AMS algorithm lacks transparency and may discriminate against job seekers
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.
- Company involved
- AMS (Arbeitsmarktservice Österreich)
- AI system involved
- AMS-Algorithmus
10 source articles · read the reporting →
Educational Testing Service's E-rater algorithm biases essay scores against minority students
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.
- Company involved
- Educational Testing Service
- AI system involved
- E-rater
10 source articles · read the reporting →
Goldman Sachs FIFA World Cup predictions were inaccurate
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.
- Company involved
- Goldman Sachs
- AI system involved
- Four machine learning models
7 source articles · read the reporting →
Epic sepsis prediction algorithm misses two-thirds of cases, study finds
A study of nearly 30,000 patients at University of Michigan hospitals found that Epic Systems' sepsis prediction algorithm missed two-thirds of sepsis cases and frequently issued false alarms. The algorithm, used by over 100 US health systems, is designed to alert staff to potential sepsis, but the study suggests it provides little value and may contribute to alert fatigue. Epic disputed the study's conclusions, claiming its system has helped save lives.
- Company involved
- Epic Systems
- AI system involved
- Epic sepsis prediction model
6 source articles · read the reporting →
Austrian court lifts ban on AMS job-chance prediction algorithm
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.
- Company involved
- Arbeitsmarktservice (AMS)
- AI system involved
- AMS-Algorithmus
5 source articles · read the reporting →
UK councils use Covid OneView AI to harvest personal data for risk scoring
UK local authorities are using a system called Covid OneView, developed by data analytics firm Xantura, to harvest millions of personal details from council records. The system uses predictive analytics and AI to assign risk scores to households and individuals, aiming to identify those vulnerable to Covid or likely to break lockdown rules. Privacy campaigners and MPs have criticised the lack of transparency and the extent of data collection, which includes sensitive information such as debt levels, living arrangements, and even notes on unfaithful sex. Xantura and Barking and Dagenham Council have defended the system as compliant with data protection rules and focused on providing support.
- Company involved
- UK local authorities
- AI system involved
- Covid OneView
6 source articles · read the reporting →
Suzhou clarifies civility scoring system after public backlash
The Suzhou municipal government announced a civility code system that assigns scores to residents based on their behavior, including traffic violations and volunteer work. The system, currently in a trial phase, aims to generate a personal portrait for each resident. Following public backlash and concerns about privacy and potential abuse, an official stated that participation will not be mandatory and that a bad score will not affect citizens. The system is still under development.
- Company involved
- Suzhou municipal government
- AI system involved
- Civility code
10 source articles · read the reporting →
SQA exam algorithm disproportionately downgraded poorer pupils, report finds
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.
- Company involved
- Scottish Qualifications Authority (SQA)
10 source articles · read the reporting →
Allstate's proposed retention model charged big spenders more in Maryland
Allstate proposed an auto insurance rate adjustment plan in Maryland using a retention model algorithm that varied premium changes based on existing premiums. The algorithm would have increased rates up to 20% for customers already paying the highest premiums while capping increases at 5% for others, and would have denied meaningful discounts to thousands owed reductions. The Maryland Insurance Administration rejected the plan as discriminatory, but Allstate claims it was withdrawn and has continued proposing similar models elsewhere. The proposal never took effect in Maryland.
- Company involved
- Allstate Corporation
- AI system involved
- retention model
9 source articles · read the reporting →
Study finds Italian car insurers charge more based on birthplace
A study by the Universities of Padua, Udine and Carnegie Mellon found that Italian car insurers, including Genertel, Mps, Quixa and Con.Te, use birthplace and citizenship in pricing algorithms, charging some drivers over €1,000 more. The practice was ruled against in a 2018 decree involving Linear, but the study says it continues. The companies contacted denied or explained the findings.
