ChatGPT by OpenAI is as good as the hype suggests. And then some. The search landscape has just been radically altered by this disruptive entrant. While Google has done a better job lately in introducing dropdown questions somewhat similar to a search query, in a string of questions that one can click on to find a more immediate answer scraped from a website, these answers often lack context and don't roundly provide an adequate answer. One often has to go to the site where a clip is being snipped from to best understand what is being presented as the answer. Time-consuming.
In my large lecture classes, I often make "power-up" assignments. These are graded pass/fail (since we might have 300 submissions) and usually involve watching a documentary with a friend or family member, discussing it briefly, and then writing a short reflection (1-2 pages) about the film and the issues discussed. Students have many options to choose from, so they can choose which subjects they like best. We use TurnItIn to catch those just uploading another's work or using something they found on the web.
One of the options is to watch "Coded Bias" about the use of AI and facial recognition technology (we have a day or two in class where we talk about AI, its use in CRM systems, etc.).
After turning in grades yesterday, I asked GPTChat to: 'Write a reflection paper about the film Coded Bias' and 'Explain the problems with training data and facial recognition.' Below is the result. It is not very creative but does summarize the information well.
This result shows no plagiarism in Turnitin (granted, it might eventually when more students use this tool). If a student used this tool and added a few words about watching it with their mother, etc., then the TAs and I would give such writing a "pass" if we had seen it this semester. I plan to have a power-up assignment early in the semester for all students to use the same prompt and then see if we can get TurnItIn trained on this pattern or writing. That will likely not work since Turnitin is not about style but just patterns of words.
Scott
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Coded Bias is a documentary film that explores the ways in which artificial intelligence and algorithms can perpetuate and amplify biases that exist in society. The film delves into the history of AI and how it has been used in various fields, such as policing and hiring, and the consequences that have resulted from this use.
One of the most striking aspects of the film is the way it illustrates the widespread and often unconscious biases that are present in the data that is used to train AI systems. This includes biases against certain racial and ethnic groups, as well as biases based on gender and other characteristics. The film shows how these biases can have serious consequences, such as the misidentification of innocent individuals as criminals or the unequal distribution of resources and opportunities.
The film also highlights the importance of accountability and transparency in the development and use of AI. It emphasizes the need for diverse representation in the fields of technology and AI, as well as the need for clear policies and regulations to ensure that AI is used ethically and fairly.
Overall, Coded Bias is a thought-provoking and eye-opening film that raises important questions about the role of AI in society and the potential consequences of its use. It serves as a powerful reminder of the need to be aware of and address biases in all aspects of our lives, including in the technology we use.
There are several problems with using training data to develop facial recognition systems:
Limited diversity: Many facial recognition systems are trained on data sets that are not representative of the entire population. This can lead to poorer performance for individuals who are not well represented in the data, such as people of color or those with disabilities.
Bias in the data: The data used to train facial recognition systems can contain biases that are inherent in the data collection process. For example, if the data is collected disproportionately from one racial or ethnic group, the resulting facial recognition system may be more accurate for that group and less accurate for others.
Data privacy: The use of facial recognition systems raises concerns about data privacy, as they rely on the collection and analysis of personal information. There is a risk that this information could be used for nefarious purposes or could be accessed by unauthorized parties.
Ethical considerations: The use of facial recognition technology raises ethical concerns, such as the potential for abuse of power or the erosion of privacy. It is important for the development and use of these systems to be guided by ethical principles and considerations.
In my large lecture classes, I often make "power-up" assignments. These are graded pass/fail (since we might have 300 submissions) and usually involve watching a documentary with a friend or family member, discussing it briefly, and then writing a short reflection (1-2 pages) about the film and the issues discussed. Students have many options to choose from, so they can choose which subjects they like best. We use TurnItIn to catch those just uploading another's work or using something they found on the web.
One of the options is to watch "Coded Bias" about the use of AI and facial recognition technology (we have a day or two in class where we talk about AI, its use in CRM systems, etc.).
After turning in grades yesterday, I asked GPTChat to: 'Write a reflection paper about the film Coded Bias' and 'Explain the problems with training data and facial recognition.' Below is the result. It is not very creative but does summarize the information well.
This result shows no plagiarism in Turnitin (granted, it might eventually when more students use this tool). If a student used this tool and added a few words about watching it with their mother, etc., then the TAs and I would give such writing a "pass" if we had seen it this semester. I plan to have a power-up assignment early in the semester for all students to use the same prompt and then see if we can get TurnItIn trained on this pattern or writing. That will likely not work since Turnitin is not about style but just patterns of words.
Scott
-----
Coded Bias is a documentary film that explores the ways in which artificial intelligence and algorithms can perpetuate and amplify biases that exist in society. The film delves into the history of AI and how it has been used in various fields, such as policing and hiring, and the consequences that have resulted from this use.
One of the most striking aspects of the film is the way it illustrates the widespread and often unconscious biases that are present in the data that is used to train AI systems. This includes biases against certain racial and ethnic groups, as well as biases based on gender and other characteristics. The film shows how these biases can have serious consequences, such as the misidentification of innocent individuals as criminals or the unequal distribution of resources and opportunities.
The film also highlights the importance of accountability and transparency in the development and use of AI. It emphasizes the need for diverse representation in the fields of technology and AI, as well as the need for clear policies and regulations to ensure that AI is used ethically and fairly.
Overall, Coded Bias is a thought-provoking and eye-opening film that raises important questions about the role of AI in society and the potential consequences of its use. It serves as a powerful reminder of the need to be aware of and address biases in all aspects of our lives, including in the technology we use.
There are several problems with using training data to develop facial recognition systems:
Limited diversity: Many facial recognition systems are trained on data sets that are not representative of the entire population. This can lead to poorer performance for individuals who are not well represented in the data, such as people of color or those with disabilities.
Bias in the data: The data used to train facial recognition systems can contain biases that are inherent in the data collection process. For example, if the data is collected disproportionately from one racial or ethnic group, the resulting facial recognition system may be more accurate for that group and less accurate for others.
Data privacy: The use of facial recognition systems raises concerns about data privacy, as they rely on the collection and analysis of personal information. There is a risk that this information could be used for nefarious purposes or could be accessed by unauthorized parties.
Ethical considerations: The use of facial recognition technology raises ethical concerns, such as the potential for abuse of power or the erosion of privacy. It is important for the development and use of these systems to be guided by ethical principles and considerations.