Concept of Bayes' theorem and AI-Generated Global Health Imagery

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• Concept of ARIMA, and Insights from Data Scientist at BharatPe

• Top 5 ChatGPT Courses to Master Natural Language Processing

Today we are talking about Bayes' Theorem which is useful in predicting the probability of any event provided some information. As part of our learning series, I wanted to provide some free options to learn data science. As part of our market insights, how AI is shaping up the global healthcare industry. Do follow us on Linkedin and Twitter for more real-time updates.

— Arun Chinnachamy

Hot News: ChatGPT's Web Browsing Power Unleashed

OpenAI has revealed that ChatGPT can now access the internet, thanks to an integration with Microsoft's Bing search engine. This feature, available to ChatGPT Plus subscribers and Enterprise users, allows ChatGPT to provide current and authoritative information with direct source links. Previously, web browsing was briefly enabled in March but later disabled due to paywall bypassing concerns.

The feature has now returned with safeguards against such actions. Microsoft's Bing Chat, powered by a more potent OpenAI language model, offers similar functionality but lacks ChatGPT's integrated interface and additional features. This enhancement comes shortly after OpenAI introduced image analysis and audio conversations to ChatGPT.

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🧠 Featured Concept: Bayes' Theorem

Bayes' theorem is a mathematical formula that allows us to calculate the probability of an event occurring given that we know some information about the event. It is a powerful tool that can be used in a wide variety of fields, including statistics, machine learning, and artificial intelligence.

The basic formula for Bayes'

P(A|B) = P(B|A) * P(A) / P(B)

where:

• P(A) is the probability of event A occurring without any prior knowledge of event B. This is known as the prior probability.

• P(B) is the probability of event B occurring without any prior knowledge of event A. This is known as the marginal probability.

• P(A|B) is the probability of event A occurring given that we know that event B has already occurred. This is known as the posterior probability.

• P(B|A) is the probability of event B occurring given that we know that event A has already occurred. This is known as the likelihood.

Bayes' theorem can be used to solve a variety of problems.

For example, it can be used to:

• Calculate the probability of a disease based on the results of a medical test.

• Calculate the probability of a customer being fraudulent based on their past transaction history.

• Calculate the probability of a spam email being genuine based on the words it contains.

• Calculate the probability of a robot being able to successfully complete a task based on its past performance.

Here is a simple example of how to use Bayes' theorem:

Suppose that we are diagnosing a patient for a disease. We know that the disease is rare, with only 1% of the population having it. We also know that a medical test for the disease is very accurate, with a 99% chance of detecting the disease if the patient actually has it, and a 1% chance of giving a false positive result.

If the patient tests positive for the disease, what is the probability that they actually have it?

To use Bayes' theorem, we need to know the following probabilities:

1. P(D) = 0.01: The prior probability of the patient having the disease.

2. P(T|D) = 0.99: The likelihood of the patient testing positive for the disease if they actually have it.

3. P(T) = 0.01 + 0.99 * 0.01 = 0.02: The marginal probability of the patient testing positive for the disease.

4. Using Bayes' theorem, we can calculate the posterior probability of the patient having the disease as follows:

5. P(D|T) = P(T|D) P(D) / P(T) = 0.99 0.01 / 0.02 = 0.495

This means that there is a 49.5% chance that the patient actually has the disease, even though the disease is rare and the test is very accurate. This is because the test result provides strong evidence that the patient has the disease, even though it is still possible that the test result is false positive.

Bayes' theorem is a powerful tool that can be used to solve a wide variety of problems. It is used in fields such as statistics, machine learning, and artificial intelligence to make predictions and decisions based on uncertain information.

Unmasking Bias in AI-Generated Global Health Imagery

AI in Global Health: Reproducing Inequality

AI's Struggle with Stereotypes

Researchers enlisted AI's help in an effort to counter prejudices and biases in global health photography. The AI-generated visuals, however, fell short of expectations and reinforced prejudicial attitudes that already existed. The goal was to produce images free of the traditional global health clichés of helpless victims and white saviours. But the AI found it difficult to escape these ingrained habits.

AI's Role in Reinforcing Prejudice

Inequality in Health Imagery

Global health media have long been chastised for depicting diseases and healthcare scenarios with racial, gender, and colonial prejudices. This skewed portrayal has far-reaching repercussions because it associates marginalised genders and ethnicities with disease and impurity, promoting damaging pre-concieved notions. Health inequities in these areas have been worsened by structural racism and historical colonialism, weakening trust in the healthcare system.

Read the full exclusive article here.