- Company involved
- Genertel, Mps, Quixa, Con.Te
8 source articles · read the reporting →
LA's VI-SPDAT housing scoring system gives lower priority to Black and Latino people
The Los Angeles Homeless Services Authority uses the VI-SPDAT scoring system to prioritise unhoused people for subsidised permanent housing. An investigation by The Markup found that Black and Latino people consistently receive lower vulnerability scores than White people, leading to lower priority for housing. The agency has acknowledged the racial disparities and is working on a new tool, but continues to use the current system.
- Company involved
- Los Angeles Homeless Services Authority
- AI system involved
- VI-SPDAT
10 source articles · read the reporting →
Wisconsin’s dropout prediction algorithm labels students high risk with racial bias
Wisconsin’s Dropout Early Warning System (DEWS) uses machine learning to predict middle school students' likelihood of graduating on time, relying on factors including race and income. The Markup found the algorithm frequently mislabels students, with a higher false alarm rate for Black and Hispanic students. The Wisconsin Department of Public Instruction has not informed schools of the racial disparities and continues to use the system.
- Company involved
- Wisconsin Department of Public Instruction
- AI system involved
- Dropout Early Warning System (DEWS)
10 source articles · read the reporting →
Nevada's AI grad score model cuts funding for low-income students
Nevada replaced its income-based school funding formula with a machine learning model developed by Infinite Campus that generates a 'grad score' for each student. The model reduced the number of students eligible for supplemental funding from 288,000 to 63,000, disproportionately affecting low-income students. Critics argue the model lacks transparency and de-emphasizes economic status, potentially leaving many high-poverty schools underfunded.
- Company involved
- Nevada Department of Education
- AI system involved
- Grad score model
7 source articles · read the reporting →
DeepScore markets facial and voice analysis app for trustworthiness scoring despite experts' doubts
DeepScore, a Tokyo-based company, is marketing an app that uses facial and voice recognition to score people's trustworthiness for lenders and insurers in Japan, Indonesia, Vietnam and the Philippines. The company says the app can detect deception with 70 per cent accuracy, but researchers and privacy advocates say there is no reliable scientific basis for such judgments and warn of discrimination and privacy harms. The chief executive said the system is only one part of lenders' and insurers' decision-making and that people can choose not to use it. Critics respond that an unequal balance of power makes consent difficult.
- Company involved
- DeepScore
- AI system involved
- DeepScore
6 source articles · read the reporting →
Retorio AI personality test swayed by candidate appearance in BR experiment
Bayerischer Rundfunk journalists conducted experiments with Retorio's AI video interview analysis tool. The AI, which assesses personality traits from short videos, produced different scores when the same actress changed her appearance (glasses, headscarf, wig) or the video background and lighting were altered. The start-up Retorio acknowledged that the AI considers external image, similar to a human interviewer. Experts warned that such software could perpetuate stereotypes and unfairly affect job candidates.
- AI system involved
- Retorio AI
10 source articles · read the reporting →
Wisconsin court used secret COMPAS algorithm to sentence Loomis to harsher term
In Loomis v. Wisconsin, a judge used a COMPAS risk score from Northpointe to sentence a defendant to a harsher punishment. The algorithm was kept secret as a trade secret, preventing the defendant from challenging its accuracy. The Wisconsin Supreme Court upheld the sentence, ruling that the score was only one part of the rationale. The case raises concerns about due process and the use of secret algorithms in criminal sentencing.
- Company involved
- State of Wisconsin
- AI system involved
- COMPAS
10 source articles · read the reporting →
USPS algorithm RRECS causes pay cuts for two-thirds of rural mail carriers
The United States Postal Service (USPS) implemented a new algorithm, RRECS, to evaluate rural carrier routes and determine pay. Due to flaws in how carriers scanned packages, the algorithm underestimated route times, resulting in pay cuts for 66 per cent of rural carriers, some losing thousands of dollars annually. Carriers report that they were not adequately trained on the system and that the cuts are scheduled to take effect, though they have been postponed multiple times. The USPS stated that the system is the result of a nationally negotiated agreement.
