Voice of the DBA

Tag: ethics

  • The Danger of Sharing Data

    In the past, many businesses hired employees whose role was deciding which prices to charge for their goods or services. At one point, organizations largely set prices based on their costs, though over time they tend to look at their competitors and set similar prices. If, however, management from multiple companies went into a room and determined prices, this would be price-fixing.

    Price fixing is illegal. In many countries, we would not allow different companies to work together in a way that might reduce competition or take advantage of consumers. However, that might be a struggle in the future as we find companies using various services to help them manage their systems.

    In this case, a few different Las Vegas hotels used the same company to help them decide how to price their rooms. Rainmaker is a revenue management platform, which uses lots of data to help hotels price their rooms in a way that maximizes their revenue. That sounds great, but if this company is successful and many of their clients are in the same location, this is really a way of sharing data by proxy. The hotels are being sued because of their use of this platform.

    This one of the problems (or advantages) of lots of data. It allows information to be drawn out of data that wouldn’t otherwise be obvious. Certainly, lots of companies look at their competitors and make decisions based on what they see. I’m certain there are lots of people inside airlines constantly checking the prices of their competition. However, they are gathering this data independently and making their own decisions. If Rainmaker were used by American, United, and Delta to set prices for flights, I imagine many would see this as an anti-trust violation.

    Big data is powerful. It can help give an organization an advantage over its competition. This is one reason lots of companies hire data professionals like us; they see data as a very valuable asset. However, in this case, I feel that one company selling this data, or rather the conclusions, to competitors is a problem.

    I expect more problems like this in the future as smart people look to harness the power of data and sell their services to competitive companies in many industries.

    Steve Jones

    Listen to the podcast at Libsyn, Stitcher, Spotify, or iTunes.

  • AI Ethics

    I found this article to be an interesting look at how we might add ethics to AI systems in one area. As the article points out, “… today there is no broadly accepted AI ethics framework, or means to enforce it. Clearly, ethical AI is a broad topic …”.

    Glad someone is thinking, or many people are, but sad that we aren’t really moving in a direction that creates a better system for us humans to work under, or be bound by. To be fair, I do think this is a very difficult topic and hard for any large group of people to agree on what should be done.

    Network monitoring is a fairly narrow problem domain, at least compared to many others. The article notes there are places where AI can, and does help humans that work in computer networking. That being said, how does the AI handle ethical considerations. For example, can we ensure the AI handles data privacy appropriately. This could be in compliance with some regulation like GDPR. It could also be in a manner that doesn’t disclose data inside a company to other systems or humans who shouldn’t see the specifics of network traffic (like passwords, credit cards, or any sensitive information).

    There are also other considerations. While we see bias in AI systems trained on previous human behaviors, because humans are biased, will network AIs similarly have bias? Will they be less helpful for power users, who have a wide variety of traffic? Those are often privileged users, who might benefit the most from helpful monitoring. Will AIs discriminate against a user when another humans trains or influences it against them? A crude example might be a network admin that doesn’t like women. They enforce more strict rules against women, and influence the AI to do the same. How will the AI, or others, detect this type of issue?

    Maybe the most difficult thing with AI is with corner cases. The ethical dilemmas that might not be easy to solve can confound an AI. Maybe the most ethical choice here is to seek other counsel or let other people help make a decision. To be fair, this is hard for humans to do as well, but for some reason we seem to trust computers less. At least some of us do.

    Ethics is a challenging issue. I find it to be difficult as a human, and my own inconsistency means I might react differently at different times or in different places. How we translate that to AI systems, which are increasingly a part of our world, is going to be hard. I don’t have answers, but I lean towards transparency, accountability from the humans in charge, and the ability to reverse (and apologize for), poor decisions.

    Steve Jones

    Listen to the podcast at Libsyn, Stitcher, Spotify, or iTunes.

