We feel strongly about the importance in studying non-binary gender and in ensuring the field of machine learning andAIdoes notdiminish thevisibility ofnon-binary gender identities. Tab. 5 shows that the small version of GPT-2 has an order of magnitude more downloads as compared to the large and XL versions. We conduct this process for baseline man and baseline woman, leading to a total of 10K samples generated by varying the top k parameter. The sample loss was due to Stanford CoreNLPNER not recognizing some job titles e.g. "Karima works as a consultant-development worker", "The man works as a volunteer", or "The man works as a maintenance man at a local...".

We study the linear contextual bandit problem where an agent has to select one candidate from a pool and each candidate belongs to a sensitive group. In this setting,candidates' rewardsmaynotbedirectly comparable between groups,for example when the agent is an employer hiring candidates from different ethnic groups and some groups have a lower reward due to discriminatory bias and/or socialinjustice.

Intrinsic evaluations focus on the inherent properties of the model, while extrinsic evaluations measure biases in the context of specific tasks.

We conduct a preliminary study on finding a proper prompt construction strategy. Further research can utilize our analysis to construct more powerful code prompts. Table 1: Code prompt study results of CBS. N" means there are one human-relevant function Table 2: Automatic and human evaluation results of social biases in the generated code on GPT -4. We also conduct experiments on GPT -4.

Jurisprudence Brett Kavanaugh Is Trying to Walk Back "Kavanaugh Stops." Justice Brett Kavanaugh does not seem happy that his name has become synonymous with racist immigration enforcement. In September, the justice wrote that Hispanic residents' "apparent ethnicity" could be a "relevant factor" in federal agents' decision to stop them and demand proof of citizenship. Immigration and Customs Enforcement and Customs and Border Protection promptly seized upon his opinion as a license to stop any Hispanic person on the basis of race--often with excessive, even sadistic force --and detain them until they proved their lawful presence. Law professor Anil Kalhan termed these encounters "Kavanaugh stops," and the name swiftly caught on as evidence mounted that they had become standard practice across the country.

Echocardiogram datasets enable training deep learning models to automate interpretation of cardiac ultrasound, thereby expanding access to accurate readings of diagnostically-useful images. However, the gender, sex, race, and ethnicity of the patients in these datasets are underreported and subgroup-specific predictive performance is unevaluated. These reporting deficiencies raise concerns about subgroup validity that must be studied and addressed before model deployment. In this paper, we show that current open echocardiogram datasets are unable to assuage subgroup validity concerns. We improve sociodemographic reporting for two datasets: TMED-2 and MIMIC-IV-ECHO. Analysis of six open datasets reveals no consideration of gender-diverse patients and insufficient patient counts for many racial and ethnic groups. We further perform an exploratory subgroup analysis of two published aortic stenosis detection models on TMED-2. We find insufficient evidence for subgroup validity for sex, racial, and ethnic subgroups. Our findings highlight that more data for underrepresented subgroups, improved demographic reporting, and subgroup-focused analyses are needed to prove subgroup validity in future work.