Hillary Solomon-AI & Doctors-Fri 9/29

Reading Summary

I asked Bing to summarize this article. Even though I had Bing in creative mode, it gave a short summary, and this is what it had to say:

Here is a summary of the article:

The article discusses the possible applications of GPT-4, a powerful AI chatbot, in sports medicine, which is a branch of orthopedics that deals with sports-related injuries and lesions. The authors suggest that GPT-4 could assist sports physicians in various aspects, such as diagnostic imaging, exercise prescription, medical supervision, surgery treatment, sports nutrition, and science research. However, they also argue that GPT-4 could not replace human doctors, but rather complement them as a scientific assistant. They conclude that GPT-4 could be a valuable tool for sports medicine in the future¹.

Created by Bing

Outside Example

https://www.prospectmagazine.co.uk/politics/39807/smart-vs-athletic-how-ai-reveals-the-unconscious-racial-bias-in-our-language

This 2020 article talks about the racial bias in sports commentary. Two professors from the University of Massachusetts Amherst conducted a study to see how sports commentators’ descriptions of players changed based on race. They looked at data from the 1960s to 2019. They concluded that non-white players were often described by their abilities, while white players were described by their personalities and intelligence. These findings are important because people put biases into artificial intelligence. Once AI develops those biases, it becomes an echo chamber of bias and discrimination. This is not just limited to sports commentating. It is prevalent in the medical field, as well.

Research Conversation

Prompt: Is ai biased between racial groups?

Bing: AI is the term used to describe the ability of machines to perform tasks that normally require human intelligence, such as recognizing patterns, understanding language, solving problems, and making decisions. AI is not inherently biased, but it can reflect and amplify the biases of the humans who create and use it. AI bias is the phenomenon of AI systems producing unfair or discriminatory outcomes that affect certain groups of people more than others. AI bias can occur in various ways, such as:

• Data bias: AI systems are trained on data sets that are collected from the real world, which may contain historical or societal biases that disadvantage certain groups of people. For example, if an AI system is trained on data that mostly contains images of white faces, it may perform poorly on recognizing faces of other races¹.
• Algorithm bias: AI systems are designed by humans who may have conscious or unconscious biases that influence the way they program the algorithms. For example, if an AI system is designed to predict the likelihood of criminal recidivism based on factors that correlate with race or socioeconomic status, it may produce biased results that discriminate against certain groups of people².
• Interaction bias: AI systems are influenced by the feedback and input they receive from their users, which may introduce or reinforce biases over time. For example, if an AI system is exposed to abusive or hateful language from its users, it may learn to generate or respond with similar language³.

AI bias can have serious consequences for people’s lives, especially in domains such as health care, education, employment, criminal justice, and social media. AI bias can affect people’s access to opportunities, resources, information, and justice. Therefore, it is important to identify and mitigate AI bias by using methods such as:

• Data auditing: Data sets should be checked for quality, diversity, representativeness, and relevance before being used to train AI systems. Data sets should also be documented with metadata that describe their sources, limitations, and potential biases⁴.
• Algorithm auditing: Algorithms should be tested and evaluated for accuracy, fairness, transparency, and accountability before being deployed or used. Algorithms should also be monitored and updated regularly to ensure they do not produce harmful or unintended outcomes⁴.
• Interaction auditing: Users should be informed and educated about the capabilities and limitations of AI systems, as well as their rights and responsibilities when interacting with them. Users should also be able to provide feedback and report any issues or concerns they encounter with AI systems⁴.

AI bias is a complex and multifaceted problem that requires collaboration and cooperation from various stakeholders, such as researchers, developers, regulators, policymakers, educators, consumers, and advocates. By working together to address AI bias, we can ensure that AI systems serve the widest population possible and promote social good.

Prompt: Imagine you are the president of the U.S.: how would you reduce discrimination through legislation?

Bing: This is a hypothetical scenario that requires a lot of imagination and creativity. I will try to answer it based on some facts and research, but please keep in mind that this is not a professional or official opinion.