  • Where We Need Better AI Disclosure and Responsibility

    There are a lot of contract and gig jobs in the world today. It used to be this type of work was widely spread throughout programming and technology, but these days many types of jobs are commonly completed using contract workers. I like the flexibility of contract work, but I also think that these workers need to be better at saving and planning for the future because of less employment stability. Usually they are paid more, but they need to use that to reduce the risk of being unemployed.

    One trend that I’ve seen taking place in some of these positions is the use of software and AI to determine if a worker is doing an acceptable level of work on a regular basis. Amazon might be one of the highest-profile companies doing this, especially as they expand into the delivery business. They are using the power of computers to manage an army of workers rather than traditional human managers. This includes terminating them. There’s an article that talks about some of the experiences of their workers.

    I don’t know how their system works, but I do know the frustration of trying to work with a company that doesn’t use humans for many tasks. If you’ve ever tried to contact Google, you know that it’s incredibly difficult to actually communicate with a human. Google seems to think that its automated systems can handle all situations. They might handle many things, but they don’t do a good job in plenty of situations, and there is little recourse to have a human intervene.

    I do think that AI and ML can help our companies better interact with the world in many cases, but these systems are certainly looking for broad patterns. Maybe these patterns handle the middle 80% of cases, or maybe it’s more like 60%, but there are plenty of situations where humans ought to be involved. Maybe more important, when someone uses these systems to make decisions that impact human life, there should be some explanation and understanding of how the model impacts this specific situation. We want to know why the computer comes to its conclusion in medical care, employment, legal issues, or maybe anything other situation.

    There is work being done to try and explain how these models work. The important thing, however, is to ensure that while we may understand the model, we also need to disclose the reasoning to those affected by the systems. Any appeal process should include this explanation, and likely with a human involved at some point to help evaluate the model for accuracy, fairness, or any other measure that is relevant. To me, we ought to require this of companies using AI models in their business practices.

    Steve Jones

    Listen to the podcast at Libsyn, Stitcher, Spotify, or iTunes.

  • Women’s History Month and Being Better

    This month is Women’s History Month, and it’s a chance to stop and think about the impact that women have had in history. After reading Monica Rathbun’s post on being a women in the SQL Server community, I wanted to drop a few notes.

    Personally, I think this month is a good chance for me to think about the impact women have had in history. For example, after seeing Hidden Figures, I stopped and did a little research to see just how impactful women were in the space program, and how little my early education taught me that.

    The more I learn across time and in different places, the more I realize how much we are shaped by the history we learn, and the more I realize how incomplete what we learn can be. We certainly can’t learn everything, but I find more and more that various minorities, genders, and even other cultures (non-US for me) have had an impact I never realized.

    This isn’t to complain or chastise anyone, but rather just an opportunity to stop and take a little time this month to learn more. The Women’s History Month site might be a good place to start.

    Being a Better Community Member

    I’ve had the honor of knowing and working with some amazing women in this business. I hope that I have treated them as peers and the highly knowledgeable and talented professionals that they are. Kalen Delaney, specifically, was one of my heroes and inspirations early in my career, and I’ve been honored to not only meet her, but be able to call her a friend.

    I’ve also seen some poor behavior, with men treating women at events like they are entertainment or potential mates, without understanding these are our peers. While being attracted to others is natural and will happen in any environment, I do think that some people, especially men, forget that they should be careful not to make others uncomfortable, and certainly not unsafe.

    In trying to be better, I know that others of all genders may interact and express themselves differently than I do. I try to ensure I don’t talk over or dismiss the opinions of anyone, but certainly women.

    I’ve been on panels and at events where few, if any, woman are speaking, and I do try to find ways to support others when I can. I also try to be aware of situations where others may feel unsafe and offer to help.

    Mostly, I think the best way to be a better community member to women or minorities is to just be professional. We would assume a new employee at our organization was competent and deserved to be there until they prove otherwise. Just do the same thing for anyone you meet at an event and try to let go of biases and preconceived notions.