Sam Anderson
Biographic Data
| ID | 6529822 |
|---|---|
| NAME | Sam Anderson |
| GIVEN NAMES | Sam |
| FAMILY NAME | Anderson |
| SIGNATURE | ANDERSON S |
| AFFILIATIONS | Medgar Evers College |
| ORCID | 0000-0001-6757-6846 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2003 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 1 |
Gender bias in generative artificial intelligence text-to-image depiction of medical students
Introduction: In Australia, 54.3% of medical students are women yet they remain under-represented in stereotypical perspectives of medicine. While potentially transformative, generative artificial intelligence (genAI) has the potential for errors, misrepresentations and bias. GenAI text-to-image production could reinforce gender biases making it important to evaluate DALL-E 3 (the text-to-image genAI supported through ChatGPT) representations of …
A century of variability of heatwave-driven streamflow in melt-driven basins and implications under climate change
Meltwater runoff from snow and glaciers in high mountain regions supports the water needs of hundreds of millions of people, but extreme events such as heatwaves modify the timing and magnitude of water available for downstream communities and ecosystems. The streamflow response to heatwaves depends strongly on heatwave timing and temperature, and the amount of snow and glacier ice available to melt. However, as ongoing climate change continues t…
Insights from Self-Organizing Maps for Predicting Accessibility Demand for Healthcare Infrastructure
As urban populations grow worldwide, it becomes increasingly important to critically analyse accessibility—the ease with which residents can reach key places or opportunities. The combination of ‘big data’ and advances in computational techniques such as machine learning (ML) could be a boon for urban accessibility studies, yet their application in this field remains limited. In this study, we provided detailed predictions of healthcare accessibi…
The reparations movement
The reparations movement
Insights from Self-Organizing Maps for Predicting Accessibility Demand for Healthcare Infrastructure
As urban populations grow worldwide, it becomes increasingly important to critically analyse accessibility—the ease with which residents can reach key places or opportunities. The combination of ‘big data’ and advances in computational techniques such as machine learning (ML) could be a boon for urban accessibility studies, yet their application in this field remains limited. In this study, we provided detailed predictions of healthcare accessibi…
Gender bias in generative artificial intelligence text-to-image depiction of medical students
Introduction: In Australia, 54.3% of medical students are women yet they remain under-represented in stereotypical perspectives of medicine. While potentially transformative, generative artificial intelligence (genAI) has the potential for errors, misrepresentations and bias. GenAI text-to-image production could reinforce gender biases making it important to evaluate DALL-E 3 (the text-to-image genAI supported through ChatGPT) representations of …
A century of variability of heatwave-driven streamflow in melt-driven basins and implications under climate change
Meltwater runoff from snow and glaciers in high mountain regions supports the water needs of hundreds of millions of people, but extreme events such as heatwaves modify the timing and magnitude of water available for downstream communities and ecosystems. The streamflow response to heatwaves depends strongly on heatwave timing and temperature, and the amount of snow and glacier ice available to melt. However, as ongoing climate change continues t…
Art (2 works) · Artificial Intelligence (2 works) · Computer Science (2 works) · Geography (2 works) · Medicine (2 works) · Aesthetics (1 work) · Artificial Intelligence (1 work) · Artificial Intelligence in Healthcare and Education (1 work) · Business (1 work) · Climate change (1 work)