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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
33 Own interpretation · 6 No full text of the sources · 4 Sources not found · 3 Supported · 2 Partly supported · 2 Own method · 2 No basis given · 1 Supported so far · 1 Not checked yet · 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)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None 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
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
- 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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)
plaindirectn = 2- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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 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)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
hedgeddirect- Asserted: fails — The authors may not put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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”
From Positioning Paradata: A Conceptual Frame for AI Processual Documentation in Archives and Recordkeeping Contexts (2023) · cites this paper as [25]
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).”
From How to Design AI for Social Good: Seven Essential Factors (2020) · cites this paper as Friedman and Nissenbaum 1996
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.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Bias in Computer Systems > 3.1 The National Resident Match Program (NRMP)
“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)”
§ Bias in Computer Systems > References
“18. WILLIAMS, K. J., WERTH, V. P., AND WOLFF, J. A. 1981. An analysis of the resident match.”
Citations
Cites
The evolution of the labor market for medical interns and residents: A case study in game theory
Roth, A. E. (1984) · Journal of Political Economy · doi:10.1086/261272 · Cited by 1,219
MOOR, J. H. (1985) · Metaphilosophy · doi:10.1111/j.1467-9973.1985.tb00173.x · Cited by 863
The British Nationality Act as a logic program
Sergot, M. J., Sadri, F., Kowalski, R. A., Kriwaczek, F., Hammond, P., & Cory, H. T. (1986) · Communications of the ACM · doi:10.1145/5689.5920 · Cited by 564
An experimental time-sharing system
Corbató, F. J., Merwin-Daggett, M., & Daley, R. C. (1962) · Proceedings of the May 1-3, 1962, spring joint computer conference on - AIEE-IRE '62 (Spring) · doi:10.1145/1460833.1460871 · Cited by 234
New physicians: A natural experiment in market organization
Roth, A. E. (1990) · Science · doi:10.1126/science.2274783 · Cited by 141
Sex Bias in Educational Software: The Effect of Designers' Stereotypes on the Software They Design1
Huff, C., & Cooper, J. (1987) · Journal of Applied Social Psychology · doi:10.1111/j.1559-1816.1987.tb00328.x · Cited by 85
Fundamental Errors in Legal Logic Programming
Leith, P. (1986) · The Computer Journal · doi:10.1093/comjnl/29.6.545 · Cited by 51
Moor, J. H. (2017) · Routledge eBooks · doi:10.4324/9781315259697-7 · Cited by 40
Moor, J. H. (2020) · Routledge eBooks · doi:10.4324/9781003075011-1 · Cited by 21
National resident matching program
(1981) · New England Journal of Medicine · doi:10.1056/nejm198108273050923 · Cited by 10
Research works printed but not linked: 4
- FISHLOCK , T. 1981. Delhi press detect racism in Nationality Bill. The London Times ( Jan. 20 ) . FISHLOCK, T. 1981. Delhi press detect racism in Nationality Bill. The London Times (Jan. 20).
- GAO . 1992. Patriot Missile defense: Software problem led to system failure at Dhahran , Saudi Arabia. GAO/IMTEC-92-26, U.S. General Accounting Office, Washington, D.C. GAO. 1992. Patriot Missile defense: Software problem led to system failure at Dhahran, Saudi Arabia. GAO/IMTEC-92-26, U.S. General Accounting Office, Washington, D.C.
- JOHNSON , D.G. AND MULVEY , J. M. 1993. Computer decisions: Ethical issues of responsibil-ity and bias. Statistics and Operations Res. Series SOR-93-11 , Dept. of Civil Engineering and Operations Research , Princeton Univ ., Princeton, N.J. JOHNSON,D.G.AND MULVEY, J. M. 1993. Computer decisions: Ethical issues of responsibil-ity and bias. Statistics and Operations Res. Series SOR-93-11, Dept. of Civil Engineering and Operations Research, Princeton Univ., Princeton, N.J.
- OTT , J. 1988. American Airlines settles CRS dispute with British Airways. Aviat. Week Space Tech. ( July 18 ) . OTT, J. 1988. American Airlines settles CRS dispute with British Airways. Aviat. Week Space Tech. (July 18).
