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
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.
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- 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.
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- 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.
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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.
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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.
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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.”
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Items 801–900 of 1,155
IS EDUCATIONAL TECHNOLOGY USEFUL TO MATHEMATICS TEACHERS ACTIVISTS
Stoilescu, D. (2009) · Acta Didactica Napocensia · Cited by 2
Prinsloo, P., & Slade, S. (2016) · IGI Global eBooks · doi:10.4018/978-1-4666-9840-6.ch079 · Cited by 2
“…impacts of those algorithms become clearer (Eubanks, 2015; Henman, 2004; Kalhan, 2013; Pasquale, 2015), so too does the unease around accuracy, reliability and inherent biases (Bozdag, 2013; Diakopoulous, 2014a, 2014b; Friedman & Nissenbaum, 1996; Kitchen, 2014b; Pariser, 2011; Pasquale, 2011).”
BackgroundSeeing mobility: how software engineers produce unequal representations
Sanches, P. (2016) · Engineering Studies · doi:10.1080/19378629.2016.1163572 · Cited by 2
Zwischen Bias und Diversität – Bildung und Diversity im Kontext algorithmischer Strukturen
Verständig, D. (2019) · Springer eBooks · doi:10.1007/978-3-658-26753-7_5 · Cited by 6
Design for the Values of Democracy and Justice
Pols, A. J. K., & Spahn, A. (2014) · Springer eBooks · doi:10.1007/978-94-007-6994-6_13-1 · Cited by 2
The Bias Cut: Toward a Technopoetics of Algorithmic Systems
Rosner, D. K. (2022) · Catalyst Feminism Theory Technoscience · doi:10.28968/cftt.v8i2.37867 · Cited by 2
Ospitia-Medina, Y., Beltrán, J. R., & Baldassarri, S. (2023) · Personal and Ubiquitous Computing · doi:10.1007/s00779-023-01721-4 · Cited by 2
“According to Friedman and Nissenbaum, biases in computer science can be classified into 3 categories [19]: preexisting, technical, and emergent.”
BackgroundŽalnieriūtė, M. (2024) · Cambridge University Press eBooks · doi:10.1017/9781009321211.009 · Cited by 2
Resilience and Values: Antecedents for Effective Co-design of Information Systems
Barn, B. S., & Barn, R. (2015) · Middlesex University Research Repository (Middlesex University Of London) · doi:10.18151/7217272 · Cited by 3
Conducting an ethical study of web traffic
Duncan, J. F., & Camp, L. J. (2012) · Cited by 2
Identifying Risks in Datasets for Automated Decision–Making
Mecati, M., Cannavò, F., Vetrò, A., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-57599-1_25 · Cited by 2
“Generally speaking, discrimination can be defined as an “unjustified distinction of individuals based on their membership, or perceived membership, in a certain group or category” [10].”
BackgroundGartner, M. (2021) · Proceedings of the ICA · doi:10.5194/ica-proc-4-36-2021 · Cited by 2
“…with a multitude of known biases, with respect to both perception and cognition that the user may display (Wall et al. 2019), the most obvious of which is the implicit or explicit ranking of the displayed information; a choice likely to influence the user (Taib 1990; Friedman and Nissenbaum 1996).”
BackgroundAspects of values in human-technology interaction design : a content-based view to values
Maksimainen, J. P. (2011) · Jyväskylä University Digital Archive (University of Jyväskylä) · Cited by 2
Understanding values and biases in I.T.
Birrer, F. A. J. (1999) · ACM SIGCAS Computers and Society · doi:10.1145/382042.382047 · Cited by 2
Communication and Collaboration in Agile Software Development Teams
Whitworth, E. (2006) · Cited by 2
Through the Social Web: Citizen-led Participation in Ontario Policy-making
Smith, K. L. (2014) · TSpace (University of Toronto) · Cited by 2
Website gender perceptions: effects and recommendations for gender inclusivity
Stonewall, J. H. (2016) · doi:10.31274/etd-180810-4777 · Cited by 2
Transparent Lives and the Surveillance State: Policing, New Visibility, and Information Policy
Newell, B. C. (2015) · ResearchWorks at the University of Washington (University of Washington) · Cited by 2
What 45,000 Seniors Value About Online Social Interaction
Burmeister, O. K. (2008) · Charles Sturt University Research Output (CRO) · Cited by 2
LearnxDesign 2021: Engaging with challenges in design education
Bohemia, E., Nielsen, L. M., Pan, L., Börekçi, N. A. G. Z., & Zhang, Y. (2021) · doi:10.21606/drs_lxd2021.001 · Cited by 2
Algorithmic ExclusionPreprint
Albert, K., & Delano, M. K. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4122529 · Cited by 2
Fairness Challenges in Artificial Intelligence
Chakrobartty, S., & El-Gayar, O. (2023) · IGI Global eBooks · doi:10.4018/978-1-7998-9220-5.ch101 · Cited by 2
Future Approach Generative AI, Stylized Architecture, and its Potential in Finance
Bhatnagar, A. B., Nanad, A., Kumar, A., & Kumar, R. (2025) · Wiley eBooks · doi:10.1002/9781394271078.ch3 · Cited by 3
Sathio, A. A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5186331 · Cited by 2
Identifying Imbalance Thresholds in Input Data to Achieve Desired Levels of Algorithmic Fairness
Mecati, M., Adrignola, A., Vetrò, A., & Torchiano, M. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10021078 · Cited by 1
“are unreasonable or inappropriate” [3].”
