Artificial intelligence (AI) is a powerful tool that can help companies in all industries make better strategic decisions. However, as the capabilities of artificial intelligence become stronger and stronger, the need for human intervention is becoming more and more obvious. Technology has not yet reached the point where it can completely imitate human thinking and decision-making processes, but artificial intelligence and machine learning tools have developed to the point where they can be trusted for their key tasks and decisions.
Table of Contents
- Human Intervention
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AI, automation, and other HiTech innovations are an essential part of humanity’s collective advancement. Still, without human intervention, these innovations have no ethical compass and can even hold implicit biases of their own. In this post, we will discuss why human intervention is crucial to the long-term success of AI.
The Importance of Human Intervention
While science fiction is full of examples of Artificial Intelligence running amok and causing problems for humanity, such as Terminator and 2001: A Space Odyssey, the reality is that issues with Machine Learning and Artificial Intelligence can be more nuanced and obscure. AI and Machine Learning algorithms are not developed in a vacuum. Like all web development or app development projects, AI is built by humans who have their own flaws and biases.
AI and Machine Learning algorithms can get tainted or affected by a developer’s biases, even if the developer is unaware that these biases exist. It may be hard to believe, but algorithms cannot only absorb but perpetuate gender, racial, ethnic, and other biases. In the computer science field, this is referred to as AI bias. One famous example of AI bias stands out in recent memory: Amazon’s AI model for recruiting talent.
Amazon’s AI model was designed to screen job candidates. As you can imagine, Amazon receives many applications for open positions, and AI helps the company screen candidates and find the best people for available jobs. However, the problem with Amazon’s AI model was that it didn’t like women candidates. This is a significant issue. As a global society, we have been working on being more inclusive and including diverse voices in the workplace, so how did Amazon’s AI model end up with this gender bias?
The answer is shockingly simple, and it highlights how vital human intervention is for AI and Machine Learning models. Amazon’s AI model held a gender bias against women because it was trained using historic company hiring data for computer engineers. The overwhelming majority of computer engineers employed at Amazon and in most tech companies are men.
Using this information, Amazon’s AI model decided that men were preferable to women. As a result, the model demoted any application that made references to women. Applications with references to attending an all women’s college or captaining a women’s sports team are some of the examples that were demoted based on AI bias.
Luckily, internal research teams at Amazon caught this issue before it was deployed on a wide scale. However, this is an important example of the value of human intervention. Without human intervention, in this case, who knows how much long-term damage would have been done. The problem with AI models and algorithms is that they don’t always have the broader societal context that we take for granted.
It is doubtful that anyone purposely trained the model to discriminate against women in the Amazon case, but that doesn’t change the result. Without human intervention, AI models could easily discriminate against large populations of people.
Practical Ways Human Intervention is Used to Guide AI
The Amazon example highlights the importance of human intervention, but such issues, while they get the most attention, are not the most common. Therefore, your project management team needs to understand the practical, everyday way they can use human intervention to ensure your AI models and Machine Learning algorithms are functioning the way they were intended.
Human intervention is beneficial for:
- Data manipulation
Data analytics and processing are some tasks that AI excels at, but as we saw in the Amazon example, sometimes AI models and algorithms run amok. Human intervention to aid in data collection, annotation, and validation helps improve the performance of Artificial Intelligence.
Computer systems can collect billions of data points, but this data is useless if the AI algorithm is not trained properly. Human intervention is needed to annotate data to correct false assumptions made by AI and help build its vocabulary. Artificial Intelligence tools can do so much more than humans, but these tools provide inconsistent or flawed results without human intervention and guidance.
Without a set of rules or a detailed plan, AI can accomplish very little. Yes, AI can learn things on its own, but even how it learns needs to be dictated by a human, as we have already discussed. Thus, the human role in governance is to ensure that AI models follow the rules and apply them to match their training.
However, human oversight does not end with training and application of the rules. Human intervention must also oversee the outcomes produced by AI. The goal is to ensure that the results are in line with expectations. This oversight can also help identify biases in the data and faults in training.
Machines don’t understand compassion. Therefore, it is up to human intervention to teach AI models and Machine Learning algorithms to account for the compassionate side of humanity. Artificial Intelligence is a powerful tool, but if it can’t make humane and compassionate decisions, it won’t do humanity much good.
Artificial Intelligence is a fantastic innovation, but it cannot replace humans entirely. The more advanced AI becomes, the more necessary it will be to have human oversight. If you’re interested in how your business can harness the power of Artificial Intelligence and Machine Learning, reach out to an app development partner. Artificial Intelligence is a great tool, but human intervention will always be necessary to ensure positive outcomes.