Cited by
Sorted by citations
Items 501–600 of 1,155
Confronting the tensions where UX meets AI
Cramer, H. S. M., & Kim, J. (2019) · interactions · doi:10.1145/3364625 · Cited by 11
“Anticipating harms, including those that in hindsight are obvious, requires techniques that promote A I research has now been around for about 65 years, and the consequences of design decisions on AI outcomes have been a lively debate for 20-plus years [1], if not longer.”
BackgroundMudliar, P. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376564 · Cited by 12
A GA-based algorithm meets the fair ranking problem
Tahery, S., Aftabi, S. Z., & Farzi, S. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102711 · Cited by 10
Reflections on Cultural Bias and Adaptation
Pargman, D. S. (1999) · Javnost - The Public · doi:10.1080/13183222.1999.11008725 · Cited by 12
“The Internet is an example of a computer system that suffers from technical and emergent social bias.”
BackgroundInfluentialWong, R. Y., & Mulligan, D. K. (2016) · Journal of Human-Robot Interaction · doi:10.5898/jhri.5.3.wong · Cited by 11
“…that the creation of artifacts or the making of technical decisions have social effects—both intended and unintended (e.g., Cranor & Reagle, 1997; Friedman & Nissenbaum, 1996; Latour, 1992; Winner, 1980)—the privacy community at large has sought to encourage technical designs that express and…”
BackgroundSustaining the public good vision of the internet: the politics of searchPreprint
Introna, L. D., & Nissenbaum, H. F. (2000) · RePEc: Research Papers in Economics · Cited by 10
Self-citationA discussion frame for explaining records that are based on algorithmic output
Andresen, H. (2019) · Records Management Journal · doi:10.1108/rmj-04-2019-0019 · Cited by 10
Development and validation of Generative AI Competence Scale (GenAIComp) among university students
Lee, S. C., Baby, T., Vongvit, R., Lee, J., Kim, Y., Cha, M. C., & Yoon, S. H. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.103059 · Cited by 27
Folk Theories and User Strategies on Dating Apps
Nader, K., & Lee, M. K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-96957-8_37 · Cited by 10
“Additionally, technical biases and societal biases within computer systems can create further marginalize certain communities [26].”
BackgroundBias and fairness in machine learning and artificial intelligence
Cirillo, D., & Rementeria, M. J. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-821392-6.00006-6 · Cited by 14
FairBoost: Boosting supervised learning for learning on multiple sensitive features
Colakovic, I., & Karakatič, S. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110999 · Cited by 10
THE DARK SIDE OF ARTIFICIAL INTELLIGENCE ON THE BASIS OF PUBLIC ADMINISTRATION
ULAŞAN, F. (2023) · Toplum Ekonomi ve Yönetim Dergisi · doi:10.58702/teyd.1345570 · Cited by 11
Balanced Quality Score: Measuring Popularity Debiasing in Recommendation
Coppolillo, E., Minici, M., Ritacco, E., Caroprese, L., Pisani, F. S., & Manco, G. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3650043 · Cited by 10
“” [16].”
A workshop on artificial intelligence biases and its effect on high school students’ perceptions
Gómez, M. J., Dabbah, J., & Benotti, L. (2024) · International Journal of Child-Computer Interaction · doi:10.1016/j.ijcci.2024.100710 · Cited by 10
Metaethical perspectives on ‘benchmarking’ AI ethics
LaCroix, T., & Luccioni, A. S. (2025) · AI and Ethics · doi:10.1007/s43681-025-00703-x · Cited by 29
“In this context, biases can be understood as behaviours that involve systematic discrimination against specific individuals or groups (typically in favour of other individuals or groups) (Friedman and Nissenbaum, 1996).”
A decade of gender bias in machine translation
Savoldi, B., Bastings, J., Bentivogli, L., & Vanmassenhove, E. (2025) · Patterns · doi:10.1016/j.patter.2025.101257 · Cited by 17
“Bias is a sociotechnical problem, rooted in pre-existing normative discrimination and asymmetries, 17,141 and is contextually and evolutionarily dynamic.”