Link, User-Centred Designer: Game Characters as Transcendent Models
Seaborn, K. (2023) · Annual Symposium on Computer-Human Interaction in Play · doi:10.1145/3573382.3616052 · Cited by 1
“This is not merely bias or "slant," nor is it necessarily negative [6].”
BackgroundDraude, C. (2023) · Springer eBooks · doi:10.1007/978-3-031-45304-5_6 · Cited by 1
Facial Recognition Technology in Context
Matulionytė, R., & Žalnieriūtė, M. (2024) · Cambridge University Press eBooks · doi:10.1017/9781009321211.002 · Cited by 1
Algorithm-driven populism: An introduction
França, L. A., & de Abreu, C. A. F. (2023) · Archiwum Kryminologii. · doi:10.7420/ak2021.29 · Cited by 1
Group Fairness Refocused: Assessing the Social Impact of ML Systems
Hertweck, C., Loi, M., & Heitz, C. (2024) · IEEE Swiss Conference on Data Science (SDS) · doi:10.1109/sds60720.2024.00034 · Cited by 2
“[7, p. 332] “use the term bias to refer to computer systems that systematically and unfairly discriminate against certain individuals or groups of individuals in favor of others”.”
BackgroundAlgorithmic Innovations: Pioneering the Future of Library Services
Meesad, P., & Mingkhwan, A. (2024) · Studies in big data · doi:10.1007/978-3-031-69216-1_3 · Cited by 2
Rutherford, A., LePere‐Schloop, M., & Perai, N. A. A. (2024) · Nonprofit and civil society studies · doi:10.1007/978-3-031-67896-7_10 · Cited by 1
eCommerce Search and Discovery
Kallumadi, S. T., & King, T. H. (2024) · ACM eBooks · doi:10.1145/3674127.3674140 · Cited by 1
More Than Bias: Social and Technological Selectivities of (Subsymbolic) Artificial Intelligence
Huchler, N. (2024) · Springer eBooks · doi:10.1007/978-3-658-45708-2_3 · Cited by 1
Ethical Framework for Multimodal AI Systems
Patkar, U. C., Pitrubhakta, V., Sutar, S., Arora, A., Jaiswal, R., Bhardwaj, S., & Patil, U. S. (2025) · International Conference on AIML-Applications for Engineering & Technology (ICAET) · doi:10.1109/icaet63349.2025.10932210 · Cited by 1
“[18].”
Martínez, J. C., Alemán, M. D. D., & de la Torre, J. (2025) · ANIAV - Revista de Investigación en Artes Visuales · doi:10.4995/aniav.2025.23353 · Cited by 1
Filleul, A., Drolet, M.-J., Sigouin, J., Hudon, A., & Kairy, D. (2025) · BMJ Open · doi:10.1136/bmjopen-2025-099728 · Cited by 1
“26 These are people who already have easier access to rehabilitation; therefore, TR has the potential not to reduce, but to exacerbate pre-existing biases 27 such as inequalities in health, particularly in terms of access to care.”
