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 101–200 of 1,155
Fällman, D. (2011) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1979099 · Cited by 147
“They are by no means the only philosophers that have arrived at such a conclusion (see e.g. [57]) and it has been explored in HCI-related literature before, e.g. [24, 23, 21], but the notion is still to be to be accepted by mainstream HCI—in part because of the rather persistent ‘tool’ perspective.”
BackgroundModelling opinion dynamics in the age of algorithmic personalisation
Perra, N., & Rocha, L. E. C. (2019) · Scientific Reports · doi:10.1038/s41598-019-43830-2 · Cited by 139
Big data governance of personal health information and challenges to contextual integrity
Winter, J. S., & Davidson, E. (2018) · The Information Society · doi:10.1080/01972243.2018.1542648 · Cited by 142
“As many scholars have argued (e.g., Friedman and Nissenbaum 1996), technical systems are not value-free – they carry the biases of the social and cultural context in which they arise.”
BackgroundCooper, A. F., Moss, E., Laufer, B., & Nissenbaum, H. F. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533150 · Cited by 188
Self-citationTechnology, Megatrends and Work: Thoughts on the Future of Business Ethics
D’Cruz, P., Du, S., Noronha, E., Parboteeah, K. P., Trittin‐Ulbrich, H., & Whelan, G. (2022) · Journal of Business Ethics · doi:10.1007/s10551-022-05240-9 · Cited by 133
“Business ethics scholars should follow others who acknowledge that digital technologies have biases that are value-laden (Friedman & Nissenbaum, 1996; Johnson, 2004) or have political dimensions (Winner, 1980), while also identifying how individuals, corporations and society can control that same…”
BackgroundKroll, J. A. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2018.0084 · Cited by 132
“Work in both Privacy by Design [7,8] as well as ethics for computer systems [9–11] aims to contextualize technology in human value systems, but recent debates on the governance of artificial intelligence ignore the extent to which inscrutability is always a structural concern and a technological…”
BackgroundOtterbacher, J., Bates, J., & Clough, P. D. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025727 · Cited by 149
“Writing well before the rise of “Big Data” and online service giants, Friedman and Nissenbaum [18] explained that systems are biased when two conditions hold: 1) results are slanted in unfair discrimination against particular persons or groups, and 2) that discrimination is systematic, i.e., not…”
BackgroundWall, E., Blaha, L. M., Franklin, L. R., & Endert, A. (2017) · IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast.2017.8585669 · Cited by 129
“[27, 64], here we focus on cognitive bias injected by analysts.”
BackgroundTrust and trustworthiness in AI ethics
Reinhardt, K. (2022) · AI and Ethics · doi:10.1007/s43681-022-00200-5 · Cited by 157
On sociomaterial imbrications: What plagiarism detection systems reveal and why it matters
Introna, L. D., & Hayes, N. (2011) · Information and Organization · doi:10.1016/j.infoandorg.2011.03.001 · Cited by 133
Algorithmic Bias in EducationPreprint
Baker, R. S., & Hawn, A. (2021) · doi:10.35542/osf.io/pbmvz · Cited by 117
Advancing health equity with artificial intelligence
Thomasian, N. M., Eickhoff, C., & Adashi, E. Y. (2021) · Journal of Public Health Policy · doi:10.1057/s41271-021-00319-5 · Cited by 117
“Interpretability is only part of the story when it comes to the discussion of human factors during algorithm implementation [31, 32] Developers should attempt to understand why population and public health practitioners choose to use or to ignore model recommendations and develop controls for these…”
BackgroundPredictive privacy: towards an applied ethics of data analytics
Mühlhoff, R. (2021) · Ethics and Information Technology · doi:10.1007/s10676-021-09606-x · Cited by 134
How Different Groups Prioritize Ethical Values for Responsible AI
Jakesch, M., Buçinca, Z., Amershi, S., & Olteanu, A. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533097 · Cited by 116
“In contrast, proponents of value-sensitive design [23, 24], reflective design [64], and critical technical practice [1] advocate that AI systems should be designed with broader human and societal values in mind.”