Rodrigues, T. V. (2025) · The International Journal of Social Sciences and Humanities Invention · doi:10.18535/ijsshi/v12i05.04 · Cited by 10
“As a result, such outputs may be inadvertently incorporated into the final product, thereby compromising the epistemic integrity of the work (Friedman & Nissenbaum, 1996; Lipton, 2020).”
Navigating the Ethical Landscape of AI Innovation: Challenges and Opportunities
Mao, Q., & Xu, M. (2025) · International Journal of Digital Law and Governance · doi:10.1515/ijdlg-2025-0005 · Cited by 10
“…(ii) the bias management framework developed by the United States National Institute of Standards and Technology (NIST) (Schwartz et al. 2022); and (iii) the ethical perspective proposed by Friedman and Nissenbaum (1996) based on the Value-Sensitive Design (VSD) (Friedman and Nissenbaum 1996).”
Software Fairness: An Analysis and Survey
Soremekun, E., Papadakis, M., Cordy, M., & Traon, Y. L. (2025) · ACM Computing Surveys · doi:10.1145/3762170 · Cited by 41
“Bias: In this paper, we refer to bias in terms of algorithmic bias, specifically, bias occurs when a software system systematically and unfairly discriminate against certain individuals or groups of individuals in favour of others [53].”
Modeling for fairness: A Rawlsian approach
Diekmann, S., & Zwart, S. D. (2013) · Studies in History and Philosophy of Science Part A · doi:10.1016/j.shpsa.2013.11.001 · Cited by 10
Setlur, V., & Kumar, A. (2020) · IEEE Visualization Conference (VIS) · doi:10.1109/vis47514.2020.00050 · Cited by 10
“Handling system expectations, biases, and failures: NL algorithms have shown to exhibit socio-economic biases, including gender and racial assumptions often due to the nature of the training data [17].”
BackgroundBackfiring and favouring: how design processes in HCI lead to anti-patterns and repentant designers
Widdicks, K., Pargman, D. S., & Björk, S. L. (2020) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3419249.3420175 · Cited by 12
Turning biases into hypotheses through method: A logic of scientific discovery for machine learning
Enni, S. A., & Herrie, M. B. (2021) · Big Data & Society · doi:10.1177/20539517211020775 · Cited by 10
“…can be difficult to ensure, when decisions are made on the basis of an “automatically” learning system (Burrell, 2016; Citron and Pasquale, 2014; Friedman and Nissenbaum, 1996; Kroll et al., 2017; Pasquale, 2015), and hidden uncertainties and biases encoded in data sets can have subtle…”
BackgroundTowards a Closer Look at the Pipes and Joints of Educational Data Infrastructures
Richter, C., Raffel, L.-A., & Allert, H. (2021) · Seminar net · doi:10.7577/seminar.4232 · Cited by 9
Jacobs, M., & Simon, J. (2022) · Digital Society · doi:10.1007/s44206-022-00009-z · Cited by 10
“…involved in managing training data or developing decision models in many cases lack or cannot fully account for information on societal features of the context of use of an end-user application in order to adjust the AI system to this context and, e.g., avoid bias (see Friedman & Nissenbaum, 1996).”
BackgroundMarkets, market algorithms, and algorithmic bias
van Basshuysen, P. (2022) · Journal of Economic Methodology · doi:10.1080/1350178x.2022.2100919 · Cited by 11
Pervasive computing and an aging populace
Shankar, K. (2010) · Journal of Information Communication and Ethics in Society · doi:10.1108/14779961011071051 · Cited by 10
“To counter this form of technological determinism and counteract inherent biases, Friedman and Nissenbaum (1996)”
BackgroundAddressing Social Bias in Information Retrieval
Otterbacher, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-98932-7_11 · Cited by 10
Fair pairwise learning to rank
Cerrato, M., Köppel, M., Segner, A., Esposito, R., & Krämer, S. (2020) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa49011.2020.00083 · Cited by 11
“While the definitions of “bias” and “fairness” are still a topic of active academic discussion, it is not a newly found concern, in neither machine learning nor computer science, where discussions on the matter date back to the nineties [17].”