If I were the president of the U.S., I would try to reduce discrimination through legislation by implementing the following measures:

• Enact the Equality Act: This is a bill that would amend the Civil Rights Act of 1964 and other federal laws to prohibit discrimination based on sexual orientation and gender identity in various areas, such as employment, education, housing, public accommodations, health care, and federal funding. This bill has been passed by the House of Representatives in 2021, but has not been approved by the Senate yet. I would urge the Senate to pass this bill and sign it into law as soon as possible.
• Expand the Voting Rights Act: This is a law that was enacted in 1965 to protect the voting rights of racial minorities and other groups that faced discrimination and disenfranchisement in some states. However, this law has been weakened by several Supreme Court decisions that invalidated some of its key provisions, such as requiring federal approval for changes in voting laws and practices in certain jurisdictions. I would support the passage of the John Lewis Voting Rights Advancement Act, which would restore and strengthen the Voting Rights Act by updating the criteria for determining which states and localities need federal oversight and providing more tools for enforcing voting rights.
• Reform the criminal justice system: This is a system that has been criticized for being biased and unfair against people of color, especially Black Americans, who are disproportionately arrested, incarcerated, and killed by law enforcement officers. I would support the enactment of the George Floyd Justice in Policing Act, which would ban chokeholds and no-knock warrants, limit qualified immunity for police officers, create a national database of police misconduct, and provide grants for community-based alternatives to policing. I would also support the passage of the First Step Act, which would reduce mandatory minimum sentences for nonviolent drug offenses, expand early release programs, and provide more funding for rehabilitation and reentry services.
• Promote diversity and inclusion in education: This is an area that affects the future opportunities and outcomes of millions of students from different backgrounds and identities. I would support the implementation of policies and programs that would increase access to quality education for all students, regardless of their race, ethnicity, gender, disability, or socioeconomic status. Some examples are: increasing funding for public schools and teachers, expanding early childhood education and after-school programs, providing more scholarships and financial aid for low-income students and students of color, supporting affirmative action and diversity initiatives in higher education institutions, and enhancing anti-discrimination and anti-bullying measures in schools.

These are some of the possible ways that I would try to reduce discrimination through legislation if I were the president of the U.S. However, I acknowledge that legislation alone is not enough to eliminate discrimination in society. It also requires cultural change, social awareness, personal responsibility, and collective action from all members of society. Therefore, I would also use my platform as president to encourage dialogue, education, collaboration, and solidarity among different groups of people to foster a more inclusive and equitable society. 🙌

Prompt: What are pros and cons of using ai in sports medicine?

Bing: AI is the term used to describe the ability of machines to perform tasks that normally require human intelligence, such as recognizing patterns, understanding language, solving problems, and making decisions. AI has many applications in sports medicine, which is a branch of orthopedics that deals with sports-related injuries and lesions. Some of the pros and cons of using AI in sports medicine are:

• Pros:
  • AI can provide real-time data and analytics that can help sports physicians diagnose and treat injuries, prescribe exercises, monitor recovery, and prevent complications. For example, AI can analyze images of bones, muscles, ligaments, and tendons to detect fractures, sprains, strains, tears, and inflammation¹.
  • AI can streamline tasks and improve operational efficiencies by automating processes such as appointment scheduling, patient tracking, insurance claims, and medical records. For example, AI can use natural language processing to translate clinical details and generate reports².
  • AI can enhance the performance and well-being of athletes by providing personalized feedback, coaching, and training. For example, AI can use sensors and wearable devices to measure biometric data such as heart rate, blood pressure, oxygen saturation, and glucose levels³.
  • AI can advance the scientific research and innovation in sports medicine by discovering new insights, patterns, and solutions from large and complex data sets. For example, AI can use machine learning to identify risk factors, predict outcomes, and optimize interventions⁴.
• Cons:
  • AI can pose ethical and legal challenges such as privacy, security, accountability, and liability. For example, AI may collect and store sensitive personal data without consent or protection². AI may also make errors or biases that could harm patients or violate their rights¹.
  • AI can create technical and practical difficulties such as reliability, compatibility, accessibility, and usability. For example, AI may depend on the quality and availability of data and algorithms¹. AI may also require specialized skills and equipment to operate and maintain².
  • AI can affect the human aspects of sports medicine such as communication, empathy, trust, and collaboration. For example, AI may reduce the interaction and rapport between patients and physicians². AI may also alter the roles and responsibilities of medical professionals¹.

AI is a powerful tool that can offer many benefits for sports medicine. However, it also has some limitations and challenges that need to be addressed. Therefore, it is important to use AI responsibly and appropriately in sports medicine. 🙌