BackgroundEncouraging women in computer science
Roberts, E. S., Kassianidou, M., & Irani, L. C. (2002) · ACM SIGCSE Bulletin · doi:10.1145/543812.543837 · Cited by 112
“• The extraordinary flexibility of software makes it easier to incorporate social biases in the design of computing systems [ 9 ].”
MethodThis Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology
Mulligan, D. K., Kroll, J. A., Kohli, N., & Wong, R. Y. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359221 · Cited by 111
“terrogatingthevaluesoftechnicalandsociotechnicalsystems havedeeprootsfrommultipledisciplines,includingscience&technologystudies, law,philosophy, HCI, and computer science & engineering (e.g., [22, 27, 50, 89, 90, 96, 126, 151]). In addition to the critical analysis of understanding values in technical artifact…”
BackgroundValue Sensitive Design: Applications, Adaptations, and Critiques
Davis, J., & Nathan, L. P. (2015) · Springer eBooks · doi:10.1007/978-94-007-6970-0_3 · Cited by 127
Toward creating a fairer ranking in search engine results
Gao, R., & Shah, C. S. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.102138 · Cited by 114
Exploring author gender in book rating and recommendation
Ekstrand, M. D., Tian, M., Kazi, M. I. R., Mehrpouyan, H., & Kluver, D. (2018) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3240323.3240373 · Cited by 110
Mann, M., & Matzner, T. (2019) · Big Data & Society · doi:10.1177/2053951719895805 · Cited by 88
Defining the Web: the politics of search engines
Introna, L. D., & Nissenbaum, H. F. (2000) · Computer · doi:10.1109/2.816269 · Cited by 109
Self-citationCobbe, J. (2019) · Legal Studies · doi:10.1017/lst.2019.9 · Cited by 174
Nicolescu, R., Huth, M., Radanliev, P., & Roure, D. D. (2018) · Journal of Information Technology · doi:10.1057/s41265-018-0054-1 · Cited by 111
“(2017). The Real Lesson of the Alleged Russian Hack.”
Propagation of societal gender inequality by internet search algorithms
Vlasceanu, M., & Amodio, D. M. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2204529119 · Cited by 109
Policy advice and best practices on bias and fairness in AI
Álvarez, J. M., Colmenarejo, A. B., Elobaid, A., Fabbrizzi, S., Fahimi, M., Ferrara, A., Ghodsi, S., Mougan, C., Papageorgiou, I. T., Lobo, P. R., et al. (2024) · Ethics and Information Technology · doi:10.1007/s10676-024-09746-w · Cited by 147
“…was triggered by the US Civil Rights Act of 1964 (Hutchinson & Mitchell, 2019), while bias in procedural (i.e., hand-written by humans) algorithms has been considered since the mid 1990’s (Friedman & Nis-senbaum, 1996)—with the first known case tracing back to 1986 (Lowry & Macpherson, 1986).”
Diversity and language technology: how language modeling bias causes epistemic injustice
Helm, P., Bella, G., Koch, G., & Giunchiglia, F. (2024) · Ethics and Information Technology · doi:10.1007/s10676-023-09742-6 · Cited by 110
“In terms of social groups, studies have focused on gender, ethnicity, and race, but also other forms of bias (religion-related, age-related, political, etc.) (Friedman & Nissenbaum, 1996).”
