Michael Abramov

Experts are working on strategies to combat bias and implement fair AI practices. They are looking into how mitigating data bias in AI e-commerce can be achieved for various ethical considerations in online shopping. This includes understanding different biases like those in the data itself and algorithms. Knowing about these

AI trends in finance are pointing toward exponential growth. Machines can spot fraud in transactions, keep your money safe, and change how finance works altogether. Companies are using AI to be more creative and lead the way. They use it for many things like smart trading, planning for the future,

The construction industry's future is not just about using more machines to replace people. It's about teaming up human skills with AI help. Despite worries that AI will take jobs away, it's been shown to boost what people can do just as well. To

AI is getting better fast and the role of farmer expertise in AI models is increasing. Our machines are already good at diagnosing diseases, translating languages, and helping customers. In farming, AI can help with collecting and analyzing data, doing tasks, and even heavy labor. This frees up farmers to

AI governance became a motto of 2024, as an estimated 42% of companies use AI. This situation opens the door for biased outcomes, heightened inequalities, and violations of individual rights. AI is particularly difficult to regulate because it touches on so many aspects of society and includes so many stakeholders.

Data annotation for AI in finance is a key challenge for unlocking value of ML models. Annotated data is essential for building AI models that are both accurate and fair for finance tasks. It allows computers to understand data like financial reports or market trends. Adding human insights to data