BackgroundSaldanha, D. M. F., & da Silva, M. B. (2020) · Cadernos EBAPE BR · doi:10.1590/1679-395120190023x · Cited by 10
“Algorithms have biases that can reflect discriminatory practices in society, due to the limitations of computational systems which may be perceived only after interaction with users (FRIEDMAN and NISSENBAUM, 1996).”
BackgroundNews automation, materialities, and the remix of an editorial process
Dierickx, L. (2021) · Journalism · doi:10.1177/14648849211023872 · Cited by 10
“Although biases are not always only human: they can also be institutional or technical, and they are not always easy to identify (Friedman and Nissenbaum, 1996).”
BackgroundDiversity Concepts in Computer Science and Technology Development: A Critique
Schelenz, L. (2022) · Science Technology & Human Values · doi:10.1177/01622439221122549 · Cited by 12
“The focus on the conceptual notion of diversity is inspired by works that point to the value-ladenness of technology (Friedman and Nissenbaum 1996).”
BackgroundDigital libraries and human values: Human‐computer interaction meets social informatics
Fleischmann, K. R. (2007) · Proceedings of the American Society for Information Science and Technology · doi:10.1002/meet.1450440229 · Cited by 10
Airing Grievances Online: Search Engine Algorithms and the Fate of Minorities at Risk
Asal, V. H., & Harwood, P. G. (2008) · Journal of Information Technology & Politics · doi:10.1080/19331680801915009 · Cited by 9
“37; see also Anderson & Henderson, 1997 ; Friedman & Nissenbaum, 1996 ; Gerhart, 2004 ; Ward, 2001 ).”
Critique as Collaboration in Design Anthropology
Forlano, L., & Smith, S. L. (2018) · Journal of Business Anthropology · doi:10.22439/jba.v7i2.5607 · Cited by 10
Blyth, A. (1999) · Journal of Organizational Computing and Electronic Commerce · doi:10.1207/s153277440904_3 · Cited by 10
Bunker, D. (2000) · Journal of the Association for Information Systems · Cited by 10
Havens, L., Terras, M., Bach, B., & Alex, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.05911 · Cited by 12
“Consequently, all NLP systems are subject to biases originating in the social contexts in which the systems are built (“preexisting bias”) (Friedman and Nissenbaum, 1996).”
BackgroundFairCompass: Operationalizing Fairness in Machine Learning
Liu, J., Chen, H., Shen, J., & Choo, K. R. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3348429 · Cited by 12
“This may even result in technical bias [25].”
BackgroundCastells, P., & Jannach, D. (2024) · ACM eBooks · doi:10.1145/3674127.3674131 · Cited by 10
A Survey on Bias in Machine Learning Research
Mikołajczyk, A., & Grochowski, M. (2025) · IEEE Access · doi:10.1109/access.2025.3649998 · Cited by 10
“Emergent bias arises in the context of use with real users [35].”
InfluentialBüyük Veri (Big Data)’nin Yapay Zekâ Uygulamalarında Toplumsal Sınıflandırmaya Yönelik Kaygılar
ÜNAL, S., & Sezgin, A. A. (2021) · AJIT-e Academic Journal of Information Technology · doi:10.5824/ajite.2021.01.004.x · Cited by 14
Trustworthy Autonomous Systems (TAS): Engaging TAS experts in curriculum design
Naiseh, M., Bentley, C., & Ramchurn, S. D. (2022) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon52537.2022.9766663 · Cited by 12
“, [23,24], we need empirically grounded research on the TAS skills and knowledge needed for engineers to engage with this complexity.”
BackgroundSex and Gender in the Computer Graphics Research Literature
Dodik, A., Sellán, S., Kim, T., & Phillips, A. (2022) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/3532836.3536227 · Cited by 9
“We apply the framework of Suresh and Guttag [2021], which categorizes bias by its stage in a system’s lifecycle (cf. [Friedman and Nissenbaum 1996; Mehrabi et al. 2021; Olteanu et al. 2019]).”
MethodDatanalysing the uninsured: The coloniality of inclusive insurance platforms
Perticone, Y., Graz, J.-C., & Rahel, K. (2022) · Competition & Change · doi:10.1177/10245294221125849 · Cited by 10
“Thereby, racial biases in platforms’ big data analysis are mainly implicit, unconscious and often rooted in the social thoughts, institutions and practices prior to, or embedded in, the creation of the system (Friedman and Nissenbaum, 1996; Turner Lee, 2018; Simon et al., 2020).”
