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Make a Positive Impact on Your Community; Data Science and AI

    Data science is a field that has exploded in recent years. It allows individuals to harness the power of data to predict, influence, and control outcomes in Society. Data science can contribute to social developmental projects by identifying and targeting needs or developing better policies.
    Data science often requires advanced statistical and machine learning techniques to extract knowledge from large datasets in solving complex problems. This blog will discuss how data science and AI could improve the community.
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    AI is unethical; AI for good is often harmful.
    AI is a powerful tool. It can accomplish both evil and good, and the potential ethical consequences of using AI are significant. This can lead to AI stigma for being unethical.
    Although we have become familiar with AI’s ability to make decisions in autonomous vehicles, facial recognition software, and natural language processing, there is still much that we don’t know about the implications of AI.
    The lack of knowledge in this field means that people are generally uncomfortable with the idea of a machine making decisions and are wary of their use.
    One way to address this risk is through education on ethics and responsible AI use. Organisations such as the Partnership on AI can help educate organisations on how they should be using AI in their projects.
    By educating yourself before you implement an AI project and partnering with other organisations in your societal projects, you can overcome these concerns and positively change the society.

    Using AI to develop the society
    Artificial intelligence (AI) is a subset of machine learning that deals with the design and use of intelligent machines. AI can solve complex problems, such as predicting health outcomes in populations and improving individual decision making.
    Data science for societal good uses AI technologies to extract knowledge from large datasets and create predictive models of the world. By making data science more accessible, organisations can tackle poverty, homelessness, and education.
    Applications of AI for social development
    Data science help society in several different ways. Here are some of the applications highlighted by Matthew Stewart.
    ⦁  ⦁ Combining satellite imagery and machine learning to predict poverty
    Prediction of diabetic retinopathy 
    On Identifying Hashtags in Disaster Twitter Data
    Weak Supervision for Fake News Detection via Reinforcement Learning
    Protecting Geolocation Privacy of Photo Collections
    Detecting and Tracking Communal Bird Roosts in Weather Radar Data
    The Stanford Acuity Test: A Precise Vision Test Using Bayesian Techniques and a Discovery in Human Visual Response
    Linguistic Fingerprints of Internet Censorship: The Case of Sina Weibo
    Potential Issues regarding the use of AI
    The field of data science is rapidly expanding, and there are many ways to develop Society. Unfortunately, recently AI has been used to automate racial and gender bias in policing. Many advances have been made in using AI in societal projects, but it is essential to remember that it can also be a force for evil. So here is Mckinsey’s ten steps to deploying data science products for societal good to overcome barriers.

    1. Determine what type of problem you want to tackle. Have a clear definition of the problem with measurable objectives and requirements for success.
    2. Translate societal issues into a technical problems.
    3. Compare the value of AI to other solutions and evaluate potential risks and mitigations to determine whether AI is the right solution.
    4. Social sector organisation with resources to deploy AI solution at scale has committed to solving the defined societal problem.
    5. Ensure the required data set are available.
    6. Make data available for public use and the solution builder.
    7. Ensure availability of quality data—pre-processed data into a standard format for use by the training algorithm. The focus is shifting from big data to good data.
    8. Build and train AI models on the available data set.
    9. Use the Trained AI model to prove its value on the ground and report it.
    10. Build the technical capabilities to run and maintain the AI model independently and sustainably.
      AI is a powerful tool that can help us make positive changes in the world. There are many different ways to use AI to solve these problems and make a difference. But before you dive into the world of data science, there are some things to consider to do it right.
      Next week, I will show you three simple data science projects to get you started.
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     Further reading
    Mckinsey’s⦁ Approach
    Partnership on AI
    Matthew Stewart’s work on AI for Social Good
    ⦁ Check other highlighted links in the blog.