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The Natural Cycles contraceptive app faced scrutiny after users reported unwanted pregnancies, leading to an investigation by the Swedish Medical Products Agency. The app, which uses an algorithm to predict fertility based on basal body temperature, was criticized for its effectiveness and the potential for user error. While the company maintains the app is effective when used correctly, reports of failures prompted concerns about its reliability as a primary method of birth control.
Some women using the contraceptive app, Natural Cycles, reported unwanted pregnancies, revealing its algorithm's difficulties in mapping menstrual cycles.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
AI systems that fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences, especially in critical applications or areas that require moral reasoning.
Also classified as
Users placed a high level of trust in the app, treating it with the same expectations as a contraceptive pill, which led to unsafe reliance in a critical health scenario.
The algorithm lacked the robustness to handle typical human variations, such as irregular menstrual cycles, stress, or minor temperature tracking errors, leading to incorrect fertility predictions.
AI system
Due to a decision or action made by an AI system
Unintentional
Due to an unexpected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed
The unwanted pregnancies were caused by the failure of the AI algorithm to robustly and accurately predict fertility windows under real-world conditions post-deployment.
Added categories peak at 2: Minor (Psychological Harm, Direct)
An algorithmic failure in the Natural Cycles contraceptive app led to unwanted pregnancies and subsequent regulatory scrutiny in Sweden. As a consumer health product issue, this incident carries negligible national security implications across all assessed categories.
The AI system acts as a health assistant by analyzing user-provided temperature data to offer recommendations on fertility windows, requiring human decision-making and action to achieve the goal of contraception.
High Risk: The system is a certified medical device used in healthcare for contraception, which has significant implications for physical health, safety, and fundamental rights.
The primary goal of the Natural Cycles algorithm is to act as a health assistant that tracks and analyzes basal body temperature to predict fertility windows and assist with contraception.
The reports provide detailed, consistent accounts of the app's functionality, the specific regulatory investigation in Sweden, and multiple personal testimonies of the resulting harms. The clinical efficacy statistics and the limitations of the technology are well-documented.
The incident could be interpreted primarily as a human behavioral failure (user error) rather than an AI failure, but the algorithm's lack of robustness under typical real-world conditions supports the AI safety classification.
Technology
Inaccurate BBT Premise
BBT raises marginally and up to 75 percent of women show no rise.
Algorithmic Cycle Mapping Failure
Algorithm fails to accurately predict ovulation for irregular cycles.
Unstable Predictions
Fertility predictions changed from green to red within the same day.
Data Inputs
Erratic Temperature Readings
Sleep disturbances, stress, and hangovers cause erratic temperature data.
Measurement Sensitivity
Slightest movement before measuring temperature skews the data inputs.
Missing Measurement Data
Users forgetting to measure daily or waking up at inconsistent times.
Human Factors
Unprotected Sex on Red Days
Users had unprotected sex on days the app designated as high-risk.
Over-reliance on App
Users trusted the app like a pill, neglecting behavioral requirements.
Unsuitable User Profiles
Users with irregular cycles or PCOS used the app despite high risks.
Process and Methods
Flawed Clinical Studies
Studies used self-selected existing users rather than randomized trials.
No Pre-Purchase Screening
No mandatory screening to block users who would be devastated by pregnancy.
Stressful Tracking Process
Demanding daily tracking routine caused high stress and anxiety.
Management
Misleading Marketing
Promoted app as as effective as the pill without behavioral context.
Growth Prioritized Over Safety
Used aggressive influencer marketing targeting young, unsuitable users.
Inadequate Risk Communication
Downplayed warnings that the app is not for those avoiding pregnancy.
Regulatory Environment
Premature Certification
EU and FDA approvals gave a medical device status to a behavioral method.
Lack of Marketing Oversight
Regulators allowed aggressive marketing without clear safety warnings.
Lack of Standardized Testing
Lack of medicalized control tests required for contraceptive certification.
AI system involved
Identified by automated extraction; can differ from the alleged developers above.