AI in Healthcare: Do Not Forget About Allied Healthcare
Hulsen, T., & Scheper, M. C. (2025) · AI · doi:10.3390/ai6060114 · Cited by 1
Ndjama, J. D. N., & Westhuizen, J. V. D. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-9989-7.ch005 · Cited by 1
de Villiers, R. (2025) · Administrative Sciences · doi:10.3390/admsci15070259 · Cited by 1
Design as Translation, Translation as Design: Toward Critical, Creative, and Ethical Pedagogies
Oleson, A., & Taylor, L. K. (2025) · doi:10.1145/3742901.3742902 · Cited by 1
Boriati, D., & D’Ambrosio, M. (2025) · Community Notebook People Education and Welfare in the Society 5 0 · doi:10.61007/qdc.2025.1.272 · Cited by 1
UX values framing: an authoring tool to capture children’s values for widening narratives
Caelenberghe, M., Lochrie, M., & Read, J. C. (2025) · Interacting with Computers · doi:10.1093/iwc/iwaf032 · Cited by 1
Ethical Implications of User Autonomy in Digital Public Health
Dassow, H.-H., & Eßmeyer, T. (2025) · Springer series on epidemiology and public health · doi:10.1007/978-3-031-90154-6_29 · Cited by 1
Ethical Challenges of the IA in Byung Chul Han’s Theory
Pedreros, P. A. M., & Pajajoy, M. J. S. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-97907-1_23 · Cited by 1
Empowering Tenants and Landlords Through Conversational AI for Housing Rights
Subedi, N. (2025) · interactions · doi:10.1145/3767299 · Cited by 1
“The Legal Services Corporation reports a vast “justice gap,” where the civil legal needs of a majority of low-income Americans go unmet, leaving them to face complex challenges alone [2].”
Gender bias in Artificial Intelligence
Cuesta, J. (2025) · Journal of Economic Policy Reform · doi:10.1080/17487870.2025.2579039 · Cited by 1
“Friedman and Nissenbaum (1996) and Bender and Friedman (2018) expand this framework by differentiating between pre-existing biases from societal norms, technical biases from algorithmic design, and emergent biases from the misapplication of AI systems.”
Bell, A. S., Fonseca, J., Abrate, C., Bonchi, F., & Stoyanovich, J. (2025) · ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) · doi:10.1145/3757887.3763014 · Cited by 1
Hensel, P. G. (2025) · Scientometrics · doi:10.1007/s11192-025-05466-0 · Cited by 2
“The political nature of algorithms is rooted in their function of classification systems that may impact societal understandings of reality (Friedman & Nissenbaum, 1996; Tufekci, 2015).”
The Digital Platform's Influence on Work and Social Inequality
Demirtaş, Ş. C., & Şahin, S. Ç. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-6082-9.ch003 · Cited by 1
Here's what you need to know about my data: Exploring Expert Knowledge's Role in Data Analysis
Lin, H., Lisnic, M., Akbaba, D., Meyer, M., & Lex, A. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3634821 · Cited by 1
“The presence of biases in data hunches is compounded when other analysts encounter and subsequently base their interpretations on these potentially skewed data hunches, leading to an unjustified outcome based on the data [21].”
A humble proposal for GeoAI: Epistemology, methodology, and relevance in curb ramp classification
Deitz, S. L. (2025) · Canadian Geographies / Géographies canadiennes · doi:10.1111/cag.70043 · Cited by 2
Safari, N., & Techatassanasoontorn, A. A. (2025) · European Journal of Information Systems · doi:10.1080/0960085x.2025.2599226 · Cited by 1
“…examining various technologies and design contexts, including artificial intelligence and autonomous agents (Friedman, 1997), computer algorithms (Friedman & Nissenbaum, 1996), crowdsourcing (Deng et al., 2016), and ICT-enabled self-management of chronic diseases (Dadgar & Joshi, 2018), among…”
The Role of Artificial Intelligence in Legal Decision-Making
Beg, M. S. (2026) · Springer eBooks · doi:10.1007/978-3-032-09582-4_6 · Cited by 1
ReFaRAG: Re-ranking for Bias Mitigation in Retrieval-Augmented Generation
Zhao, Y., Efthymiou, V., Nummenmaa, J., & Stefanidis, K. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-05727-3_42 · Cited by 3
Singh, P., Angra, P. K., & Ahmed, H. M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5871-0.ch009 · Cited by 1
Who governs the ledger: rethinking blockchain governance through democratic innovation
Motea, M., & Oba, P. (2026) · Humanities and Social Sciences Communications · doi:10.1057/s41599-026-06980-z · Cited by 1
“All technical systems carry embedded values (Friedman and Nissenbaum, 1996).”
Beyond Bias Detection: Community Auditors and Normative Reasoning in AI Oversight CSCW006
Jackson, C., Ahmad, T., Raj, S. D., & Wu, N. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3788042 · Cited by 1
“Additionally, some harms only emerge, or are only perceived as problematic, within specific use contexts, which formal audits may not adequately simulate [13, 18, 20, 22, 38, 39].”
