Incidents gathered from public reporting around the world. Each one links to the articles it came from. None of it is a finding that anyone broke the law.
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44 incidents closest to “SAS Credit Scoring” · matched on meaning · public reporting
The AI platform screened job applicants, affecting their hiring prospects.
1 source article · read the reporting →
A study by Dartmouth College researchers found that the COMPAS recidivism risk assessment algorithm, used by judges in several US states, is no more accurate than random people recruited online. The algorithm, developed by Equivalent, exhibited racial bias by classifying black defendants as higher risk and white defendants as lower risk, leading to harsher rehabilitation recommendations for black defendants. The bias likely stems from skewed arrest rate data. The findings build on a 2016 ProPublica investigation.
10 source articles · read the reporting →
AI screening system determined which job applicants to advance to the next stage of recruitment.
1 source article · read the reporting →
Mary Louis and Monica Douglas filed a class action lawsuit in 2022 alleging that SafeRent Solutions' SafeRent Score product discriminated against rental applicants in Massachusetts who held public housing vouchers, violating fair housing and consumer protection laws. SafeRent denied wrongdoing. The court approved a settlement in November 2024, and payments were distributed to eligible class members in 2025 and 2026.
5 source articles · read the reporting →
Верховный апелляционный суд ЮАР рассмотрел дело об алгоритмах Sassa - UA.NEWS
The Supreme Court of Appeal heard an appeal on 25 August about Sassa's digital application system for the SRD grant. The system only accepts online applications and uses automated bank account checks that may deny grants to people whose accounts receive deposits that are not regular income. The Global Center on AI Governance submitted that automated decisions must uphold constitutional rights and be fair, non-discriminatory, and suited to South Africa's informal economy.
1 source article · read the reporting →
Tenant screening companies assign renters a score drawn from data far wider than credit history, an industry subject to less regulation than credit scoring agencies. ProPublica reports that experts warn these algorithms can decide who gets an apartment on the basis of information applicants cannot see or correct. Kim Fuller was denied a rental application on such a score.
1 source article · read the reporting →
In the late 1970s and 1980s, St George's Hospital Medical School used a computer algorithm to automate the first round of its admissions process, scoring applicants based on historical data. The model was found to discriminate against female applicants and those with non-European-looking names, denying them interviews. The bias was discovered by two professors, and the university co-operated with a Commission for Racial Equality inquiry, subsequently contacting affected applicants and offering some places.
4 source articles · read the reporting →
In late 2019, David Heinemeier Hansson alleged on Twitter that the Apple Card underwriting algorithm, operated by Goldman Sachs, gave him a higher credit limit than his wife despite similar financial profiles. The New York Department of Financial Services investigated and found no violation of fair lending laws, but critics argue the audit methodology was outdated and failed to detect proxy discrimination. Apple later updated its credit policy to allow spouses to combine credit files, acknowledging a lack of fairness in industry credit scoring.
6 source articles · read the reporting →
The Chicago Police Department has used the Strategic Subject List since 2012, an algorithm that assigns risk scores to individuals based on arrest records. The list was kept secret until a court forced its release, revealing that 56% of black men aged 20-29 have a score, and only 3.6% of those on the list were involved in violence. Despite claims it is not used for enforcement, documents show it influences police deployment and arrests, leading to allegations of racial bias and lack of transparency.
9 source articles · read the reporting →
The city of Rotterdam deployed a machine learning algorithm built by Accenture to flag welfare recipients for fraud investigation. The system used personal data including gender, language, and subjective caseworker notes to generate risk scores, leading to investigations that disproportionately targeted women and migrants. An external review found the algorithm discriminatory and inaccurate, prompting the city to suspend its use in 2021. The system's opacity made it nearly impossible for those flagged to challenge the decisions.
6 source articles · read the reporting →
An investigation by SPIEGEL and BR Data reveals that Schufa's credit scoring algorithm often assigns poor risk scores to consumers with only positive credit information. One consumer, Sven Drewert, was denied a credit card limit increase despite having no negative entries. The algorithm uses limited data, and its secret formula can lead to arbitrary categorisations, affecting access to loans, phone contracts, and housing. The system's opacity and potential biases raise concerns about fairness and accountability.
2 source articles · read the reporting →
A Stanford study found that the IRS's secret audit selection algorithm causes Black taxpayers to be audited at 2.9 to 4.7 times the rate of non-Black taxpayers. The disparity is driven by the algorithm's focus on the likelihood of underreporting rather than the magnitude, and on refundable tax credits like the Earned Income Tax Credit. Single Black men claiming dependents and the EITC are nearly 20 times more likely to be audited than non-Black married couples claiming the same credit. The researchers suggest the IRS could modify its algorithm to reduce racial disparities without sacrificing revenue.