BackgroundAI in Education: Some Thoughts About Ethics, Equity, and Social Impact
Jones, J. W. (2024) · Postdigital science and education · doi:10.1007/978-3-031-64487-0_3 · Cited by 12
Comandé, G. (2017) · Studies in big data · doi:10.1007/978-3-319-54024-5_8 · Cited by 10
Promoting Fairness in Learned Models by Learning to Active Learn under Parity Constraints
Sharaf, A., Daumé, H., & Ni, R. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3534632 · Cited by 11
“Concerns about biased or disparate treatment of groups or individuals by computer systems has been raised since the 1990s [21].”
BackgroundSaldanha, D. M. F., & da Silva, M. B. (2020) · Cadernos EBAPE BR · doi:10.1590/1679-395120190023 · Cited by 30
Bias mitigation with AIF360: A comparative study
Hufthammer, K. T., Aasheim, T. H., Ånneland, S., Brynjulfsen, H., & Slavkovik, M. (2020) · NORA - Norwegian Open Research Archives · Cited by 8
van Leeuwen, C., Smets, A., Jacobs, A., & Ballon, P. (2021) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3468507.3468514 · Cited by 9
“Friedman and Nissenbaum [25] coined the term “pre-existing bias” to describe the same effects.”
BackgroundMethodInfluentialToward Annotator Group Bias in Crowdsourcing
Liu, H., Thekinen, J., Mollaoglu, S., Tang, D. Q., Yang, J. S., Cheng, Y., Liu, H., & Tang, J. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.126 · Cited by 16
“Bias is caused due to pre-existing societal norms (Friedman and Nissenbaum, 1996), data source, data labeling, training algorithms, and post-processing models.”
Examining the technology-mediated cycles of injustice that contribute to digital ageism
Chu, C. H., Nyrup, R., Donato‐Woodger, S., Leslie, K., Khan, S. S., Bernett, C., & Grenier, A. M. (2022) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3529190.3534765 · Cited by 11
“According to Friedman and Nissenbaum [6]: “a system discriminates unfairly if it denies an opportunity or a good or if it assigns an undesirable outcome to an individual or group of individuals on grounds that are unreasonable or inappropriate” (p.”
BackgroundInfluentialEthical Awareness in Paralinguistics: A Taxonomy of Applications
Batliner, A. M., Neumann, M., Burkhardt, F., Baird, A., Meyer, S., Vu, N. T., & Schuller, B. W. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2140385 · Cited by 9
“Value by design (Friedman, 1996) and bias in computer systems (Friedman & Nissenbaum, 1996) have already been discussed in the 1990s.”
From meaning well to doing well: ethical expertise in the GIS domain
Huff, C. (2014) · Journal of Geography in Higher Education · doi:10.1080/03098265.2014.936314 · Cited by 9
“Does one simply blame the users for not taking the time, or think through how to redesign the interface to require less time to make appropriate privacy settings (Friedman, Kahn, & Borning, 2006; Friedman & Nissenbaum, 1996)?”
BackgroundFeinberg, M. (2017) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2998181.2998204 · Cited by 11
“Freedom from bias is a pervasive value in user-centered design, and it holds an even stronger position for information studies [17, 27].”
BackgroundUrueña, R. (2019) · Latin American Law Review · doi:10.29263/lar02.2019.05 · Cited by 9
Value sensitive design of interactions with urbansim indicators
Borning, A. H., Friedman, B., & Davis, J. (2006) · Cited by 9
Self-citationBig Data Analytics and AI in Mental HealthcarePreprint
Rosenfeld, A., Benrimoh, D., Armstrong, C., Mirchi, N., Langlois-Therrien, T., Rollins, C. P. E., Tanguay-Sela, M., Mehltretter, J., Fratila, R., Israel, S., et al. (2019) · arXiv.org · doi:10.48550/arxiv.1903.12071 · Cited by 19
“While relying on physician judgement seems justified for now as algorithms are still limited and laden with potential biases [91], it will become less evident as this technology continues to develop and improves in accuracy and quality.”
