Bias in Artificial Intelligence: Can AI be Trusted?

Artificial intelligence is more artificial than intelligent.

In June 2022, Microsoft released the Microsoft Responsible AI Standard, v2 (PDF). Its stated purpose is to “define product development requirements for responsible AI”. Perhaps surprisingly, the document contains only one mention of bias in artificial intelligence (AI): algorithm developers need to be aware of the potential for users to over-rely on AI outputs (known as ‘automation bias’).

In short, Microsoft seems more concerned with bias from users aimed at its products, than bias from within its products adversely affecting users. This is good commercial responsibility (don’t say anything negative about our products), but poor social responsibility (there are many examples of algorithmic bias having a negative effect on individuals or groups of individuals).

Bias is one of three primary concerns about artificial intelligence in business that have not yet been solved: hidden bias creating false results; the potential for misuse (by users) and abuse (by attackers); and algorithms returning so many false positives that their use as part of automation is ineffective.

Academic concerns

When AI was first introduced into cybersecurity products it was described as a defensive silver bullet. There’s no doubt that it has some value, but there is a growing reaction against faulty algorithms, hidden bias, false positives, abuse of privacy, and potential for abuse by criminals, law enforcement and intelligence agencies.

According to Gary Marcus, professor of psychology and neural science at New York University (writing in Scientific American, June 6, 2022), the problem lies in the commercialization of a still developing science:

“The subplot here is that the biggest teams of researchers in AI are no longer to be found in the academy, where peer review used to be coin of the realm, but in corporations. And corporations, unlike universities, have no incentive to

Read More

What Is ML Bias and In which Can We See It?

In this to start with portion of a two-section deep dive into the realm of ML bias, Serhii Pospielov, AI Observe lead, Exadel, looks at what ML biases are and how we can location them superior at the resource to finally mitigate their detrimental results.

As we’re approaching a tech-pushed upcoming, the scope of artificial intelligence in our every day lives is expanding significantly. As machine learning and artificial intelligence improve, so does the concern about machine understanding bias. We concentration on this subject matter for the reason that we are doing work to strengthen our face recognition remedy: CompreFace. The accuracy of CompreFace is rather high – 99%, like many other facial recognition remedies. Nonetheless, the method nonetheless suffers from bias, and we are knowledgeable of our role in correcting it.

Despite the fact that the greater part of AI biases are accidental, their presence in machine mastering techniques can have a intense influence. Deepening the way device understanding units are used, device understanding biases can lead to unlawful actions, decreased income or product sales, and likely inadequate customer company.

Now scientists distinguish three sorts of biases: illegal, unfair, and inherent

Unlawful bias refers to types that split the legislation, for illustration, discriminating towards a social team. Unfair bias refers to models with embedded unethical actions. Consider a design that prefers gentlemen around ladies or similar views over opposing sights. Inherent bias relates to data designs that device mastering devices are projected to establish. All of these biases can have serious-entire world penalties, that’s why addressing this problem ought to be portion of the every day routines of AI teams. 

What is ML bias? 

Machine studying bias takes place when an algorithm systematically provides biased effects owing to incorrect assumptions in the machine discovering method. The kinds

Read More