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 →
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 →
SEC warns public of deep fake investment scams featuring Lance Gokongwei
The Securities and Exchange Commission (SEC) warned the public that scammers are using deep fake videos and audio of Lance Gokongwei to endorse fraudulent investment schemes. The manipulated media circulate on social media, deceiving people into investing in a platform registered in Cyprus. Victims are asked to provide credit card details and OTPs, then lose contact when attempting to withdraw funds. The SEC advises the public to verify investment offers with the agency.
2 source articles · read the reporting →
Hangzhou plans permanent health codes with scoring, sparking privacy backlash
Hangzhou health authorities announced plans to launch a permanent health-tracking QR code system that would assign residents a color and numerical score based on medical records and lifestyle choices. The proposal, an expansion of the existing COVID-19 health code system, has sparked online backlash over privacy concerns and fears of discrimination. Critics argue that the system could lead to public exposure of personal health data and potential misuse by employers. The authorities aim to complete the project by May or June 2020.
- Company involved
- Hangzhou Health Authorities
- AI system involved
- Health Code
10 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 →
Qoves facial assessment tool ranks journalist's attractiveness and suggests procedures
Tate Ryan-Mosley, a journalist at MIT Technology Review, used Qoves Studio's AI facial assessment tool, which automatically scored her face and listed perceived flaws such as smile lines and under-eye bags. The tool recommended skin-care products and paid surgical consultation reports. She also tried Megvii's Face++ beauty-scoring system, which gave her two percentage attractiveness scores. The article raises concerns that such algorithms are inaccurate, biased and not transparently used by social media platforms.
- Company involved
- Qoves Studio
- AI system involved
- Facial Assessment Tool
9 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 →
Facebook Allowed Age-Targeted Credit Card Ads Violating Its Policy
The Markup found that four companies—Aspiration, Hometap, Chime, and Varo Bank—ran Facebook ads for credit cards and home equity loans that were targeted by age, excluding users under 25 or 35. This practice violates Facebook's own anti-discrimination policy and may violate the Equal Credit Opportunity Act and California's Unruh Civil Rights Act. Facebook did not respond to requests for comment, and some advertisers said they would review their ad targeting.
- Company involved
- Facebook
- AI system involved
- Facebook Ad Platform
9 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 →
Natural England biodiversity metric undervalues rewilding habitats
Natural England published a new biodiversity net gain metric in July 2021 that uses an algorithm to score habitat value. Ecologists and conservationists warned that the metric misclassifies scrubby and ruderal habitats as degraded, potentially undervaluing rewilding projects like Knepp estate and leading to inadequate compensation for habitat loss. The article claims the metric could blight hundreds of thousands of sites and penalise rewilding-style management.
- Company involved
- Natural England
- AI system involved
- Biodiversity Net Gain (BNG) metric
10 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 →
ScaleFactor reportedly failed to deliver promised automated bookkeeping software
ScaleFactor, an Austin-based startup, is reported to have failed to deliver the automated, real-time bookkeeping tools it promised customers, instead relying on human bookkeepers and a Filipino contract accounting firm. The company told Forbes in June that it was shutting down, initially blaming the pandemic, but Forbes later reported that its problems predated Covid-19. Investors reportedly came to see the company as more of a services business than a software platform, and pulled funding after a pivot to a marketplace model. No legal or regulatory action is reported.
- Company involved
- ScaleFactor
10 source articles · read the reporting →
Southland Homes & Real Estate and Investment, LLC v. Lam (SC California): AI-hallucinated content in court filing, Monetary Sanctions; Bar…
The AI system generated legal briefs containing fabricated case citations, which were filed in court, affecting the court and the opposing party.
1 source article · 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 →
Four commercial large language models perpetuate race-based medical misconceptions
A study published in npj Digital Medicine tested four commercial large language models (Bard, ChatGPT, GPT-4, and Claude) for their tendency to propagate discredited race-based medical beliefs. When asked about kidney function, lung capacity, and skin thickness, the models sometimes endorsed debunked racial differences, particularly affecting Black patients. The study concludes that these biases pose a potential hazard and urges caution before using such models in clinical decision-making.
- Company involved
- Not named in article (refers to commercial LLMs generically as Google's Bard, OpenAI's ChatGPT and GPT-4, and Anthropic's Claude)
- AI system involved
- Bard, ChatGPT, GPT-4, Claude
6 source articles · read the reporting →
Cigna StressWaves Test found unreliable and invalid in independent study
A study published in Scientific Reports evaluated the Cigna StressWaves Test (CSWT), an AI tool that claims to assess psychological stress from speech. The study found that the CSWT had poor test-retest reliability and poor validity compared to the Perceived Stress Scale. The authors warned that widespread availability of the tool could lead to misleading results and negative consequences for users making healthcare decisions. Cigna has not publicly responded to the findings.
- Company involved
- Cigna
- AI system involved
- Cigna StressWaves Test
4 source articles · read the reporting →
Study finds LLMs used in up to 16.9% of AI conference peer reviews
According to a new paper on arXiv, researchers have begun using generative AI services to help write peer reviews of machine learning papers submitted to leading AI conferences. The study analysed reviews from ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023 and estimated that between 6.5% and 16.9% of review text may have been substantially modified by large language models. The authors argue that this risks depriving authors of diverse expert feedback and may skew reviews towards AI model biases. They have called for greater transparency about the use of LLMs in peer review.
9 source articles · read the reporting →