Business and the Ethical Implications of Technology: Introduction to the Symposium
Martin, K. E., Shilton, K., & Smith, J. A. (2019) · Journal of Business Ethics · doi:10.1007/s10551-019-04213-9 · Cited by 115
Philosophy of Computing and Information Technology
Brey, P. A. E., & Søraker, J. H. (2009) · Elsevier eBooks · doi:10.1016/b978-0-444-51667-1.50051-3 · Cited by 122
Srinivasan, R., & Chander, A. (2021) · Communications of the ACM · doi:10.1145/3464903 · Cited by 132
“In their 1996 paper “Bias in Computer Systems,” Friedman and Nissenbaum 10 provide an example: Software that relies on”
BackgroundStark, L., & Crawford, K. (2019) · Surveillance & Society · doi:10.24908/ss.v17i3/4.10821 · Cited by 102
Brown, N., Xie, B., Sarder, E., Fiesler, C. L., & Wiese, E. S. (2023) · ACM Transactions on Computing Education · doi:10.1145/3634685 · Cited by 126
“By synthesizing the various approaches, we can contribute to the ongoing dialogue and guide future developments for computing ethics education.”
BackgroundAI Ethics Principles in Practice: Perspectives of Designers and Developers
Sanderson, C., Douglas, D., Lu, Q., Schleiger, E., Whittle, J., Lacey, J., Newnham, G. J., Hajkowicz, S. A., Robinson, C., & Hansen, D. T. (2023) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2023.3257303 · Cited by 112
“These biases may be pre-existing individual and societal biases of those developing the system, technical biases resulting from how the system is implemented, or emergent biases resulting from the context within which the system is used [27].”
BackgroundBorning, A. H., Friedman, B., Davis, J., & Lin, P. (2006) · Springer eBooks · doi:10.1007/1-4020-4023-7_23 · Cited by 100
Self-citationDeVos, A., Dhabalia, A., Shen, H., Holstein, K., & Eslami, M. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517441 · Cited by 98
Through the Google Goggles: Sociopolitical Bias in Search Engine Design
Diaz, A. (2008) · Information science and knowledge management · doi:10.1007/978-3-540-75829-7_2 · Cited by 96
Value of Information Systems and Products: Understanding the Users’ Perspective and Values
Kujala, S., & Väänänen, K. (2009) · Journal of the Association for Information Systems · Cited by 98
Diagnosing Gender Bias in Image Recognition Systems
Schwemmer, C., Knight, C. R., Bello-Pardo, E. D., Oklobdzija, S. N., Schoonvelde, M., & Lockhart, J. W. (2020) · Socius Sociological Research for a Dynamic World · doi:10.1177/2378023120967171 · Cited by 101
“At the same time, recent research has shown algorithmic classification systems to be mechanisms for the reproduction, and even amplification, of more general social biases (Friedman and Nissenbaum 1996; Noble 2018).”
BackgroundArtificial Intelligence and Machine Learning Applied at the Point of Care
Angehrn, Z., Haldna, L., Zandvliet, A. S., Gil Berglund, E., Zeeuw, J., Amzal, B., Cheung, S. Y. A., Polasek, T. M., Pfister, M., Kerbusch, T., et al. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.00759 · Cited by 93
Conservative AI and social inequality: conceptualizing alternatives to bias through social theory
Zajko, M. (2021) · AI & SOCIETY · doi:10.1007/s00146-021-01153-9 · Cited by 83
HCI and Societal Issues: A Framework for Engagement
Hochheiser, H., & Lazar, J. K. (2007) · International Journal of Human-Computer Interaction · doi:10.1080/10447310701702717 · Cited by 99
Bringing Design to the Privacy Table
Wong, R. Y., & Mulligan, D. K. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300492 · Cited by 100
Fairness in Ranking, Part I: Score-Based Ranking
Zehlike, M., Yang, K., & Stoyanovich, J. (2022) · ACM Computing Surveys · doi:10.1145/3533379 · Cited by 116
“We study ranking systems with respect to the types of bias that they attempt to mitigate, namely, pre-existing bias, technical bias, and emergent bias, as defined by [35].”
BackgroundCognitive imperialism in artificial intelligence: counteracting bias with indigenous epistemologies
Ofosu-Asare, Y. (2025) · AI & SOCIETY · doi:10.1007/s00146-024-02065-0 · Cited by 97
“This process is vital for developing AI that is fair and equitable (Friedman and Nissenbaum 1996).”
