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"How should societies get prepared for AI?"
Himanshi Allahabadi
Q.
Before delving into my thoughts, I think it’s important to point out the distinction between General and Narrow Artificial Intelligence. The former speaks more to the sci-fi portrayal of AI while the latter is what powers most of the leading tech today. The following is about narrow AI.
First and foremost, it is important to realize that while AI is used for forecasting and predictions and is generally future-looking, it uses historical data to try to find patterns and essentially ‘fit’ that data. Some of the areas of application that have thrived due to this property of AI are facial recognition, financial forecasting, medical image classification, and recommendation systems. The learning system by itself, while classifying previously unseen examples, works under the following assumptions:
- The input provided to train the AI model is all of the information needed to predict the outcome
- The data itself (i.e. the ‘ground truth’) is correct and unbiased
- The future will not be too different from the past
These assumptions altogether have important implications on the outcomes. They can lead to potential risks, particularly where AI is deployed for decision-making in the real world. In order to educate oneself about AI, one should keep these assumptions in mind.
Some key considerations come to mind as a result of these assumptions. For instance, it is important to think about the gap between the context of AI deployment and what AI can do, given the data at hand. We should have clarity regarding what the AI predicts, the key decision(s) being made based on the predictions, and the factors the system does not account for. We should be aware of any real-world factors, measurable or not, that are not included in the input of the learning model, and that could influence the outcomes. Whether or not those factors have been appropriately considered for interpreting results and making decisions, speaks to the goodness of the solution.
Secondly, it is important to consider what real-world policies and practices generate the data distribution. The AI system will more or less try to emulate these policies and practices implicitly, in the sense that its predictions will be consistent with what it learnt from the training data set unless explicitly trained to fulfill other objectives. For instance, if a bank’s policy for accepting loan applications is discriminatory, an AI system trained on their data will reflect and possibly amplify the same.
Lastly, while AI can be a powerful tool to analyse and mine patterns from the data, it is important to understand that sometimes future decisions cannot solely depend upon patterns learnt from historical data. To that end, it is essential to recognize the need for human expertise and knowledge.
Education systems can be adapted to prepare individuals for an AI-powered world by teaching them how it fundamentally works, making them aware of its potential, current limitations, and risks, and imparting them with knowledge of different career paths that leverage or research AI and its impact. Ultimately, AI is a problem-solving tool – albeit a rather powerful one. Education systems can encourage students to think about AI-based solutions for problems that they come across in their daily lives and empower them to use AI for environmental and societal good.
"Education systems can encourage students to think about AI-based solutions for problems that they come across in their daily lives and empower them to use AI for environmental and societal good."
Himanshi Allahabadi is a data scientist focusing on AI engineering and ethics. She is experienced in the development of large-scale cloud platforms for data-driven applications. She researches areas of trustworthy AI and machine learning best practices for collaborative open-source initiatives.
Himanshi Allahabadi
"Education systems can be adapted to prepare individuals for an AI-powered world."
AI Ethicist
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