Ethical Implications of Business AI
This is a guide on ethical implications of business AI.
We are going to understand the ethical considerations and implications of using generative AI in business applications. So there are some ethics in operation with business cases for relationship AI, and this is really going to be an idea that we are trying to understand currently as of 2023, and that is the idea of the quest for ethical AI.
What does that actually mean? We know that we have got some necessity for responsible AI. We know that these systems are really powerful, they are intelligent, they can create content momentarily, but it is all derived from really just humans kind of generating that content.
So, we have not really been able to decide how we eradicate this thing known as the cognitive biases and judgements in this decision making. There are organizations such as the Responsible AI Institute. They are working to provide guidance concerning data rights, privacy, security, explainability, and fairness. Now, the objective is to create an ethical AI that comes with the idea of trustworthiness. Can we trust what this thing is saying to us?
We can see that alright, there are some ethics associated with this, but can I also trust the content that is being generated from this tool? We have to be accountable as well that we are talking about who is accountable for this kind of data generation and what does that actually mean using these tools? We have to be accountable for the actions that it takes.
This leads to all sorts of legal implications, not just ethical implications that we have to understand, but the idea is that we are really concerned with things like data rights, with these tools, privacy, security, explainability, and fairness, that we also need to look into things like our transparency. This is just one of the four guiding principles of AI.
Really, these principles come down to transparency. We have fairness, privacy, and security. Now, some of this stuff makes sense right? Fairness is going to be paramount for what we are trying to do. When I refer to transparency I just mean that one should be able to explain how the AI came to the decision.
Right now, these large language models are very advanced. They are very hard for us to know exactly what is happening without the billions of parameters that are associated with the model, which ones are important to the output that we just generated? How can we trace through the actual inward operations of this AI to get the output to see how this is actually being done?
So, another thing is that security and privacy is a major concern as we are providing data to this information. How much of it is being stored and is it being securely stored? There are all sorts of things that need to happen to make sure that we are satisfying these four principles of our AI.
Now, when we are talking about this ethical implication of AI in business, we know that there are going to be biased models, and biased models usually just come from the dataset being biased themselves. Is the dataset prejudiced to some race or group of people? That is going to cascade down to the AI being prejudiced or biased towards a group of people. Industry is going to have to understand these biased models because again, they can lead to legal implications.
There are also things like employee attrition. This is another issue, that the loss of employees is going to be a huge reported incident because what you are going to start seeing is that younger employees are increasingly expressing disinterest in working for organizations that do not practice this kind of responsible AI.
So, that will also be in effect for this as well. Industry is struggling to find and retain top talent and it is important to listen to exactly what these employees are going to be telling you about how your inward operations on using AI in your business.
There is also another thing, a kind of public perception. It feels that the public does not really know how these AI tools work. So, this is just naturally going to provide distrust with anything because these models seem a little scarier. People do not understand what they are.
This could potentially give some type of reputational damage to their corporate image if not marketed and communicated properly of what your AI practices are and what you are doing to make sure that you are following all the proper ethics and guidelines with using these tools. Now there are some more ethical implications for AI in business as well. It gets down to the consideration is data was biased to begin with.
One of the rules of AI is garbage in garbage out. Whatever you give the AI to train with, its output is going to be based off what that input was for your training. So, if your data was biased to begin with when you are training these tools, then it is naturally going to give out biased output based on that data. So you got to start with the training data and understand where it is from and what it says.
Make sure that it is not biased in any way toward a particular group of people, a gender, and start there. If you can clean out your data and make sure that it is not biased, your model is not going to be biased. This leads to my other things is that these models are not intelligent in the sense that they can detect their own bias. So, when these models cannot detect their own bias, you have to understand that you are going to have to provide some type of metric yourself to sort of measure what that bias is doing.
These could be things like a quantitative metric to see. What is the bias level for that? There are different ways to do that. You can actually calculate the toxicity level of what is being generated by this AI. A way that you can do even more is to have testers or a quantitative approach. Just ask it a few questions and see how people respond to this tool to see how in terms of the biased or non-biased approach.
This also leads to the slow uptake of AI adoption. This is going to take some time for everything to work because many feel responsible that AI is not being integrated fast enough. What you are starting to see now are legislators trying to create regulations and legislations to introduce regulatory legislation to force companies to increase their adaption rate.
So, what you are going to start seeing now, are actual laws that are being built around these kinds of AI adoptions, so that we make sure that there is going to be a responsible AI adoption so that corporations cannot get away with using their data in a harmful way. This is going to take some time. I think over the next decade, we are going to start seeing legislation in the united States and in Canada that is preventing this automated deployment decision tools to, for example, to screen job candidates.
Should that not be illegal? What does this actually mean? Just to give you an example of how this can become an issue. Let us say that you had an artificial intelligence that is inherently biased, and its job now is to receive resumes for a job description that you gave it. Now, because the AI is biased, it is now affecting the hiring practices. Because they are biased themselves, the organization became biased.
This just happened last year. They find organizations that are using AI, and it is biased from the start. Another ethical implication of business AI is that you have to understand that your real data and your training data are going to be different. It is going to be significantly different so that it is going to start getting some type of bias on its own. An AI model may work for many audiences, but not all of them. It is important that we understand which audiences are not performing properly with this AI. This comes with the idea of external monitoring.
Like I mentioned before, you might have some type of quantitative approach to monitor this tool to see exactly what is going to happen. Now this usually requires outside practitioners who have a deep understanding of the technical process involved, but it could also just be somebody using the tool and seeing how it behaves.