Ethical Dimensions of Music Information Retrieval Technology
Holzapfel, A., Sturm, B. L. T., & Coeckelbergh, M. (2018) · Transactions of the International Society for Music Information Retrieval · doi:10.5334/tismir.13 · Cited by 98
“Such a change of users and stakeholders who interact with a software is one of the main sources of an emergent bias (Friedman and Nissenbaum, 1996).”
BackgroundInfluentialPapakyriakopoulos, O., Hegelich, S., Serrano, J. C. M., & Marco, F. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3372843 · Cited by 95
“To do so, we adopt the Friedman et al. [38] proposed framework for analyzing algorithmic bias.”
MethodBiased Humans, (Un)Biased Algorithms?
Pethig, F., & Kroenung, J. (2022) · Journal of Business Ethics · doi:10.1007/s10551-022-05071-8 · Cited by 118
“…all that we know about editing photos (Carlson, 2019) or the very human (and therefore biased) choices that underlie the development of algorithms (Friedman & Nissenbaum, 1996), research so far has shown a remarkable tendency—shared especially by lay people (e.g., Logg et al., 2019)—to trust the…”
BackgroundFairness in Ranking, Part II: Learning-to-Rank and Recommender Systems
Zehlike, M., Yang, K., & Stoyanovich, J. (2022) · ACM Computing Surveys · doi:10.1145/3533380 · Cited by 105
The Ethics of AI Ethics. A Constructive Critique
Heilinger, J. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00557-9 · Cited by 116
“• How can AI systems be prevented from reflecting existing discrimination, biases and social injustices based on their training data, thereby exacerbating them (cf. e.g. Bender et al., 2021; Benjamin, 2019; Friedman & Nissenbaum, 1996)?”
BackgroundA Human-Centered Approach to Algorithmic Services
Lee, M. K., Kim, J. T., & Lizarondo, L. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025884 · Cited by 94
“For example, Batya and Nissenbaum [14] explain how the order of items in a list can promote the selection of top items.”
BackgroundArtificial Intelligence (AI) in Islamic Ethics: Towards Pluralist Ethical Benchmarking for AI
Elmahjub, E. (2023) · Philosophy & Technology · doi:10.1007/s13347-023-00668-x · Cited by 200
A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
Suresh, H., & Guttag, J. (2021) · Equity and Access in Algorithms, Mechanisms, and Optimization · doi:10.1145/3465416.3483305 · Cited by 82
“Similarly, Friedman and Nissenbaum [21]’s “preexisting bias” might map to our “historical bias” (Section 3.1) when it describes existing societal stereotypes that are reflected in datasets.”
BackgroundInfluentialORES: Lowering Barriers with Participatory Machine Learning in Wikipedia
Halfaker, A., & Geiger, R. S. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415219 · Cited by 82
“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].”
“It lives all around us”: Aspects of data literacy in teen's lives
Bowler, L., Acker, A., Jeng, W., & Chi, Y. (2017) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.2017.14505401004 · Cited by 85
“…through personalization, young people are encountering a world in which their personal digital data may serve to shape their opportunities and very identities (Friedman & Nissenbaum, 1996; Introna & Nissenbaum, 2000; Pariser, 2012; Sweeney, 2013; Zang, Dummit, Graves, Lisker & Sweeney, 2015).”
BackgroundShen, Q., Tao, W., Zhang, J., Wen, H., Chen, Z., & Lu, Q. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3481948 · Cited by 85
“Unfairness bias can be explained as the system systematically and unfairly discriminates against certain individuals or groups of individuals in favor others [3, 8, 12, 15, 28].”
BackgroundDesigning AI for Social Good: Seven Essential Factors
Cowls, J., King, T., Taddeo, M., & Floridi, L. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3388669 · Cited by 83
“These decision 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).”