BackgroundMapping ethical concerns in algorithm-driven period and fertility tracking technologies
Punzi, M. C., & Thuis, T. (2025) · Contraception · doi:10.1016/j.contraception.2025.110837 · Cited by 8
Naudts, L., Helberger, N., Veale, M., & Sax, M. (2025) · Journal of Consumer Policy · doi:10.1007/s10603-025-09586-1 · Cited by 17
“Unsurprisingly, without intervention, the systems trained on said data will reflect these societal biases (Friedman & Nissenbaum, 1996).”
George, M. B., Ijiga, M. O., & Adeyemi, O. (2025) · doi:10.38124/ijisrt/25mar1859 · Cited by 8
Ten reasons why—the case for more and better AI regulation
Wörsdörfer, M. (2025) · AI and Ethics · doi:10.1007/s43681-025-00865-8 · Cited by 11
“…these technologies exacerbate inequalities, deepen social divisions, and undermine democratic principles through harmful feedback 11 AI biases refer to biased computer systems that unfairly deny opportunities or assign undesirable outcomes based on unreasonable or inappropriate grounds [81].”
Ferrero, F., & Barujel, A. G. (2019) · Latin American Conference on Learning Technologies (LACLO) · doi:10.1109/laclo49268.2019.00038 · Cited by 7
“As Friedmann and Nissenbaum [28] already formulated in their classic work, there are various sources of biases.”
BackgroundMethodMaschinelles Lernen zur Vorhersage chemischer Reaktivität: Die Bedeutung “gescheiterter” Experimente
Strieth‐Kalthoff, F., Sandfort, F., Kühnemund, M., Schäfer, F. R., Kuchen, H., & Glorius, F. (2022) · Angewandte Chemie · doi:10.1002/ange.202204647 · Cited by 8
Jung, S., Salminen, J. O., & Jansen, B. J. J. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3532203 · Cited by 8
“Research in this area explores algorithmic bias, focusing on how systems systematically discriminate against particular individuals and groups [19].”
BackgroundWernick, A., Banzuzi, E., & Mörelius-Wulff, A. (2023) · Internet Policy Review · doi:10.14763/2023.1.1698 · Cited by 9
Investigating Semi-Automatic Assessment of Data Sets Fairness by Means of Fuzzy Logic
Gallese, C., Scantamburlo, T., Manzoni, L., & Nobile, M. S. (2023) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb56990.2023.10264913 · Cited by 8
“Data is one of the most common source of algorithmic discrimination, a problem studied since the nineties and referred to “computer systems that systematically and unfairly discriminate against certain individuals or groups” [1].”
Jarke, J., Prietl, B., Egbert, S., Boeva, Y., Heuer, H., & Arnold, M. (2024) · Amsterdam University Press eBooks · doi:10.1515/9789048556908 · Cited by 18
Foresight: Fifty Years to Think Your Futures
Cuhls, K. E., Dönitz, E. J., Erdmann, L., Gransche, B., Kimpeler, S., Schirrmeister, E., & Warnke, P. (2024) · Sustainability and innovation · doi:10.1007/978-3-031-66100-6_4 · Cited by 13
Richterich, A. (2016) · Law, governance and technology series · doi:10.1007/978-3-319-33525-4_3 · Cited by 7
Accelerating Financial Innovation Through RegTech
Narang, S. A. (2020) · Advances in finance, accounting, and economics book series · doi:10.4018/978-1-7998-4390-0.ch004 · Cited by 8
Power in the modern ‘surveillance society’: From theory to methodology
Rudschies, C. (2022) · Information Polity · doi:10.3233/ip-211536 · Cited by 13
A Meta-learning Approach to Fair Ranking
Wang, Y., Tao, Z., & Fang, Y. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531892 · Cited by 8
“As addressed by Friedman [10], the historic discrimination to the socially underrepresented group in the dataset will make its way into the model as the pattern will be observed during the training process.”
BackgroundFairness in machine learning from the perspective of sociology of statistics
Benbouzid, B. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3593974 · Cited by 15
“4In their article “Bias in Computer Systems” [21], Friedman and Nissenbaum describe three types of bias in software.”
