As we venture into a world increasingly defined by Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP) models, and Big Data, we must ask ourselves a critical question: Are these technologies working for us or against us? Specifically, when it comes to the creative industry, are these technologies empowering Black creatives and promoting equity, or are they perpetuating systemic bias and marginalization?
In her groundbreaking paper, “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” Dr. Timnit Gebru reveals how large language models trained on biased data can perpetuate and amplify existing societal inequalities. This could lead to real-world consequences, such as discrimination in hiring and lending decisions, further exacerbating the challenges faced by marginalized communities, primarily through the lens of Black creatives.
For instance, despite its promise, facial recognition technology has been shown to produce higher error rates for those with darker skin tones, particularly Black individuals. This not only contributes to the injustices and systemic racism within the criminal justice system but also endangers the very lives and freedoms of Black people.
Moreover, using AI in hiring processes can perpetuate and reinforce existing biases within the workforce. For example, suppose a model is trained on data from a predominantly white company. In that case, it may favor white candidates over others, leading to a lack of diversity and perpetuating the same biases in the hiring process.
In the creative industry, the consequences of bias in AI and Big Data models are dire, leading to the underrepresentation and marginalization of Black creatives. We can see this in the lack of diversity in the representation of Black creatives in advertising, design, film, video game, and other creative fields.
We must recognize that these biases in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP) models, and Big Data are not inherent flaws in the technology itself but rather a reflection of the biases present in the data, programmers, and society that these models are trained on. To address this problem, we must strive to detect and mitigate bias in AI and Big Data models, increase transparency and accountability, and ensure diverse perspectives are represented in developing and deploying these technologies.
Moreover, we must uplift and amplify the voices of Black creatives, providing opportunities for them to showcase their work, celebrate their contributions, and create networks for professional growth and mentorship.
As we move forward in a world shaped by AI, ML, NLP, and Big Data, let us challenge bias and work towards a fair, equitable, and empowering future for Black creatives. Together, we can co-create a more diverse and inclusive creative industry that celebrates and elevates the contributions of Black creatives.
Dr. Gebru, Timnit. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? March 2021
Dr. Gebru, Timnit. Race and Gender. 2019
As we venture into a world increasingly defined by Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP) models, and Big Data, we must ask ourselves a critical question: Are these technologies working for us or against us? Specifically, when it comes to the creative industry, are these technologies empowering Black creatives and promoting equity, or are they perpetuating systemic bias and marginalization?
In her groundbreaking paper, “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” Dr. Timnit Gebru reveals how large language models trained on biased data can perpetuate and amplify existing societal inequalities. This could lead to real-world consequences, such as discrimination in hiring and lending decisions, further exacerbating the challenges faced by marginalized communities, primarily through the lens of Black creatives.
For instance, despite its promise, facial recognition technology has been shown to produce higher error rates for those with darker skin tones, particularly Black individuals. This not only contributes to the injustices and systemic racism within the criminal justice system but also endangers the very lives and freedoms of Black people.
Moreover, using AI in hiring processes can perpetuate and reinforce existing biases within the workforce. For example, suppose a model is trained on data from a predominantly white company. In that case, it may favor white candidates over others, leading to a lack of diversity and perpetuating the same biases in the hiring process.
In the creative industry, the consequences of bias in AI and Big Data models are dire, leading to the underrepresentation and marginalization of Black creatives. We can see this in the lack of diversity in the representation of Black creatives in advertising, design, film, video game, and other creative fields.
We must recognize that these biases in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP) models, and Big Data are not inherent flaws in the technology itself but rather a reflection of the biases present in the data, programmers, and society that these models are trained on. To address this problem, we must strive to detect and mitigate bias in AI and Big Data models, increase transparency and accountability, and ensure diverse perspectives are represented in developing and deploying these technologies.
Moreover, we must uplift and amplify the voices of Black creatives, providing opportunities for them to showcase their work, celebrate their contributions, and create networks for professional growth and mentorship.
As we move forward in a world shaped by AI, ML, NLP, and Big Data, let us challenge bias and work towards a fair, equitable, and empowering future for Black creatives. Together, we can co-create a more diverse and inclusive creative industry that celebrates and elevates the contributions of Black creatives.
Dr. Gebru, Timnit. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? March 2021
Dr. Gebru, Timnit. Race and Gender. 2019
As we venture into a world increasingly defined by Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP) models, and Big Data, we must ask ourselves a critical question: Are these technologies working for us or against us? Specifically, when it comes to the creative industry, are these technologies empowering Black creatives and promoting equity, or are they perpetuating systemic bias and marginalization?
In her groundbreaking paper, “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” Dr. Timnit Gebru reveals how large language models trained on biased data can perpetuate and amplify existing societal inequalities. This could lead to real-world consequences, such as discrimination in hiring and lending decisions, further exacerbating the challenges faced by marginalized communities, primarily through the lens of Black creatives.
For instance, despite its promise, facial recognition technology has been shown to produce higher error rates for those with darker skin tones, particularly Black individuals. This not only contributes to the injustices and systemic racism within the criminal justice system but also endangers the very lives and freedoms of Black people.
Moreover, using AI in hiring processes can perpetuate and reinforce existing biases within the workforce. For example, suppose a model is trained on data from a predominantly white company. In that case, it may favor white candidates over others, leading to a lack of diversity and perpetuating the same biases in the hiring process.
In the creative industry, the consequences of bias in AI and Big Data models are dire, leading to the underrepresentation and marginalization of Black creatives. We can see this in the lack of diversity in the representation of Black creatives in advertising, design, film, video game, and other creative fields.
We must recognize that these biases in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP) models, and Big Data are not inherent flaws in the technology itself but rather a reflection of the biases present in the data, programmers, and society that these models are trained on. To address this problem, we must strive to detect and mitigate bias in AI and Big Data models, increase transparency and accountability, and ensure diverse perspectives are represented in developing and deploying these technologies.
Moreover, we must uplift and amplify the voices of Black creatives, providing opportunities for them to showcase their work, celebrate their contributions, and create networks for professional growth and mentorship.
As we move forward in a world shaped by AI, ML, NLP, and Big Data, let us challenge bias and work towards a fair, equitable, and empowering future for Black creatives. Together, we can co-create a more diverse and inclusive creative industry that celebrates and elevates the contributions of Black creatives.
Dr. Gebru, Timnit. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? March 2021
Dr. Gebru, Timnit. Race and Gender. 2019