BackgroundThe right to refuse diagnostics and treatment planning by artificial intelligence
Ploug, T., & Holm, S. (2019) · Medicine Health Care and Philosophy · doi:10.1007/s11019-019-09912-8 · Cited by 85
Highly Accurate, But Still Discriminatory
Köchling, A., Riazy, S., Wehner, M., & Simbeck, K. (2020) · Business & Information Systems Engineering · doi:10.1007/s12599-020-00673-w · Cited by 107
Egbert, S., & Leese, M. (2020) · Routledge eBooks · doi:10.4324/9780429328732 · Cited by 124
Recommender systems under European AI regulations
Noia, T. D., Tintarev, N., Fatourou, P., & Schedl, M. (2022) · Communications of the ACM · doi:10.1145/3512728 · Cited by 80
(2024) · ACM eBooks · doi:10.1145/3674127 · Cited by 86
Stoyanovich, J., Howe, B., & Jagadish, H. V. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3415478.3415570 · Cited by 89
“In their seminal 1996 paper, Friedman and Nissenbaum identified three types of bias that can arise in computer systems: pre-existing, technical, and emergent bias [18].”
BackgroundStinson, C. (2022) · AI and Ethics · doi:10.1007/s43681-022-00136-w · Cited by 101
“The colloquial meaning of bias is closer to the definition Friedman and Nissenbaum offer of “bias of moral import”, which is, “systematically and unfairly discriminat[ing] against certain individuals or groups of individuals in favor of others” [18].”
BackgroundDesign methods for ethical persuasive computing
Davis, J. (2009) · International Conference on Persuasive Technology · doi:10.1145/1541948.1541957 · Cited by 77
“For example, Friedman and Nissenbaum analyze the problem of bias, or the systematic and unfair assignment of undesirable outcomes to a person or group, in a range of information systems concerned with tasks from making airline reservations to determining whether persons are eligible for citizenship [18].”
BackgroundValue Sensitive Design: Applications, Adaptations, and Critiques
Davis, J., & Nathan, L. P. (2013) · Springer eBooks · doi:10.1007/978-94-007-6994-6_3-1 · Cited by 79
Predicting future AI failures from historic examples
Yampolskiy, R. V. (2018) · foresight · doi:10.1108/fs-04-2018-0034 · Cited by 75
The dangers of faulty, biased, or malicious algorithms requires independent oversight
Shneiderman, B. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1618211113 · Cited by 73
“Later, Friedman and Nissenbaum proposed a taxonomy of bias in computer systems that included preexisting biases based on social practices and attitudes, technical bias based on design constraints in hardware and software, and emergent bias that arises from changing the use context (10).”
BackgroundInfluentialMowshowitz, A., & Kawaguchi, A. (2002) · Communications of the ACM · doi:10.1145/567498.567527 · Cited by 74
Introduction to the Special Forum on Critical Making as Research Program
Ratto, M., Wylie, S., & Jalbert, K. (2014) · The Information Society · doi:10.1080/01972243.2014.875767 · Cited by 73
“Born of the same founding works (Friedman 1996; Friedman and Nissenbaum 1996), VID/VSD projects share a focus on encouraging and supporting designers and engineers to embrace more value-sensitive attitudes within their work processes.”
BackgroundDeng, W. H., Guo, B., Devrio, A., Shen, H., Eslami, M., & Holstein, K. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581026 · Cited by 103
It's My Data! Tensions Among Stakeholders of a Learning Analytics Dashboard
Sun, K., Mhaidli, A. H., Watel, S., Brooks, C. A., & Schaub, F. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300824 · Cited by 77
“Algorithms may suffer from different inherent biases, such as pre-existing bias from social institution and attitudes, technical bias due to technological constraints, and emergent bias resulting from a system’s context of use [24].”
BackgroundMolecular Machine Learning: The Future of Synthetic Chemistry?
