Interest in doing the right thing is extending to IT systems beyond AI. This is good, even if mainly motivated by the fear of legal or financial consequences. Credit: RapidEye / Getty Images Years ago, I found myself sitting in a conference room dealing with a question that I’ve gotten thousands of times in my career: Was the technology I was representing, which I created as the CTO of an enterprise technology company (I’m being purposely vague), the right fit for a specific customer problem that I had just learned about in that conference room? This would have been an easy sale and one that we needed to make our quarter. All I needed to do was agree with the customer, who was already convinced that my technology was a “perfect fit.” Instead, I explained that a competitor’s technology was likely a better fit and had the features and functions needed for this problem. The salesperson shot daggers at me from across the table. Commission gone. My technology was unique but it was designed for a specific problem. The customer likely could have made it work, but it was not the optimal fit for this situation. IT ethics defined That was my first real experience making choices that lived up to an ethical standard. I did it because it was the right thing to do; I’m not an exceptionally virtuous man. I was called back to that same customer six months later to look at a new problem for which my technology was the right fit. That deal was three times bigger than the first one, and I found out recently that my technology is still running that portion of the system. That feels good. Ethics in IT, as I define it, is the process and guidance of solving the problem with the best possible solution rather than choosing something because of any personal agenda or benefit, such as making your sales numbers to trigger a bonus or doing a favor for a friend. Artificial intelligence has caused many enterprises, technology providers, and consulting organizations to refocus on ethics. All are realizing that it is not only a good idea but also can significantly reduce their legal exposure. In a recent LinkedIn poll that I conducted, about one-third do not consider ethics when making IT decisions. If that sounds bad, it would have been 50% to 60% just a few years ago. Walk the talk The concept of ethics is very much like the concept of sustainability. You’ll get nodding heads in meetings when you talk about the responsibilities of building buildings, cars, cities, etc., using sustainable best practices. Unfortunately, people don’t often practice what they preach. Personal benefits trump adherence to these concepts, or they may not even know they are doing something wrong. If you’re designing, building, deploying, or operating AI-based systems, ethics is probably on the radar because there is an absolute liability cost that will have to be paid. Were that not the case, ethics would be given much less consideration. Sorry if I seem a bit pessimistic about my fellow citizens, but I’ve found that consequences are the core drivers here, not a sense of right and wrong. What does ethical IT look like? We see ethics at play in cloud computing when a decision to go with one cloud provider over another is driven by non-technical considerations, such as architects feeling pressure to go with their current “cloud partner” for political and not technical reasons. The cloud partner choice may be hugely underoptimized or more costly than going with a different cloud provider or even using on-premises technology from a non-partner. However, the most optimized solution returns the most value to the business. The jig may be up for these shenanigans. New cloud finops tech can spot these bad decisions before they are implemented, in many cases. Even more importantly, how ethical is it to waste so much money choosing one technology over another for the wrong reasons? Beyond architectural decisions, there are ethical considerations regarding data privacy. As enterprises collect, store, and analyze vast amounts of data, handling this data responsibly has become a real issue, especially when data is handed over to others or when it is inadequately protected and thus subject to a higher risk of loss. High-profile data breaches and the misuse of personal information have led to public outcry and increased regulatory scrutiny. These developments have driven enterprises to prioritize ethical considerations more than they did a few years ago, ensuring that data privacy is embedded within their IT systems and business practices. Of course, AI is front and center these days, which means confronting algorithmic bias. AI and machine learning algorithms can perpetuate and even exacerbate biases in their training data. As we know, this can lead to unfair and discriminatory outcomes, which are unethical and can get you sued. Notice that much of what’s driving ethics in IT is not enterprises and people attempting to do the right thing. It’s mostly about avoiding consequences. Thus, unfortunately, we’ll need to keep consequences in place. Ethical advantage? There is a growing recognition that ethical technology can be a competitive advantage for some companies. Enterprises that proactively address ethics in their IT systems can differentiate themselves as being the “most ethical.” Indeed, I’m starting to see this in marketing for traditional enterprises and technology companies. Companies can attract ethically conscious consumers by committing to ethical standards. This is handy in recruiting talent. I’ve gotten more than a few questions about ethics when hiring. The comeback of ethics in IT is encouraging, even if motivated by a hidden agenda. The interplay between technology, society, and business is tightly coupled and complex. Focusing on ethics will help mitigate risks, meet regulatory requirements, drive innovation, build customer trust, and create a more equitable digital future. Now, what can we do about selfishness? Related content analysis Azure AI Foundry tools for changes in AI applications Microsoft’s launch of Azure AI Foundry at Ignite 2024 signals a welcome shift from chatbots to agents and to using AI for business process automation. By Simon Bisson Nov 20, 2024 7 mins Microsoft Azure Generative AI Development Tools analysis Succeeding with observability in the cloud Cloud observability practices are complex—just like the cloud deployments they seek to understand. The insights observability offers make it a challenge worth tackling. 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