BackgroundAn approach to early evaluation of informational privacy requirements
Barn, B. S., Primiero, G., & Barn, R. (2015) · ACM Symposium on Applied Computing · doi:10.1145/2695664.2695788 · Cited by 8
“Friedman is generally credited with introducing the study of values to IS practice through the Value-sensitive design (VSD) [11], defined as: what a person or group of people consider important in life .”
BackgroundEthical implications of technical limitations in GIS
Graeff, C., & Loui, M. C. (2008) · IEEE Technology and Society Magazine · doi:10.1109/mts.2008.930566 · Cited by 8
“According to the decision criteria, an applicant who lives in an “undesirable” location such as a low income neighborhood could be less likely to receive a loan [10].”
BackgroundInfluentialRantavuo, H. (2019) · International HCI and UX Conference in Indonesia (CHIuXiD) · doi:10.1145/3328243.3328268 · Cited by 9
“Practitioners can quickly build awareness on ethical questions on AI through key scientific articles in the field (5, 9, 10, 11, 15, 16).”
BackgroundDesign for the Values of Democracy and Justice
Pols, A. J. K., & Spahn, A. (2015) · Springer eBooks · doi:10.1007/978-94-007-6970-0_13 · Cited by 11
Algorithms are not neutral: Bias in collaborative filteringPreprint
Stinson, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.01031 · Cited by 8
“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].”
BackgroundBeyond "Fairness:" Structural (In)justice Lenses on AI for EducationPreprint
Madaio, M., Blodgett, S. L., Mayfield, E., & Dixon‐Román, E. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.08847 · Cited by 39
“Early work in this area examined several types of bias arising from computational systems [Friedman and Nissenbaum, 1996], with other work critiquing the “mythology” of Big Data— “the widespread belief that large datasets offer a higher form of intelligence and knowledge that can generate insights…”
Wernick, A., & Artyushina, A. (2023) · Internet Policy Review · doi:10.14763/2023.1.1695 · Cited by 10
Algorithmic solutions, subjectivity and decision errors: a study of AI accountability
Biju, P. R., & Gayathri, O. (2024) · Digital Policy Regulation and Governance · doi:10.1108/dprg-05-2024-0090 · Cited by 17
Revisiting Technical Bias Mitigation Strategies
Mahamadou, A. J. D., & Trotsyuk, A. A. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-103123-095737 · Cited by 13
A Literature Survey on Multimodal and Multilingual Sexism Detection
Luo, X., Liang, B., Wang, Q., Li, J., Cambria, E., Xiao-jun, Z., He, Y., Yang, M., & Xu, R. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3561921 · Cited by 7
“4 According to [14], bias in computer systems has three categories: pre-existing, technical, and emergent bias.”
Understanding Human-AI Misalignment in LLM-Based Job-Seeking Support for Neurodivergent Users
Hall, K., Ma, M., Zhang, X., Swain, V. D., & Kim, J. G. (2025) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3663547.3746361 · Cited by 8
“Historically, bias emerges in technologies and algorithms when the users have significantly different characteristics from the population that is assumed to use it, such as differences in values, experiences, or world-views [23].”
Political censorship in large language models originating from China
Pan, J., & Xu, X. (2026) · PNAS Nexus · doi:10.1093/pnasnexus/pgag013 · Cited by 12
(Some) algorithmic bias as institutional bias
Flowerman, C. H. (2023) · Ethics and Information Technology · doi:10.1007/s10676-023-09698-7 · Cited by 8
“6 In their explanation of biased computer systems, Friedman & Nissenbaum write that a “system discriminates unfairly if it denies an opportunity or a good or if it assigns an undesirable outcome to an individual or group of individuals on grounds that are unreasonable or inappropriate” (1996).”
BackgroundABCs: Differentiating Algorithmic Bias, Automation Bias, and Automation Complacency
Potasznik, A. (2023) · IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS) · doi:10.1109/ethics57328.2023.10155094 · Cited by 9
“Twenty years later in 1996, a seminal work by Batya Friedman and Helen Nissenbaum laid out a comprehensive classification system for biases in computer systems [15].”