Pflüger, P. M., & Glorius, F. (2020) · Angewandte Chemie International Edition · doi:10.1002/anie.202008366 · Cited by 76
Barassi, V. (2020) · The MIT Press eBooks · doi:10.7551/mitpress/12415.001.0001 · Cited by 104
Software agents and user autonomy
Friedman, B., & Nissenbaum, H. F. (1997) · doi:10.1145/267658.267772 · Cited by 78
“criteria should be expanded to include those of social and ethical significance [1, 2 ]. Our focus on user autonomy extends this line of investigation.”
BackgroundSelf-citationWorthy, P., Matthews, B., & Viller, S. (2016) · ACM Conference on Designing Interactive Systems (DIS) · doi:10.1145/2901790.2901890 · Cited by 71
“The values implicated by a system emerge from: the technology itself; the people involved the design and use of the technology; and, the social context in which that technology is used [10,27].”
BackgroundHow to see values in social computing
Shilton, K., Koepfler, J. A., & Fleischmann, K. R. (2014) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2531602.2531625 · Cited by 67
“embedded in a technological system [21].”
Ethical decision‐making for knowledge representation and organization systems for global use
Beghtol, C. (2005) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20184 · Cited by 68
Urbansim: Using Simulation to Inform Public Deliberation and Decision-Making
Borning, A. H., Waddell, P. A., & Förster, R. (2007) · Integrated series on information systems/Integrated series in information systems · doi:10.1007/978-0-387-71611-4_22 · Cited by 68
“Next, we committed to several key moral values to support explicitly: fairness and more specifically freedom from bias [15], representativeness, accountability, and support for a democratic society.”
BackgroundCobbe, J. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3226913 · Cited by 165
Exploring author gender in book rating and recommendation
Ekstrand, M. D., & Kluver, D. (2021) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-020-09284-2 · Cited by 66
Envisioning Information Access Systems: What Makes for Good Tools and a Healthy Web?
Shah, C. S., & Bender, E. M. (2024) · ACM Transactions on the Web · doi:10.1145/3649468 · Cited by 86
“Friedman and Nissenbaum [38] define bias as systematic and unfair discrimination.”
When Users Control the Algorithms
Burrell, J., Kahn, Z., Jonas, A., & Griffin, D. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359240 · Cited by 82
“This is a continuation of work set in motion over two decades ago [19, 42].”
Backgroundde Reuver, M., van Wynsberghe, A., Janssen, M., & van de Poel, I. (2020) · Ethics and Information Technology · doi:10.1007/s10676-020-09537-z · Cited by 66
Deep Automation Bias: How to Tackle a Wicked Problem of AI?
Strauß, S. (2021) · Big Data and Cognitive Computing · doi:10.3390/bdcc5020018 · Cited by 83
“In their work, Friedman and Nissenbaum (1996) argued for a better understanding of bias in computer systems, which can lead to discrimination of individuals or groups.”
BackgroundBias in Automatic Speech Recognition: The Case of African American Language
Martin, J., & Wright, K. E. (2022) · Applied Linguistics · doi:10.1093/applin/amac066 · Cited by 71
Social Justice in HCI: A Systematic Literature Review
Chordia, I., Baltaxe-Admony, L. B., Boone, A., Sheehan, A., Dombrowski, L. S., Dantec, C. A. L., Ringland, K. E., & Smith, A. D. R. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642704 · Cited by 80
“” Friedman and colleagues concluded that “biased computer systems are instruments of injus-tice ” and that “freedom from bias ” should be amongst the select few values considered when evaluating computer systems [74].”
BackgroundCriminal courts’ artificial intelligence: the way it reinforces bias and discrimination
Malek, A. (2022) · AI and Ethics · doi:10.1007/s43681-022-00137-9 · Cited by 81
“Technical bias also results from technological constrains or errors that outweigh particular groups without an underlying value [38].”
BackgroundHard choices in artificial intelligence
Dobbe, R., Krendl Gilbert, T., & Mintz, Y. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103555 · Cited by 65
“Rather, the development of the system itself creates novel situations that intervene on social life, reflected in the distinction between pre-existing, technical, and emergent bias [Friedman and Nissenbaum, 1996].”