Sheng, K., Tuyttens, F., & von Keyserlingk, M. A. G. (2026) · Technology in Society · doi:10.1016/j.techsoc.2026.103466 · Cited by 4
Exploring cyberbullying through visual narratives
Bowler, L., Mattern, E., Knobel, C. P., & Keilty, P. (2013) · D-Scholarship@Pitt (University of Pittsburgh) · doi:10.9776/13360 · Cited by 1
Cayaba, C. T., & Pablo, Z. C. (2013) · RMIT Research Repository (RMIT University Library) · Cited by 1
Security Networks and Human Autonomy: A Philosophical Investigation
Lundie, D. C. (2016) · Palgrave Macmillan UK eBooks · doi:10.1057/978-1-349-94885-7_4 · Cited by 1
Bouchagiar, G. (2019) · Review of European Studies · doi:10.5539/res.v11n1p27 · Cited by 1
The Importance of Assuring Algorithm-based Verification Agents
Haugen, O. I. (2019) · MATEC Web of Conferences · doi:10.1051/matecconf/201927301010 · Cited by 1
8.4 Theorizing Relational Privacy: Embodied Perspectives to Support Ethical Professional Pedagogies
Lundie, D. C. (2014) · Springer international handbooks of education · doi:10.1007/978-94-017-9282-0_72 · Cited by 1
Martin, K. E., & Shilton, K. (2018) · Cambridge University Press eBooks · doi:10.1017/9781316831960.005 · Cited by 1
Exploring Siri’s Content Diversity Using a Crowdsourced Audit
Glaesener, T. (2021) · Journal of Digital Social Research · doi:10.33621/jdsr.v4i1.115 · Cited by 2
“Theoretically analyzing “twelve different information filtering scenarios” (Geschke et al., 2019, p. 129), Geschke et al. (2019) found that “even without any social or technological filters, echo chambers emerge as a consequence of cognitive mechanisms, such as confirmation bias” (Geschke et al.”
BackgroundMethodInfluentialEncountering development in social design education
Nöel, L.-A. (2021) · doi:10.21606/drs_lxd2021.04.275 · Cited by 1
“…would also then be accompanied by mechanisms to remedy bias (Pargman, 1999), so that designers could produce better solutions that are relevant to the contexts in which they are practicing, limiting their own pre-existing social and technical biases (Friedman & Nissenbaum, 1996; Pargman, 1999).”
Funer, F. (2023) · Techno:Phil · doi:10.1007/978-3-662-65868-0_4 · Cited by 1
Learning to Re-rank with Constrained Meta-Optimal Transport
Hoyos-Idrobo, A. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591714 · Cited by 1
“Such disparities in exposure-outcome raise concerns of algorithmic fairness and bias of moral import [24], as they may contribute to representational and allocative harms [14].”
BackgroundFairness in Ranking: From Values to Technical Choices and Back
Stoyanovich, J., Zehlike, M., & Yang, K. (2023) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3555041.3589405 · Cited by 2
Responsible i nnovation and d igital p latforms: The c ase of o nline f ood d elivery
Gossart, C., & Srnec, C. (2024) · Journal of Innovation Economics & Management · doi:10.3917/jie.pr1.0155 · Cited by 3
COOL IT! The objective racism of carceral technofixes
Salman, C. (2024) · Critical Studies in Media Communication · doi:10.1080/15295036.2024.2314667 · Cited by 1
“Critics often suggest that algorithms discriminate because programmers make judgments about which data and mathematical procedures their software should collect, use, and run (boyd & Elish, 2018; Friedman & Nissenbaum, 1996).”
BackgroundSelektivitäten (subsymbolischer) Künstlicher Intelligenz
Huchler, N. (2024) · Springer eBooks · doi:10.1007/978-3-658-43521-9_17 · Cited by 1
Informetric Analysis of Researches on Legal Issues Related to Artificial Intelligence
Ma, G. (2023) · Asia Conference on Cognitive Engineering and Intelligent Interaction (CEII) · doi:10.1109/ceii60565.2023.00031 · Cited by 1
“In terms of types, discrimination caused by AI can be classified into preexisting discrimination [18] and hindsight discrimination [19] according to the time of occurrence.”
BackgroundTransforming Dutch: Debiasing Dutch Coreference Resolution Systems for Non-binary Pronouns
van Boven, G., Du, Y., & Nguyen, D. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3659049 · Cited by 1
“In this work we adopt the definition of bias provided by Friedman and Nissenbaum: “computer systems that systematically and unfairly discriminate against certain individuals or groups of individuals in favor of others” [18].”
