
Addressing AI’s societal challenges
The increasing use of Artificial Intelligence (AI) and Machine Learning (ML) related technologies is already helping businesses, governments, and other institutions fine-tune their human processes by leveraging the best of both sides. And while these solutions can bring enormous benefits to its users, there is a greater possibility in the form of improving the well-being of the whole civilization. Having said that, it is essential for a larger group of people to place their trust in this movement. By ensuring that AI that puts people first, protects human rights, and deserves the public’s trust can do away with the issues relating to unfair bias, safety and privacy of citizens, etc.
Working towards a greater progress in fairness, transparency, and accountability, will be critical to building strong public trust. But how does one ensure that the data is appropriate, relevant, accurate, accurately labeled, diverse, and representative? And does the data reflect any existing human bias? Is it missing relevant data and training a model that makes biased predictions? After all, AI does not decide the purpose for which it is deployed by itself. Without human presence, it is impossible for the system to decide what an AI model should achieve, optimize or maximize. With this in mind, the quality of the training data in machine learning is pivotal to the accuracy and fairness of the resulting model. And even after an AI model has been launched, human supervision is essential to assess the quality and risk of unfair bias in ongoing new training data. Given the critical importance of the training data to the risk of bias, explanation of the nature of the training data can help address concerns over transparency and fairness of an AI model.
As we take bigger steps towards improving the fairness of AI models with diverse training data, we also need to keep in mind AI’s need for vast amounts of data with fundamental privacy principles that require minimizing the personal data used and retained, also limiting the ways in which that personal data can be used in the future. But in real, AI’s bias is only a replication of human bias. But by making sure that we understand the nature of the training data, test an AI model with diverse test data, review the model’s predictions for bias, we can make significant efforts to help achieve fairness in ways human systems can never do.
