In the realm of Machine Learning, Reinforcement Learning with Human Feedback (RLHF) stands out as an innovative technique where human trainers play a crucial role in guiding the learning process of models. Unlike traditional reinforcement learning, which relies solely on pre-defined rewards, RLHF incorporates human judgment to shape the training environment. This method can have significant implications, especially when it comes to ensuring that models consistently favor certain outcomes over others. In this blog, we’ll delve into how trainers can influence models using RLHF, highlighting both the potential benefits and pitfalls. Human trainers can introduce biases, whether consciously or
Tag: Training ML Models
Some quick steps to overcome Bias and institute Fairness in Machine Learning Models
We are seeing that bias in Machine Learnings Models can be a big issue since the Data available to train these models can be biased. Consequently, using biased Machine Learning Systems can be dangerous when it becomes the basis to make decisions about humans automatically, with no human oversight, resulting in biased outcomes in fields of Employment and Loans. Similarly, another area of concern is ML Models that are being used for Political Reporting with significant “left wing” bias and publishing Reports and Stories with a left leaning slant, which makes the current political divide more pronounced. Putting this