in the model generate unequal outcomes across subgroups (Friedman & Nissenbaum, 1996).”
Liu, Y. (2023) · International Conference on Information Management and Management Science (IMMS) · doi:10.1145/3625469.3625483 · Cited by 4
“[3] Example: Through cloud platforms like AWS, chat-bots can process user interactions in real-time, essential for maintaining seamless communication.”
BackgroundEpistemic consequences of unfair tools
Lassen, I. M. S., Kristensen‐McLachlan, R. D., Almasi, M., Enevoldsen, K., & Nielbo, K. L. (2024) · Digital Scholarship in the Humanities · doi:10.1093/llc/fqad091 · Cited by 7
Trippas, J. R. (2024) · ACM eBooks · doi:10.1145/3674127.3674135 · Cited by 5
Bias in Federated Learning: Factors, Effects, Mitigations, and Open Issues
Benmalek, M., & Seddiki, A. (2024) · Ingénierie des systèmes d information · doi:10.18280/isi.290605 · Cited by 6
“Biases manifest due to historical prejudices, representation imbalance a systemic inequity ingrained in the data get propagated into the algorithms, leading to discriminatory predictions and decisions [15].”
Ethics and children's information systems
Silverstein, J., Nissenbaum, H. F., Flanagan, M., & Freier, N. G. (2006) · Proceedings of the American Society for Information Science and Technology · doi:10.1002/meet.14504301133 · Cited by 4
“Using as a starting point the analytic framework proposed by Nissenbaum et al. (Friedman & Nissenbaum, 1996; Nissenbaum, 1998) known as Values in Design, these two panelists will report on the third and last year of this project, and will focus on the design process and values investigation, with the goal of demonstra…”
MethodSelf-citationAn Exploration of Resilience and Values in the Co-Design of Sociotechnical Systems
Barn, B. S., & Barn, R. (2016) · International Journal of Systems and Society · doi:10.4018/ijss.2016010101 · Cited by 5
Mugar, G. (2017) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3134718 · Cited by 4
“Focusing on the design process, Value Sensitive Design (VSD) proposes techniques that help designers and engineers surface the values that drive the design process of new systems [15].”
BackgroundA HERMENEUTIC APPROACH TO SOLVING THE TRANSLATION PROBLEM IN DESIGNING ONTOLOGIES
Abi-Lahoud, E., Butler, T., & O’Brien, L. (2014) · Cited by 4
Huang, E. S. (2022) · Med · doi:10.1016/j.medj.2022.03.005 · Cited by 4
Detecting Risk of Biased Output with Balance Measures
Mecati, M., Vetrò, A., & Torchiano, M. (2022) · Journal of Data and Information Quality · doi:10.1145/3530787 · Cited by 4
Music Recommender Systems: A Review Centered on Biases
Medina, Y. O., Baldassarri, S., Sanz, C. V., & Beltrán, J. R. (2022) · Signals and communication technology · doi:10.1007/978-3-031-18444-4_4 · Cited by 4
Volume II: A Changing Third Sector Research Landscape—Progress or Pitfall?
Kim, M., & Raggo, P. (2023) · VOLUNTAS International Journal of Voluntary and Nonprofit Organizations · doi:10.1007/s11266-022-00555-7 · Cited by 4
The epistemic ethical concerns involving algorithms in intelligent communication
Lin, J., & Chen, C. (2023) · Teknokultura Revista de Cultura Digital y Movimientos Sociales · doi:10.5209/tekn.87552 · Cited by 5
Incorporating a Critical Reasoning Component into the ICT–Ethics Methodological Framework
Tavani, H. T. (2017) · ORBIT Journal · doi:10.29297/orbit.v1i2.55 · Cited by 4
“Third, some CS professionals have incorporated insights by Huff (2004) and Friedman and Nissenbaum (1996) that address bias and embedded values in software design, while Friedman, Kahn, and Borning (2006) have developed a full–blown framework called “Value Sensitive Design” (VSD) to respond to…”
BackgroundToward software engineering principles based on Islamic ethical values
Hameed, S. A. (2008) · International Conference on Computer and Communication Engineering · doi:10.1109/iccce.2008.4580632 · Cited by 4
“On the other hand Ibn Miskawah and Abo hamid AlGhazali define as a fixed situation of mankind soal and according to the mankind acts or behavie easely and simply without need for thinking or his acts become as default.”
BackgroundDemocracy, Technology, and Information Societies
Johnson, D. G. (2007) · Springer eBooks · doi:10.1007/978-0-387-72381-5_2 · Cited by 4
“Friedman and Nissenbaum (1996) provide cases illustrating bias in computer systems; Introna and Nissenbaum (2000) show how the design of search engines is laden with value choices; and Brey (1999) argues for 'disclosive computer ethics' on grounds that it is critical to 'disclose' and make visible…”
BackgroundApologos: A Lightweight Design Method for Sociotechnical Inquiry
Stark, L. (2021) · Journal of Social Computing · doi:10.23919/jsc.2021.0028 · Cited by 4
Pricing and competition in US Airline markets : changes in air travel demand since 2000
Geslin, C. (2006) · Cited by 4
Fairness-Aware Recommendation of Information CuratorsPreprint
Zhu, Z., Wang, J., Zhang⋆, Y., & Caverlee, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.03040 · Cited by 4
“Friedman [9] defined that a computer system is biased “if it sys-”
BackgroundUnderstanding and Explaining Automated DecisionsPreprint
Powell, A. B., Joshi, A., Carfantan, P.-M., Bourke, G., Hutchinson, I., & Eichholzer, A. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3309779 · Cited by 4
An Introduction to Algorithmic FairnessPreprint
Weerts, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.05595 · Cited by 11
Mühlhoff, R. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3666727 · Cited by 4
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
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