Kronos Algorithmic Scheduling Blamed for Erratic Work Hours for Retail Workers
Kronos workforce management software is used by major retail chains to algorithmically generate employee schedules. The software has been criticised for causing erratic and unpredictable work hours, leading to financial instability and emotional distress for low-wage workers. Following an investigation by the New York attorney general and negative press, Kronos announced new features aimed at improving schedule stability and worker control, though critics argue the changes are optional and lack enforcement mechanisms.
- Company involved
- Various retailers (e.g., Gap, Target, J.C. Penney, Starbucks)
- AI system involved
- Kronos scheduling software
9 source articles · read the reporting →
California vaccine equity algorithm may exclude 2 million vulnerable residents
An ACLU of Northern California analysis alleges that California's Department of Public Health used an algorithm, built by Blue Shield, that allocates additional COVID-19 vaccine supply based on ZIP codes rather than census tracts. The analysis found that this approach could leave over 2 million people living in disadvantaged neighbourhoods, disproportionately communities of colour, without prioritised access to the vaccine. The ACLU called on the state to review the affected areas and explain how it will ensure these residents are not left behind.
- Company involved
- California Department of Public Health
- AI system involved
- Healthy Places Index
1 source article · read the reporting →
RealPage's YieldStar algorithm pushes rents higher for tenants
RealPage's YieldStar algorithm uses private competitor data to recommend rent increases for apartment units. Landlords adopt up to 90% of the suggestions, leading to higher rents for tenants. Critics allege the software may facilitate indirect price-fixing in violation of antitrust law. The company denies wrongdoing and says the software helps eliminate collusion.
- Company involved
- RealPage
- AI system involved
- YieldStar
10 source articles · read the reporting →
France: CNAF's discriminatory risk-scoring algorithm must be stopped
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.
- Company involved
- CNAF
2 source articles · read the reporting →
UDR Sued Over Alleged Use of RealPage Algorithm to Set Rents in San Diego
A class action lawsuit alleges that UDR, Inc. used RealPage's YieldStar algorithmic pricing software to set rental rates and occupancy levels for its San Diego properties, in violation of a local ordinance. The suit claims the software relied on nonpublic competitor data, contributing to inflated rents and making housing less affordable. The lawsuit, filed in July 2026, seeks to represent all affected tenants. UDR has previously acknowledged using YieldStar as one tool among others in its decision-making.
- Company involved
- UDR, Inc.
- AI system involved
- RealPage YieldStar
1 source article · read the reporting →
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 →
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 →
Cense exposed 2.5 million records of auto accident victims online
On July 7, 2020, a security researcher discovered 2.5 million records containing personal and medical data of auto accident victims exposed online. The records, belonging to New York-based AI company Cense, included names, insurance policy numbers, claim numbers, and medical diagnosis notes. The data was labeled as staging data, possibly intended for temporary storage before being loaded into an AI system. After the researcher sent a responsible disclosure notice, Cense restricted public access to the database.
- Company involved
- Cense
- AI system involved
- Cense
5 source articles · read the reporting →
Anthropic's Claude AI loses $1,000 running a vending machine experiment
In a test by Anthropic and The Wall Street Journal, an AI agent named Claudius Sennet was given control of an office vending machine. Despite initial instructions to generate profit, the AI was manipulated by journalists into setting all prices to zero and ordering items like a PlayStation 5 and a live fish. The experiment ended after three weeks with a $1,000 loss. Anthropic's red team head called it 'enormous progress'.
- Company involved
- Anthropic
- AI system involved
- Claude (Claudius Sennet agent)
5 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 →
Shipt's new V2 algorithm reduces pay for 41% of workers, study finds
A study by MIT Media Lab and Coworker.org found that Shipt's new V2 payment algorithm reduced pay for 41% of workers by an average of 11% per shop. The algorithm, rolled out in September 2020, replaced a transparent payment system with a black-box model. Workers pooled data to analyze the impact, revealing uneven distribution of earnings.
- Company involved
- Shipt
- AI system involved
- V2
10 source articles · read the reporting →
Titus Henderson files civil rights lawsuit against COMPAS
Titus Henderson, a prisoner, filed a civil rights lawsuit against COMPAS and other defendants in the U.S. District Court for the Western District of Wisconsin on July 16, 2018. The lawsuit alleges discrimination related to the use of the COMPAS risk assessment tool. The case is ongoing.
- AI system involved
- COMPAS
6 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 →
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 →
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 →
Airbnb smart-pricing algorithm increased racial revenue gap, study finds
A study by Carnegie Mellon University found that Airbnb's smart-pricing algorithm increased the revenue gap between White and Black hosts, even though it narrowed the gap among hosts who adopted it. The algorithm, which sets daily prices automatically, was adopted by fewer Black hosts, so its suggested prices were closer to the optimum for White hosts. The researchers said the tool could reduce racial disparities only if more Black hosts adopted it.
- Company involved
- Airbnb
- AI system involved
- Airbnb smart-pricing algorithm
10 source articles · read the reporting →
Hospitals use Epic AI to predict Covid-19 decline without validation
Dozens of hospitals across the US are using Epic's deterioration index AI system to predict which Covid-19 patients will become critically ill, despite the tool not being validated for the new disease. The rapid deployment during the pandemic bypassed normal testing and validation processes.
- AI system involved
- Deterioration index
9 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 →
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 →
ChatGPT water consumption study warns of environmental impact
A study from the University of Colorado Riverside and the University of Texas Arlington estimates that OpenAI's ChatGPT consumes a 500ml bottle of fresh water for every 20 to 50 questions. The water is used to cool data center servers. The study urges companies to address their water footprint.
- Company involved
- OpenAI
- AI system involved
- ChatGPT
10 source articles · read the reporting →
ООО «Точка опоры» v. ООО «ЦСС» (West Siberian District AC): AI-hallucinated content in court filing, Monetary Fine
The AI system generated fake legal citations that were submitted to the court, resulting in a fine for contempt.
- Company involved
- ООО «ЦСС»
1 source article · read the reporting →
Purdue study finds ChatGPT wrong over half the time on software questions
A study by Purdue University found that ChatGPT provided incorrect answers to over half of 517 software development questions from Stack Overflow. Despite the errors, 34% of users preferred ChatGPT's answers over human responses. The study warns that relying on ChatGPT for coding could jeopardize programmers' professional reputations.
- Company involved
- OpenAI
- AI system involved
- ChatGPT
9 source articles · read the reporting →
ETH Zurich study shows LLMs can infer Reddit users' personal data
Researchers at ETH Zurich conducted a study where nine large language models, including GPT-4, analysed Reddit users' posts and inferred personal attributes such as age, location, gender, and income with up to 85% accuracy. The study randomly selected 520 users and found that GPT-4 was most accurate, while LlaMA-2-7b was least. The researchers warn that people unknowingly reveal personal information online that LLMs can exploit.
- Company involved
- ETH Zurich
- AI system involved
- GPT-4, LlaMA-2-7b
4 source articles · read the reporting →
