Whose Sperm Is It Anyways In The Wild, Wild West Of The Fertility Industry?
Bibliographic record
Abstract
Imagine a couple that is unable to conceive a child naturally. Luckily, they had the money and resources available to them to conceive a child through assisted reproductive technology (ART), so they decided to start their family through the use of intrauterine insemination. They selected a sperm bank and began the arduous process of selecting a sperm donor who fit the desired traits and characteristics for their child. The sperm bank matched them with an anonymous donor, Donor 9623, and assured the couple that the donor was “a healthy male with an IQ of 160, a bachelor’s of science in neuroscience, a master’s degree in artificial intelligence, a Ph.D. in neuroscience engineering on the way, and no criminal history.”\nGiven this representation of Donor 9623 as the ideal candidate, the couple moved forward with the intrauterine insemination procedure and successfully conceived a child. After starting their new family, the couple accidentally found out the identity of Donor 9623, James Aggeles, and from a simple Internet search, they uncovered the shocking truth. Instead of their ideal neuroscientist donor, the father of their child was “a college dropout with a felony conviction and diagnosed schizophrenia.” This is Angela Collins and Margaret Hanson’s story. This is Jane Doe 1 and Jane Doe 2’s story. This is more than fifteen American, Canadian, and British families’ stories. Angela Collins and Margaret Hanson believed they had control over the process to start their family and the ability to select the man that would provide half of their child’s genetics.\nThe sperm bank, Xytex Corporation (Xytex), also reassured them that this was the case, but in June 2014, Angela and Margaret received the rude awakening that this was indeed not the truth. After finding out the true identity of their sperm donor, the women began notifying other families who also used the same anonymous sperm donor to conceive their children. Donor 9623 has allegedly fathered at least thirty-six children under his false identity through the purchase, promotion, and sale of his sperm by Xytex.\nThe purpose of this Note is to assess the need for Georgia to reevaluate prenatal torts due to advances in ART procedures, the increase in use of this technology, and the lack of sperm bank regulations, which have now resulted in the problem evidenced by the Xytex cases. Part I examines the history behind the creation of the three main prenatal torts: wrongful birth, wrongful life, and wrongful conception, followed by an examination of the current sperm bank regulations. Part II analyzes Georgia’s evaluation of prenatal torts, Georgia’s policy considerations when evaluating prenatal torts, Georgia’s interpretation of claims against sperm banks, and Georgia’s limited sperm bank regulations. Part III examines several proposals to remedy this issue, while accounting for Georgia’s policy concerns, to ensure the state is protecting its citizens’ rights and deterring sperm banks from engaging in fraudulent business practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".