- Company involved
- United States Postal Service
- AI system involved
- RRECS
9 source articles · read the reporting →
University of Chicago report finds US healthcare algorithms rife with bias
A University of Chicago report alleges that algorithms used by hospitals, insurers and other businesses across the US are biased along racial and economic lines. The algorithms help triage patients, predict diabetes and flag those needing extra care, but the report says flawed products were introduced with little oversight. It says inequitable treatment has continued for more than a decade.
7 source articles · read the reporting →
Study finds credit score algorithms less accurate for minorities
A study of 50 million US consumers found that credit scoring algorithms used by mortgage lenders are less accurate for minority and low-income applicants due to sparse credit data, leading to higher rejection rates. The inaccuracy is due to noise in the data, not bias, so fairer algorithms cannot fix it. The study suggests that adjusting for bias had no effect, and that addressing the inaccuracy could reduce disparities by 50%.
- Company involved
- Mortgage lenders (unnamed)
- AI system involved
- Credit scoring algorithms
6 source articles · read the reporting →
Danish law enables algorithm to profile unemployed for long-term risk
The Danish parliament passed a law in April 2019 that allows an algorithm to analyse personal data of all unemployed individuals to identify those at risk of long-term unemployment. The system uses data on ethnicity, age, health, and education history to produce a risk score for social workers. Critics, including legal experts and unemployment insurance funds, argue the profiling is stigmatising, lacks transparency, and may violate GDPR. The Danish Data Protection Authority has reopened its review of the law.
- Company involved
- Danish Ministry of Employment
10 source articles · read the reporting →
LoanDepot algorithm denied mortgage to Black couple in Charlotte
In August 2019, Crystal Marie and Eskias McDaniels, a Black couple, were denied a mortgage for a house in Charlotte, North Carolina, by loanDepot's automated underwriting algorithm. The algorithm rejected the application because Crystal Marie was a contractor, not a full-time employee, despite her high credit score and income. After the couple enlisted their real estate agent and employer to intervene, the lender reversed the decision and cleared them to close. The couple alleged that race played a role in the denial, which loanDepot denied.
- Company involved
- loanDepot
- AI system involved
- Classic FICO and Fannie Mae/Freddie Mac automated underwriting software
10 source articles · read the reporting →
Spanish Supreme Court orders release of BOSCO algorithm code for social electricity bonus
The Spanish NGO Civio won a Supreme Court case forcing the government to release the source code of BOSCO, the algorithm that decides eligibility for the social electricity bonus (bono social eléctrico). Civio had demonstrated in 2019 that BOSCO contained serious errors that denied the benefit to vulnerable people who met the requirements. The government had refused to disclose the code, citing intellectual property. The Supreme Court ruled that transparency must prevail, setting a precedent for public access to automated decision-making systems.
- Company involved
- Ministerio para la Transición Ecológica (Gobierno de España)
- AI system involved
- BOSCO
10 source articles · read the reporting →
Swedish welfare agency's AI system flags marginalized groups for fraud investigations
Försäkringskassan, Sweden's Social Insurance Agency, uses an AI risk-scoring system to flag welfare applicants for fraud investigations. The system disproportionately targets women, individuals with foreign backgrounds, low-income earners, and those without university degrees, according to an investigation by Lighthouse Reports and Svenska Dagbladet. Amnesty International has called for the system to be discontinued, citing violations of the right to equality and non-discrimination.
- Company involved
- Försäkringskassan (Swedish Social Insurance Agency)
6 source articles · read the reporting →
Dutch Ministry of Foreign Affairs used secret algorithm to score visa applicants based on nationality
The Dutch Ministry of Foreign Affairs used a secret algorithm called IOB to score short-stay visa applicants based on nationality, gender, and age. Applicants flagged as high risk were automatically moved to an intensive track with higher rejection rates and delays. The ministry's own data protection officer warned of potential ethnic discrimination, but the system continued to be used. The algorithm has profiled millions of applicants since 2015.
- Company involved
- Ministry of Foreign Affairs
- AI system involved
- IOB
10 source articles · read the reporting →
