Fact check: Chances of having boy or girl

1. Why these papers talk about predicting gender, not biological odds

Both items are studies of classification methods: one describes a real-time Convolutional Neural Network (CNN) system trained to predict perceived gender from visual inputs, while the other reviews approaches to infer age and gender of Twitter users from text and metadata ^{[1] [2]}. Each frames “gender” as a label assigned by algorithms based on observable signals, not as a biological outcome like sex at birth. The documents emphasize model performance metrics and dataset annotations, which address how accurately algorithms can label existing images or accounts, rather than the epidemiology or genetics underlying whether a pregnancy results in a male or female child ^{[1] [2]}. This framing creates a category mismatch between the user’s question about chances and the papers’ focus on prediction.

2. What the CNN paper claims and what it leaves out

The CNN-focused study reports high classification accuracy for real-time gender prediction from visual data and highlights engineering aspects such as model architecture and latency improvements for practical deployment ^[1]. However, the analysis explicitly does not translate those accuracy figures into population probabilities or provide evidence about biological sex ratios; it also does not address the ethical or scientific pitfalls of conflating perceived gender with chromosomal sex or the statistical question of likelihoods of male versus female births ^[1]. The paper’s performance claims therefore cannot be used to infer the underlying chance of having a boy or a girl.

3. What the Twitter scoping review reveals about demographic inference

The scoping review catalogues methods and annotated datasets used to predict age and gender on Twitter and stresses the limitations and biases inherent in socialmedia-based inference—dataset representativeness, annotation errors, and cultural variability in how gender is signaled ^[2]. These methodological caveats illustrate why social-media or image-based predictions are ill-suited to answer questions about biological probabilities: models learn patterns tied to expression, language, and presentation, not the biological determinants of fetal sex or population birth ratios. The review therefore undercuts any suggestion that aggregate algorithmic labels reflect true chances at conception or birth ^[2].

4. How classification accuracy differs from population likelihoods

High algorithmic accuracy in labeling does not imply information about sex-determining biology; accuracy measures how often an algorithm assigns the same label as human annotators or ground-truth tags in a dataset, not the base-rate probability of male versus female births. The studies’ metrics—precision, recall, overall accuracy—are conditional on dataset composition and annotation standards, and they omit crucial epidemiological factors such as birth sex ratios, selective practices, or parental and environmental influences. Therefore the documents cannot provide a reliable estimate of the chance of having a boy or girl because they do not model nor report on population-level outcomes ^{[1] [2]}.

5. Practical consequences and common misinterpretations to avoid

A common misunderstanding is to equate an algorithm’s ability to predict perceived gender with knowledge about biological sex probabilities; the papers warn against this by documenting annotation noise and demographic bias in training data ^{[1] [2]}. Using these prediction systems as proxies for sex-at-birth would conflate social presentation with chromosomal or fetal outcomes and could perpetuate biased inferences. Thus any claim about “chances” based on these studies would be misapplied, since the original research questions, methods, and datasets do not align with reproductive epidemiology or genetics ^{[1] [2]}.

6. What the studies imply researchers and readers should do next

Both analyses imply a need for clearer definitions, better datasets, and transparency about limitations when inferring demographics from digital traces or images; they recommend caution in extrapolating algorithmic labels to broader populations ^{[1] [2]}. For those seeking authoritative answers about the probability of a boy versus a girl, the packet’s materials point investigators to different literature—epidemiology, obstetrics, and genetics—rather than machine learning publications. Readers should therefore consult population health and biological sources for the original “chances” question, and treat these AI studies as contributions to classification science, not to reproductive probability estimates ^{[1] [2]}.