Can Ethical AI Technology Exist?
AI is a hot topic right now because of the significant advancements that have been made in recent years. These advancements have been driven by a combination of increased computing power, improved algorithms, and access to vast amounts of data. As a result, AI is now able to perform tasks that were previously thought to be impossible, including image recognition, speech recognition, and natural language processing. Additionally, the potential applications of AI are vast, spanning across industries and use cases, from healthcare to finance to transportation.
According to a report by the McKinsey Global Institute, AI has the potential to create between $3.5 trillion and $5.8 trillion in value annually across 19 industries. The report states that "AI has the potential to create new sources of competitive advantage and value creation across a range of industries, including healthcare, manufacturing, financial services, and retail." This potential value creation has made AI a popular topic for businesses across industries.
Furthermore, AGI or Generative AI, defined as artificial intelligence with human cognitive abilities, as opposed to more narrow artificial intelligence, such as the headline-grabbing ChatGPT – could free people from menial tasks and usher in a new era of creativity. But such a historic paradigm shift is already raising insurmountable social and ethical issues, experts warn.
Here I'll highlight some of the most talked about issues and strategies companies can use to mitigate their risk.
One of the most significant ethical concerns associated with AI use in business is the displacement of jobs. As AI becomes more advanced, it has the potential to automate many jobs that are currently performed by humans. This could lead to significant job losses, particularly in industries that rely heavily on manual labor.
To address this ethical concern, businesses must be proactive in training their employees to work alongside AI systems. This includes providing training and development opportunities to help employees acquire new skills that are complementary to AI systems. Additionally, businesses can consider implementing AI systems that are designed to work alongside humans, rather than replacing them. This approach, known as augmented intelligence, involves using AI to enhance human performance, rather than replacing it.
Bias and Discrimination
AI, it turns out, inherits all of the biases of the humans who create it. For example, you might train an AI to find the best people by comparing them with your existing high performers. The AI does not know which aspects of those high performers are relevant. If all of your senior staff are white men, the AI will preferentially pick out white men because, based on the data, it doesn't know that this is a result of bias, not white men being legitimately better. This can then be perpetuated by hiring managers who are, indeed, biased in favor of white men. Algorithms learn based on feedback from everyone who uses them.
In other words, AI discriminates, and a lot of work is being done to find ways to reduce and eliminate AI biases. To address this ethical concern, businesses must be proactive in ensuring that their AI systems are designed and trained to be unbiased. This includes using diverse data sets to train AI systems and testing systems for bias before deploying them. Additionally, businesses must be transparent about their use of AI systems and provide opportunities for stakeholders to provide feedback on their performance.
Privacy and Security
AI systems often rely on large amounts of data to operate effectively. This data can include personal information about individuals, such as their name, address, and financial information. This raises ethical concerns about privacy and security, particularly in light of recent high profile data breaches and hacks.
This includes implementing robust data security measures, such as encryption and access controls. There are many factors that go into data protection, but the most important is that data permission should be role-based, and only people who really need it should be given admin-level access. It's important to remember that the weak link in any security system is people.
Transparency and Explainability
I think most everyone would agree that businesses should be transparent about their data collection and use practices and provide individuals with the ability to control their personal data.
Because AI systems can be complex, lack of transparency and explainability is another major concern with regards to data collection and use. The inherent complexity makes it difficult for stakeholders to understand just how decisions are being made. Without this understanding, companies may be at risk for decisions or actions taken that may appear ethically questionable.
Additionally, AIs being fed with data that does not belong to the person training the AI, is becoming an issue. With AI art and photo manipulation tools, for example, artists have discovered their work being used to train these AIs without the artists' permission.
To address these ethical concerns, businesses must ensure transparency within their AI systems and provide explanations for how decisions are being calculated. This includes providing stakeholders with access to the data used to train AI systems and the algorithms used to make choices that impact their business and their customers. Additionally, businesses can consider implementing explainable AI systems, which are designed to include and provide explanations for their decisions.
As AI continues to revolutionize the way businesses operate, it is essential that businesses consider the ethical implications of their use of AI systems. While the potential beneficial use cases of AI technology are almost unlimited, the very real social and ethical concerns I've outlined here must be recognized and addressed in order for businesses to ensure that their AI protocols are implemented responsibly.
Need help? At Envative, we help businesses design and implement AI systems that are ethical, transparent, and explainable.
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