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Zvažování rizik u strojového učení

Risk considerations for machine learning

How digitization and data can revolutionize product design and development.

Expanding artificial intelligence (AI) in society requires not only technically-educated talents, but also people with soft skills such as active listening, communication and critical thinking. Organizations need people with creative thinking to help them consider where and how to use artificial intelligence and address the implications of its deployment.
They need people who can understand the ideas and interactions of humans to work with those who program, design and implement this technology.

By Dagmar Kolberova

This insight first appeared in  Contigen 

The risk of using models that incorporate elements of bias is very real and there are numerous examples of how it can happen at the expense of the subjects involved.


While artificial intelligence (AI) has a potential to solve some of greatest problems in society, its use is also associated with the risk of persistent distortion, which can have far-reaching consequences and must first be fully understood. Since machines can evaluate or make a "decision" about anything, from evaluating suitability of job seekers to certain work to anything in terms of job risks or special health insurance, these AI systems should always maintain approach to justice and diversity in decision-making. It's imperative to ensure that these AI systems are able to do this and verification of such capabilities must become a standard part of their testing process. In a broader sense, it's not just about what technology can do but what it should do.

At Contigen we are excited about the opportunities that artificial intelligence brings to people and appreciate its ability to help us achieve what was previously unrealistic. That's why we are actively involved in AI projects that address, among other things, the issues of transparency, accountability and balance of artificial intelligence systems in their use in area of machine learning (ML).

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