AI Bias: Why It Happens and How to Stop It

The 80 Million Tiny Images dataset debacle highlights the problem of bias in AI systems due to biased data used for training. Most AI models are trained using datasets that reflect societal biases, leading to certain groups being represented more frequently and positively than others. This issue is further exacerbated by the lack of diverse datasets and underrepresentation of marginalized communities in the field of AI. To address this problem, researchers and technologists from diverse backgrounds are creating resources and guidelines to reduce bias in AI systems. It is crucial for institutions to invest in researching and creating diverse datasets, take accountability for any harm caused by AI tools, and promote a more inclusive workforce within the AI field.