BackgroundInfluentialPutting algorithmic bias on top of the agenda in the discussions on autonomous weapons systems
Bhila, I. (2024) · Digital War · doi:10.1057/s42984-024-00094-z · Cited by 8
“…three categories: preexisting bias-which is influenced by unequal social structures and culture, technical bias—that emanates from technical shortcomings, and emergent bias—which is a result of a change in environment or context within which the algorithm is used (Friedman and Nissenbaum 1996).”
Eventuality of an Apartheid State of Things
Sholla, S., Mir, R. N., & Chishti, M. A. (2018) · International Journal of Technoethics · doi:10.4018/ijt.2018070106 · Cited by 7
Third Space, Information Sharing, and Participatory Design
Hansen, P., Fourie, I., & Meyer, A. (2021) · Synthesis lectures on information concepts, retrieval, and services · doi:10.1007/978-3-031-02327-9 · Cited by 14
Modelling the long-term fairness dynamics of data-driven targeted help on job seekers
Scher, S., Kopeinik, S., Trügler, A., & Kowald, D. (2023) · Scientific Reports · doi:10.1038/s41598-023-28874-9 · Cited by 11
Hubness as a Case of Technical Algorithmic Bias in Music Recommendation
Flexer, A., Dörfler, M., Schlüter, J., & Grill, T. (2018) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2018.00154 · Cited by 7
Fairness-Aware Machine Learning
Bird, S., Hutchinson, B., Kenthapadi, K., et al. (2019) · ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) · doi:10.1145/3292500.3332280 · Cited by 7
Sender vs. recipient-orientated information systems revisited
MacFarlane, A., Missaoui, S., Makri, S., & Lopez, M. G. (2021) · Journal of Documentation · doi:10.1108/jd-10-2020-0177 · Cited by 9
Le, B., Spina, D., Scholer, F., & Chia, H. X. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-09316-6_5 · Cited by 8
“Friedman and Nissenbaum [4] defined a biased system as one that systematically treats specific individuals or groups differently from others, providing either unfair advantages or disadvantages.”
BackgroundA Tale of Two Deficits: Causality and Care in Medical AI
Chen, M. (2019) · Philosophy & Technology · doi:10.1007/s13347-019-00359-6 · Cited by 9
“…(especially given the more value-laden WHO conception of positive health that I have adopted for the purposes of my discussion). acquiring moral status, it has been argued in the computer ethics field that values and biases are embedded into computer systems (Friedman and Nissenbaum 1996).”
BackgroundOrdun, C. Y., Raff, E., & Purushotham, S. (2020) · arXiv (Cornell University) · doi:10.13016/m2blek-cjvs · Cited by 24
“More broadly, future work should take careful consideration into the scientific questions their research is tackling and the impact it may have in developing or prolonging undesired biases (Friedman and Nissenbaum, 1996).”
BackgroundSituating questions of data, power, and racial formation
Shelby, R. M., & Henne, K. (2022) · Big Data & Society · doi:10.1177/20539517221090938 · Cited by 7
“This scholarship evinces how big data co-produces racialized social phenomena and inequalities, extending claims that data and datafication are cultural processes (e.g. Friedman and Nissenbaum, 1996; Gitelman, 2013; Kitchin, 2014).”
Background
Metadata
- Authors
- Friedman, B., & Nissenbaum, H.
- Affiliations
- Colby College (US)
- Princeton University (US)
- Venue
- ACM Transactions on Information Systems
- Published
- 1996-07
- Volume
- 14
- Issue
- 3
- Pages
- 330-347
- ISSN
- 1046-8188 (ISSN-L)
- 1558-2868
- OpenAlex
- W2006447892
- Cited by
- 1,203
- References
- 13
- Citations by year
- 2026: 102 · 2025: 149 · 2024: 151 · 2023: 121 · 2022: 126 · 2021: 133 · 2020: 79 · 2019: 70 · 2018: 50 · 2017: 21 · 2016: 29 · 2015: 18 · 2014: 18 · 2013: 16 · 2012: 8
- FWCI
- 17.25
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Transactions on Information Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Systems Theories and Implementation
- Social Sciences › Law › Artificial Intelligence in Law
- Keywords
- technical bias, social institutions, technological constraints, societal impact of technology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
