Data Security and Privacy  

Securing data is one of the biggest challenges facing the AI industry. Since AI requires large volumes of data, data is collected from millions of users to feed AI Systems. For this reason, AI systems risk being prone to data breaches and identity theft. AI’s use of large data further creates tension with privacy legislation and culture in many jurisdictions. In the UK and Europe for instance, the current law limits both the potential for sharing data sets and the scope of automated decision-making. These restrictions are considered a limitation to the capacity of AI. Experts, therefore, recommend that creating a highly secure infrastructure to gather and store the data generated is paramount in addressing some of these challenges.

Ethical Issues In AI

The concept of AI is implicated with several ethical issues: bias, privacy breaches, and AI decision-making that could not be challenged. Because AI develops its learning system, some of these issues may not be evident until it is deployed. Many AI developers work in a competitive, profit-driven context where speed and efficiency are paramount. Due diligence implied by regulation and ethical review is regarded as costly and therefore undesirable. It’s therefore advised that AI designers identify and mitigate potential ethical risks while AI is at the design stage. It is also recommended that ethical reviews be conducted regularly once AI is in use. 

Regulatory Issues

The AI industry is heavily dependent on the private sector in terms of securing funds for research and development. Most governments often rely on giant tech companies to build their AI software and explore their AI talents. This creates serious regulatory challenges. But, without governmental control, the future application of AI’s potential would be heavily outsourced to commercial interests. That situation provides little incentive to use AI to achieve positive social change.

Transparency Of Algorithm

Algorithms are considered the brain of Artificial Intelligence. AI systems, particularly machine learning, depends on algorithms to make decision or prediction.  These machine learning algorithms tend to become more complex in an attempt to achieve the required accuracy. Also, deep learning may use different layers of features with thousands of variables. Hence, becomes a challenge for human observers to explain the factors leading to a decision or prediction.

In this digital space, one cannot do without Artificial Intelligence and other emerging technologies. The next generation of engineers is therefore encouraged to upskill in these cutting-edge new technologies to stand a chance to work with organizations of the future- Microsoft.

REFERENCE

Information from https://www.mdis.edu.sg/blog/five-main-challenges-of-artificial-intelligent-technology/, https://www.chathamhouse.org/2022/03/challenges-ai, https://www.upgrad.com/blog/top-challenges-in-artificial-intelligence/ was used in this story.