4 source articles · read the reporting →
The Portland Water Bureau used a machine learning system in a randomised control trial to identify customers for its financial assistance programme. The system selected Columbia Sportswear CEO Tim Boyle, a billionaire and one of the city's largest residential water consumers, for a 40% water bill discount. Boyle declined the discount, stating it should go to someone who needs it. The bureau is testing whether the SERVUS algorithm can accurately identify customers' ability to pay.
1 source article · read the reporting →
The Markup found that several universities, including UMass Amherst, UW–Milwaukee, and Texas A&M, use EAB's Navigate software which incorporates race as a predictor of student dropout risk. The algorithms label Black and Latinx students as high risk at disproportionately high rates compared to White students. Students, professors, and experts worry this is pushing minority students out of math and science majors through biased advising.
1 source article · read the reporting →
Houston Independent School District used the SAS Institute's EVAAS system, a proprietary algorithm, to evaluate teacher performance based on student test scores. The Houston Federation of Teachers and several teachers sued in 2014, alleging the system violated their Fourteenth Amendment procedural due process rights because the algorithm is a trade secret and teachers cannot verify the accuracy of their scores. A federal magistrate judge refused to dismiss the procedural due process claims in May 2017, ruling that teachers must have an opportunity to test the accuracy of the scores that could cost them their jobs.
1 source article · read the reporting →
A study examined the SAS Education Value-Added Assessment System (EVAAS®) used by the Southwest School District (SSD) to evaluate teacher effectiveness for high-stakes consequences. Teachers reported that the system produced inconsistent results, was biased by student factors, and reduced morale and collaboration. The study found that teachers did not use the data formatively as intended, and unintended consequences included heightened pressure and teaching to the test.
10 source articles · read the reporting →
Durham Constabulary developed the Harm Assessment Risk Tool (HART), a machine learning algorithm that assesses the recidivism risk of offenders. The tool uses 34 data categories including criminal history, age, gender and two types of postcode, one sourced from Experian's Mosaic marketing segmentation system. Big Brother Watch alleges that using such commercial consumer behaviour data to inform custody decisions risks prejudice and disproportionate targeting of deprived neighbourhoods. The force has stated it is refreshing the model with an aim to remove one of the postcode predictors.
9 source articles · read the reporting →
A study of mortgage lending from 2012-2018 found that Latinx and African-American borrowers were charged higher interest rates than white borrowers with similar credit risk. FinTech algorithms reduced the disparity by 40% but did not eliminate it. The discrimination costs minority borrowers an estimated $765 million per year in extra interest.
10 source articles · read the reporting →
Amnesty International and coalition partners filed a complaint with the Council of State against CNAF's risk-scoring algorithm used to detect benefit overpayments. The algorithm assigns risk scores based on criteria that discriminate against vulnerable groups, including those with disabilities, single parents, and low-income households. The complaint alleges the system violates human rights to equality and privacy. The EU AI Act's social scoring ban may apply, but its definition remains unclear.
2 source articles · read the reporting →
The Austrian Public Employment Service (AMS) uses an algorithm to classify job seekers into categories A, B, and C, determining their access to benefits and training. Scientists from TU Wien, WU Wien, and University of Vienna have criticised the algorithm for lacking transparency, as only two of 96 model variants have been published. They allege that the system may discriminate against women and people with migration background, and that job seekers are not informed about how the algorithm works or given a chance to appeal.
10 source articles · read the reporting →
The Educational Testing Service's E-rater algorithm, used to grade essays on the GRE and other standardized tests, has been found to systematically give higher scores to students from mainland China and lower scores to African American students compared to human graders. The bias stems from the algorithm's reliance on surface-level metrics like vocabulary and sentence length, which disadvantage certain groups. Despite studies dating back to 1999, the bias persists, and in many states, only a small percentage of essays are reviewed by humans.
10 source articles · read the reporting →
Goldman Sachs used four machine learning models to predict outcomes of the 2018 FIFA World Cup. The bank's predictions were wrong, with none of the predicted semifinalists or champion materialising. Goldman Sachs updated its models halfway through the tournament, but the revised predictions also proved incorrect. The article is an account of the failed predictions, not an allegation of harm.
7 source articles · read the reporting →
The Austrian Federal Administrative Court overturned a ban by the Data Protection Authority on the AMS algorithm, which predicts job chances of unemployed people. The system, trained on historical data including age, gender, and nationality, categorises individuals as high, medium or low chance of re-employment. Critics argue it discriminates against women and mothers, and that the data is now outdated due to the COVID-19 pandemic. The court ruled that the algorithm is lawful as long as a human advisor makes the final decision on training support.
5 source articles · read the reporting →
The Scottish Qualifications Authority (SQA) used an algorithm to moderate 2020 exam results after COVID-19 cancelled exams. The algorithm disproportionately downgraded students from disadvantaged backgrounds, leading to a public outcry and a government U-turn. An independent report criticised the SQA and Scottish Government for failing to properly assess the equality impacts and for not making the algorithm available for analysis. The SQA has expressed no regret over the moderation approach.
10 source articles · read the reporting →
