Target removes product misidentifying black leaders during Black History Month
American History Teacher Discovers Inaccuracies in Target’s Civil Rights Activity Book
A Nevada American history teacher recently purchased a magnetic activity book from retail giant Target to celebrate Black History Month. However, upon closer inspection, she found multiple inaccuracies that she later highlighted in a viral TikTok video.
Tierra Espy, a high school teacher from Las Vegas, bought the Civil Rights Magnetic Learning Activity, which included a tin case with 26 magnets and informational cards featuring illustrations of black leaders. Unfortunately, the activity book contained incorrect names and images of Carter G. Woodson, W.E.B. Du Bois, and Booker T. Washington.
“I don’t know who is in charge of Target, but these need to be pulled off the shelves immediately,” Espy emphasized in her social media video.
Espy had initially purchased the activity book to distribute to her students, but after discovering the glaring errors, she took to social media to warn others.
“As a U.S. history teacher, I noticed these discrepancies as soon as I opened the book,” Espy explained in her video.
She further added, “Mistakes happen, but this needs to be corrected ASAP.”
Following Espy’s video, Target responded by confirming that they would remove the flawed activity book from their stores and website.
It’s worth noting that Target has faced criticism and boycotts in the past year, including backlash over Pride-themed products. This includes a controversy surrounding the promotion of a black Santa Claus ornament in a wheelchair and a Pride-themed “gay nutcracker” Christmas ornament.
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What are the potential limitations or biases that could arise from utilizing PAA in information retrieval
There are several potential limitations or biases that could arise from utilizing PAA (Passage-based Automatic Answering) in information retrieval:
1. Passage Selection Bias: PAA relies on selecting relevant passages from a document to form an answer. If the algorithm is biased towards certain types of passages or sources, it may result in a skewed representation of information.
2. Data Availability Bias: PAA heavily relies on the availability of data and may be limited by the quality and quantity of the dataset used for training. If the training dataset does not adequately cover various topics or perspectives, it may result in biased or incomplete answers.
3. Contextual Bias: PAA may struggle to understand and incorporate the contextual nuances of a question. The lack of contextual understanding can lead to incorrect or irrelevant answers.
4. Confirmation Bias: PAA may unintentionally reinforce existing biases present in the data. If the training data contains biased information, the algorithm may generate biased or inaccurate answers that align with those biases.
5. Lack of Disambiguation: PAA may face challenges in disambiguating between multiple possible interpretations of a question or passage. This can lead to incorrect answers or confusion, especially in cases where context is essential for understanding.
6. Generalization Bias: PAA models are trained on a specific dataset and may struggle to generalize well to new or unseen data. As a result, the answers generated may not be accurate or reliable for all types of queries.
7. Dataset Bias: PAA models are trained on datasets that are created and labeled by humans, who may inadvertently introduce biases during the annotation process. This can perpetuate existing biases and inaccuracies in the training data.
It is crucial to consider and mitigate these limitations and biases when developing and deploying PAA systems to ensure fair and accurate information retrieval.
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