BackgroundDiversity, Equity, and Inclusion in Clinical Research: A Path Toward Precision Health for Everyone
Washington, V., Franklin, J. B., Huang, E. S., Mega, J. L., & Abernethy, A. P. (2022) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2804 · Cited by 67
Situated algorithms: a sociotechnical systemic approach to bias
Draude, C., Klumbytė, G., Lücking, P., & Treusch, P. (2019) · Online Information Review · doi:10.1108/oir-10-2018-0332 · Cited by 77
Opening the government’s black boxes: freedom of information and algorithmic accountability
Fink, K. (2018) · Information, Communication & Society · doi:10.1080/1369118x.2017.1330418 · Cited by 61
“Designers of algorithms may not even recognize biases, leaving them ‘ hidden in the code, difficult to pinpoint or explicate ’ (Friedman & Nissenbaum, 1996, p. 331).”
BackgroundPartisan search behavior and Google results in the 2018 U.S. midterm elections
Trielli, D., & Diakopoulos, N. A. (2020) · Information Communication & Society · doi:10.1080/1369118x.2020.1764605 · Cited by 61
“User input bias in particular emerges from the interaction of the user with the system (Friedman & Nissenbaum, 1996) and humans affect the design and functioning of algorithms (Bozdag, 2013).”
BackgroundAttitudes Surrounding an Imperfect AI Autograder
Hsu, S., Li, T. W., Zhang, Z., Fowler, M., Zilles, C. B., & Karahalios, K. G. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445424 · Cited by 76
Towards a Post-human Intra-actional Account of Sociomaterial Agency (and Morality)
Introna, L. D. (2013) · Philosophy of engineering and technology · doi:10.1007/978-94-007-7914-3_3 · Cited by 61
Bowler, L., Knobel, C. P., & Mattern, E. (2014) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23270 · Cited by 69
“Further work by Friedman and Nissenbaum (1996) on bias in computer systems grounds the discussion that computer technologies specifically, through the process of design, incorporate social values by enabling or limiting the activities and actions that may be accessed and achieved.”
BackgroundFair Top-k Ranking with multiple protected groups
Zehlike, M., Sühr, T., Baeza-Yates, R. A., Bonchi, F., Castillo, C., & Hajian, S. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102707 · Cited by 63
Beyond Neutrality: Conceptualizing Platform Values
Hallinan, B., Scharlach, R., & Shifman, L. (2021) · Communication Theory · doi:10.1093/ct/qtab008 · Cited by 68
Auditing News Curation Systems: A Case Study Examining Algorithmic and Editorial Logic in Apple News
Bandy, J., & Diakopoulos, N. A. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7277 · Cited by 66
“Contrary to the notion of “neutral” or “purely technical,” systems inevitably embed, embody, and propagate human values and biases (Weizenbaum, 1976; Friedman and Nissenbaum, 1996; Introna and Nissenbaum, 2000; O’Neil, 2017), such as, in this case, editorial values about what and when to publish.”
Background“Part Man, Part Machine, All Cop”: Automation in Policing
Adensamer, A., & Klausner, L. D. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.655486 · Cited by 64
“This preexisting bias, to follow the terminology of Friedman and Nissenbaum, is then supplemented and amplified by additional technical and emergent biases (Friedman and Nissenbaum, 1996).”
BackgroundMaking Sense of Conflicting Science Information
Novin, A., & Meyers, E. (2017) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3020165.3020185 · Cited by 58
“There is a common misconception that search engines are objective, but humans may code their biases into the algorithms [30], [31],[32].”
BackgroundAlgorithmic bias and the Value Sensitive Design approach
Simon, J., Wong, P.-H., & Rieder, G. (2020) · Internet Policy Review · doi:10.14763/2020.4.1534 · Cited by 43
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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