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Why is it Important to Bring a Human Connection to AI?
We've already seen how quickly artificial intelligence (AI) is being adopted. It's transforming industries from healthcare to finance by automating tasks and improving efficiency.
However, the more AI systems are involved and the more advanced they become, the more important human interaction and oversight are. AI needs humans to ensure accuracy, mitigate bias, and uphold ethical standards. So, what role will humans play in the AI future, and why is it so important that we're there every step of the way?
The Role of Human Oversight in AI Accuracy and Reliability
AI systems can't perform accurately and reliably. AI systems can generate errors, with some of these referred to as “hallucinations” , where the model fabricates information that feels factual but is inaccurate.
AI solutions like Starmind and Aira Verify use a human verification layer or human-in-the-loop validation system, whereby human experts validate information before sharing it across the organization with the relevant parties.
In Starmind's solution, the human verification layer ensures that the information is checked by a human expert within the organization using the solution.
For Aira Verify, the system improves AI-generated information for blind and low-vision users.
The Productivity Paradox: Why Technology Alone Isn’t Enough
McKinsey research has highlighted the "productivity paradox" when increased investment in technology has failed to deliver proportional productivity gains.
AI's benefits are maximal when combined with process changes and workforce reskilling. Businesses can't simply implement AI tools and expect instant productivity improvements.
For example, businesses using AI tools in customer service will only see improvements when human agents handle more complex cases.
Ethical Risks and Bias Mitigation in AI Systems
The ethical considerations and risks of AI are particularly worrying, especially to those outside of AI. General media outlets often choose to focus on this side of AI.
After all, AI systems trained on biased datasets will likely perpetuate those biases, leading to unfair decision-making. Ethical issues aren't inherently the fault of an AI system, but without human oversight, datasets with biases are more likely to see the biases amplified rather than reduced.
For example, some hiring algorithms have been shown to disproportionately disadvantage minority groups. This ethical dilemma can be somewhat simply solved by using human verification to audit datasets and correct biases to ensure fairness.
Human Connection in AI for Trust and Transparency
AI can usually be trusted. However, this trust is only possible through transparency and accountability.
Most AI systems can't be fully transparent for a myriad of reasons. Systems are valuable properties, and companies must protect their most precious products and patents.
Fortunately, human verification can foster public confidence, ensuring an understanding of how AI makes decisions and validates them, even if the exact ways AI achieves these results remain certain industry secrets.
In sectors like finance and healthcare, for example, manually reviewing AI-generated decisions can ensure that they align with ethical and professional standards, which helps maintain public trust.
Balancing Automation and Human Judgment
The balance between human interaction and automation is tricky to find. After all, AI is great at processing large datasets and repetitive tasks, while human judgment is an essential part of contextual decision-making.
For example, autonomous vehicles require human oversight for ethical dilemmas and unpredictable road scenarios.
After all, AI is exceptional at handling repetitive, data-intensive tasks but will fall short in contextual decision-making.
Autonomous vehicles are one such thorny issue within AI. The decision of whether to avoid an object at the risk of harming a pedestrian, for example, is a 21st-century example of the trolley problem.
Enhancing Innovation Through Collaboration
The real value of AI is when it works with humans. Most industry leaders agree that AI is at its best when humans use it as a tool to improve productivity.
It can handle a lot of grunt work, combining incredible processing power with creativity and contextual understanding that are still within the remit of living and breathing humans.
The human connection to AI is much the same as every other major technological leap we've seen in human history, allowing humans to achieve more. Certain jobs or specific roles disappear, but new ones are created, allowing humans to move on to more fruitful and interesting pursuits.
Thanks to human-AI collaboration, there have been some groundbreaking innovations. AI-assisted drug discovery tools can analyze thousands of molecular compounds, with human scientists guiding the experimental process and interpreting the results.
In creative industries, AI-generated art and music can offer new forms of expression, provided humans retain control over the final creative direction and don't simply lift the production wholesale and pass it off as their own.
Security and Preventing Deceptive AI Practices
The famed CAPTCHAs are a fine example of how humans and AI work together, even if many humans using CAPTCHA aren't aware of its full application.
CAPTCHAs verify that a user is human, distinguishing them between bots for cybersecurity. However, in this instance, this is also used as training data for AI systems, allowing humans to train AI while also remaining safe online.
The Future of AI with Human-Centric Development
So, what does the future of AI look like?
For one, the most powerful solutions focus on ensuring people are involved throughout the process, from design to deployment.
This allows for ethical AI frameworks emphasizing human involvement in design, testing, and monitoring. It also means that AI systems align much better with societal values and can operate safely and effectively.
Conclusion
The human connection in AI ensures accuracy, fairness, and trustworthiness. Human oversight, particularly in Starmind's human verification layer, ensures that biases are reduced, productivity is enhanced, and all use cases are approached ethically.
As AI technology evolves, human involvement must be integral to its development, from conception to implementation.