MethodInteractive and Explainable Robot Learning: A Comprehensive Review
Seraj, E., Lee, K. M., Zaidi, Z., Xiao, Q., Li, Z., Nascimento, A., van Waveren, S., Tambwekar, P., Paleja, R., Das, D., et al. (2024) · Foundations and Trends in Robotics · doi:10.1561/2300000081 · Cited by 2
Koska, C., Prugger, J., Jörg, S., et al. (2024) · Zeitschrift für Praktische Philosophie · doi:10.22613/zfpp/11.1.14 · Cited by 1
Basha, S. M. J., Kulkarni, A., Choudhary, S., & Reddy, M. L. (2024) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/ijisrt24sep789 · Cited by 1
Schedl, M., Anelli, V. W., & Lex, E. (2024) · The information retrieval series · doi:10.1007/978-3-031-69978-8_2 · Cited by 1
Galuščáková, P., Oard, D. W., & Nair, S. (2024) · ACM eBooks · doi:10.1145/3674127.3674136 · Cited by 2
Gómez, E., Cuesta, H., Gkiokas, A., Gómez-Cañón, J. S., Porcaro, L., & Yesiler, F. (2024) · ACM eBooks · doi:10.1145/3674127.3702959 · Cited by 1
Baeza-Yates, R. A., Murgai, L., & Dudy, S. (2024) · ACM eBooks · doi:10.1145/3674127.3674138 · Cited by 1
Optimizing Diverse Information Exposure in Social Graphs
Colin, J., & Maniu, S. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825032 · Cited by 1
Navigating the Complexity of Human–Technology Interaction: A User Experience Perspective
Lee, C., Jung, H., Mangla, U., Gupta, A. K., Avetisova, K., & Malakhatka, E. (2025) · Springer series in adaptive environments · doi:10.1007/978-3-031-78357-9_8 · Cited by 1
Ferenbok, J. (2010) · Palgrave Macmillan UK eBooks · doi:10.1057/9780230299047_21 · Cited by 1
Bosua, R., Clark, K., Richardson, M., & Webb, J. (2017) · Figshare · Cited by 1
The Right Tool in the Wrong Hands
Spier, S. (2017) · Elsevier eBooks · doi:10.1016/b978-0-08-100567-5.00011-6 · Cited by 1
Unveiling the oracle: Artificial intelligence for the 21st century
Cerutti, F., Grassi, A., & Vallati, M. (2018) · Intelligent Decision Technologies · doi:10.3233/idt-180342 · Cited by 1
“For instance, as reported by Friedman and Nis-senbaum [15], the National Resident Match Program (NRMP). implements a centralised method for assigning medical school graduates their first employment following graduation in USA.”
Mateo, J. F. (2020) · ICADE Revista de la Facultad de Derecho · doi:10.14422/icade.i108.y2019.004 · Cited by 3
Unveiling Real-Life Effects of Online Photo Sharing
Nguyen, V.-K., Popescu, A., & Deshayes-Chossart, J. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00391 · Cited by 1
“The inherently partial mapping of complex real-life processes into computer systems [16] is a first source of bias.”
BackgroundOgbu, N. H., & Ogbaga, I. N. (2021) · International Journal of Communications Network and System Sciences · doi:10.4236/ijcns.2021.148008 · Cited by 1
Studie: Die Darstellung von künstlichen Lebensformen in Computerspielen
Könitz, C. (2022) · MedienPädagogik Zeitschrift für Theorie und Praxis der Medienbildung · doi:10.21240/mpaed/diss.ck/2022.02.27.x · Cited by 1
Algoritmit työnhaussa ja rekrytoinnissa
Koivunen, S., & Lehtovaara, H. (2022) · Työelämän tutkimus · doi:10.37455/tt.119770 · Cited by 1
EthicAmanuensis: supporting machine learning practitioners making and recording ethical decisions
Murray-Rust, D., & Tsiakas, K. (2022) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai56018.2022.00195 · Cited by 1
“One of the existing frameworks for the classification and analysis of bias in computer systems identifies three main categories: pre-existing , technical , and emergent bias [10].”
Tackling the RecSys Side Effects via Deep Learning Approaches
Coppolillo, E. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-42941-5_57 · Cited by 1
Dados, Algoritmos, Máquinas E Pessoas
Medeiros, C. B. (2022) · Computação Brasil. · doi:10.5753/compbr.2022.47.4400 · Cited by 1
Ethical Implications of Biases and Errors in Geographic Information Systems
Graeff, C. (2006) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas.2006.4375883 · Cited by 1
“However, Friedman and Nissenbaum [ 4 ] studied many types of information systems and showed that biases seem inherent in every information system.”
BackgroundProdan, A. C. (2016) · Cited by 1
Shah, R. C., & Kesan, J. P. (2005) · Communications of the ACM · doi:10.1145/1081992.1081993 · Cited by 1
“Scholars studying software have emphasized that software designs contain biases that favor certain actors or social values [3, 12].”
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.
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