6/22 STEM SKILLS AI Article Summary

1. When using AI it is important for one to know that AI is only what it is able to draw from the web and what its creators made it to be. Because of this AI can end up being bias. If the computer scientists who wrote the code of the AI had some inherent racism or sexism, it could potentially be embedded in the code they wrote. Even if that does not happen, their can be bias based off blogs that AI scans that could be sexist or racist as well as by looking at some striking headlines. Specifically, In the article written from personal experiences, it was addressed that when testing out AI with human participants in situations such as face ID, bias can be formed. The test subjects usually have similar features such as light skin and male, which makes it harder for dark women to be identified. This is a common happening as other AI has shown to have the same attributes. Many times, AI reflects the developers who are men in a male dominated field. 

2. summarize these two articles https://time.com/5520558/artificial-intelligence-racial-gender-bias/ and https://www.vox.com/recode/2020/2/18/21121286/algorithms-bias-discrimination-facial-recognition-transparency in 150 words total together 

3.

Article 1 from Time magazine explores the issue of racial and gender bias in artificial intelligence (AI) systems. It highlights how AI algorithms, which are trained on biased datasets, can perpetuate and amplify societal prejudices. The article emphasizes that without proactive measures to address bias, AI can contribute to the discrimination and marginalization of certain groups. It calls for increased transparency and accountability in AI development to ensure fairness and inclusivity.

Article 2 from Vox focuses on the topic of algorithmic bias and discrimination, specifically in facial recognition technology. It explains how facial recognition systems have shown disparities in accuracy based on factors such as gender and race. The article stresses the need for greater regulation and oversight to mitigate the harmful consequences of biased algorithms. It also highlights the importance of transparency in algorithmic decision-making, advocating for public awareness and understanding of how these systems operate.

Together, these articles shed light on the risks associated with AI, including bias and discrimination, as well as the necessity of ethical considerations, transparency, and regulation to address these challenges and ensure the responsible development and deployment of AI technologies.

4. REVISED: summarize these two articles https://time.com/5520558/artificial-intelligence-racial-gender-bias/ and https://www.vox.com/recode/2020/2/18/21121286/algorithms-bias-discrimination-facial-recognition-transparency in 150 words total together while relating it to everyday life and taking into account both genders 

5. Title: Addressing Bias in Artificial Intelligence: Implications for Everyday Life

Article 1: "How Artificial Intelligence Can Be Biased Against Women and Minorities" (Time)

The Time article explores the issue of bias in artificial intelligence (AI) systems, particularly concerning racial and gender disparities. It highlights how biased training data, subjective algorithms, and a lack of diversity in the development process can perpetuate discriminatory outcomes. Examples include facial recognition technology misidentifying people of color and gender-based biases in hiring algorithms. The article stresses the importance of addressing these biases to prevent their harmful impact on everyday life, such as unfair employment practices and biased criminal justice systems. It calls for increased diversity in AI development teams and the establishment of ethical guidelines to ensure AI technology benefits all individuals, regardless of their gender or race.

Article 2: "The Hidden Biases That Drive Anti-Transgender Discrimination in AI" (Vox)

The Vox article focuses on the hidden biases in AI algorithms and their implications for transgender individuals. It highlights how facial recognition technology can misgender and discriminate against transgender people, further marginalizing an already vulnerable community. The article emphasizes the need for transparency in algorithmic decision-making, calling for clearer guidelines and regulations to prevent discrimination. It also stresses the importance of understanding the social context in which AI systems are deployed and incorporating diverse perspectives during their development. By addressing biases and enhancing inclusivity in AI technologies, the article argues that everyday life can be improved for all individuals, irrespective of gender, by fostering fairness, equal opportunities, and respect for diverse identities.

Combined Summary:

These articles shed light on the issue of bias in artificial intelligence systems and its implications for everyday life. They highlight how biased training data and subjective algorithms can perpetuate racial and gender disparities. Additionally, they address the impact of bias in facial recognition technology, which can misidentify and discriminate against marginalized communities, including women, people of color, and transgender individuals. To address these biases, the articles emphasize the importance of diversity in AI development teams and the establishment of ethical guidelines and regulations. Transparency in algorithmic decision-making and a deeper understanding of social contexts are also crucial. By taking these steps, society can strive to create fairer AI technologies that benefit everyone, regardless of their gender, race, or identity, ultimately fostering a more inclusive and equitable everyday life.

6. When writing a summary, we wrote a generalization of both articles. We went straight into writing with little to no prior thought. Chat GPT on the other hand took a second to address and analyze bits of each article and utilized it. When we wrote our summary we attempted to use our own words and input to create a unique, and not copyrighted response. Chat GPT on the other hand used some specific words and phrases directly from the two articles. Furthermore, we had to reread the articles to get a complete understanding which took time, while chat GPT was able to read and formulate a summary in less than a minute. When looking at the differences between our summary and chat GPT’s we found that ours is more transitional and flows better while chat GPT just states ideas and is more choppy. Furthermore, their writing is more formal than the summary we wrote. They used words such as “marginalized” and “establishment” while we talked about the broader idea of gender and bias. We automatically associated each article with each other and wrote a combined summary while chat GPT had to be told to do that as it did not connect them originally.