How to Improve the Functionality of AI

By CIOReview | Friday, February 1, 2019

Artificial Intelligence is known for all its promised benefits but the fact that it can be bias cannot be neglected. Concerns regarding racial or gender bias in AI have arisen during process like hiring, policing and financial services. As this innovation is becoming a part of many industries, addressing biases must be the priority now. Businesses need to overcome specific challenges before they realize the real potential of this emerging technology.

• Provability is Essential

Organizations that are implementing AI cannot demonstrate- what it does and how it does. Provability is a significant element that is lacking in the AI predictions. There is no way that organization can prove or guarantee that AI’s decision making is transparent. The solution for this problem is making AI provable, transparent and explainable; authenticity and credibility are significant.

• Data privacy

AI is highly dependent on the enormous amounts of data to learn and make decisions. Machine learning systems are reliable on data that is sensitive and personal in nature and learn from them for future purposes. This makes it prone to severe issues like data breach and identity theft. For this, EU has designed a General Data Protection Regulation (GDPR) to ensure the protection of personal data and confidentiality.

• Algorithm bias

As AI systems rely on data, they do not know what evil and sound data are. Bad data includes- racial, gender or communal preference. These biased algorithms can make vital decisions go wrong and could lead to unfair and unethical judgment. In the future, these biases can be accentuated, as many AI systems can be fed on bad data. Hence the need is to train the systems with unbiased data and develop algorithms that can be easily explained.

• Inadequate Data

Organizations have access to more data than ever before but the datasets that are relevant to AI are indeed rare. The most potent AI machines are the ones that are trained on supervised learning, and this training requires labeled data- data that is organized to make it ingestible for machines to. Transfer learning, Unsupervised Learning and Active learning are a few examples of next-gen AI algorithms that can resolve the issue of inadequate data.