Challenges AI must overcome
1. Bias: The issue around AI and bias has come to the fore in the recent years. The good news is leading organisations around the world are taking a closer look at it and have been dedicated to finding solutions to deal with the problem. As highlighted in an online article on neoteric.eu: “AI makes decisions based on the available data only. It doesn’t have opinions, but it learns from the opinions of others. And that’s where bias happens. Bias can occur as a result of a number of factors, starting with the way of collecting data. If the data is collected by means of a survey published in a magazine, we have to be aware of the fact that the answers (data) come only from those reading said magazine, which is a limited social group. In such a case, we can’t say that the dataset is representative of the entire population. The way data is probed is another way to develop bias: when a group of people is using some system, they may have favorite features and simply not use (or rarely use) other features. In this case, AI cannot learn about the functions that are not used with the same frequency.”
2. Privacy: Another challenge AI faces is around data – privacy. Tech writer Alan Martin opines in his article: “To learn, artificial intelligence needs data, and lots of it. Even with the best intentions (not even thinking about those with bad intentions), that data can be lost and potentially used against you, even with safeguards in place. That’s quite a big deal when AIs have access to everything about you from your daily routine tracked by smartphone to your private health records. Laws can regulate this to a degree, but that in turn impinges on process, because the more data an AI can absorb, the more effective it will be. It’s an awkward catch-22.”
3. Building trust: As AI becomes more pervasive, the concern over whether we can trust it or not has increased. If AI has to live up to its full potential, experts must find a way to get people to trust it. One way of doing so is to take a more collaborative approach towards decision-making – this enables AI to learn from human experiences and perform better.
4. Lack of skilled resources: The need for skilling, reskilling and upskilling has been a constant challenge for the AI industry. With the increasing applications of AI and the scale of its growth, there is an urgent need for field experts that have knowledge of how to make AI systems more useful to niche industries.
5. Cultural challenges: In his article for UPS, Iain Brown of SAS Insights explains: “For many business leaders, the emergence of AI is like an unwanted, surprise visit from the doctor. “Can we have a look at that data?” “Why does this process work in this way?” “Does this solution work with that solution?” “Have you seen how they’re doing it in this other department?” Imagine being on the receiving end of these questions, and it’s easy to see the role that culture plays in AI adoption. AI is powerful. It can also prove to be a real challenge to organizations accustomed to doing things a certain way. Perhaps even more importantly, it tends to shine a bright spotlight on parts of the organization that many have been content to simply leave in the dark. By now it’s standard practice to identify cultural issues as a potential obstacle to technology adoption — or, really, any scenario in which people are required to do things differently. But recent history has shown that if anything, culture plays an even larger role in successful AI adoption. As your organization expands its AI strategies, plan on taking extra care to manage the cultural side of the equation.”
