Social impact of AI in shaping future human interaction

For many years, one of the main forces behind technology has been artificial intelligence (AI). AI has permeated practically every facet of our life, from the first chess machine to defeat a human to the most sophisticated AI systems capable of carrying out challenging jobs.

AI permeates every aspect of our lives, from improving daily tasks to revolutionizing corporate tactics. It has altered how we interact, work, and live, and its influence will only increase going forward.

But automation and artificial intelligence have drawbacks as well as advantages. Numerous academics caution about the potential negative social effects of AI, including ethical dilemmas, employment loss, and privacy issues.

The debate about AI’s social effects frequently seems superficial, yet it’s crucial to examine the moral and ethical issues that AI raises. For instance, the well-known AI startup DeepMind started “DeepMind Ethics and Society” to investigate crucial topics including responsibility, transparency, justice, and the financial impacts of AI.

Let’s first examine the definition and operation of artificial intelligence before delving into the risks and potential mitigation strategies.

Artificial intelligence: what is it?

AI can process information, recognize patterns, and even take actions based on logic. Artificial Intelligence (AI) is the ability of machines to copy human-like intelligence. In simple terms AI allows computers to think, learn and make decisions just like humans do.

In real life, AI can perform tasks such as analyzing data, recognizing images, understanding speech, and making smart decisions.  These abilities show how AI impacts society in areas like healthcare, education, business, and daily life.

An AI system gets smarter and more accurate the more and better data it has. While sophisticated AI systems learn from vast, varied data sources supplied by companies, researchers, and organizations, simple AIs are trained on limited data sets.

AI is fundamentally still a software system, but it is completely influenced by the data it is fed. Because the outcomes rely on how the AI is trained and applied, talks about AI must also address responsibility and accountability.

Social Impact of AI:

Understanding AI Bias

Biased decision-making is one of the main worries regarding AI’s social effects. AI has demonstrated prejudice in a variety of domains, such as:

• Unfair loan or credit decisions are known as credit approvals.
• Biased risk assessments and sentences in criminal justice.
• Hiring procedures: racist or sexist hiring methods.

AI systems have occasionally even generated outcomes that were skewed by gender or race.

Why Does AI Become Biased?

Artificial intelligence is not biased by design. The data utilized to train AI systems is the source of the issue. Simply said, AI picks up knowledge from patterns in its training data. The AI will reflect and magnify any human biases present in the data.

For instance, if a business uses its historical hiring records to train AI and those records reveal discrimination, the AI will pick up on and replicate those trends. To put it simply, AI is only as good or bad as the data it uses.

Who Should Be Accountable?

This raises an important question: who is responsible for AI’s biased outcomes? The answer lies in accountability. Developers, businesses, and data scientists must ensure that the data used to train AI is accurate, diverse, and fair.

Steps to reduce bias include:

• Careful data review before training AI models.
• Independent audits of AI systems to detect discrimination.
• Transparency in how AI makes decisions.
• Collaborations between data scientists, ethicists, and businesses to validate fairness.

Can AI Bias Be Fixed?

While it is difficult to change the mindset of every individual contributing to biased data, it is possible to identify and reduce bias in AI systems. With stronger accountability measures, better data quality, and continuous monitoring, AI can be made fairer and more ethical.

This does not replace the need to challenge bias in society, but it provides a practical way to minimize harmful social impacts of AI.

Unemployment and Automation

One of the biggest concerns about the social impact of AI is unemployment. Since the beginning, AI and automation have been seen as a threat to human workers, especially in labor-intensive industries. But the reality is more complex than simply “AI takes jobs.”

Lessons from the Past

History shows that automation does not always reduce employment. For example, when ATMs were introduced, many believed bank tellers would lose their jobs. Instead, the opposite happened. ATMs lowered costs for banks, which allowed them to open more branches and this created more jobs for tellers.

Similar patterns can be seen in other roles, such as cashiers and paralegals, where automation changed job structures but did not fully eliminate opportunities.

The Risk of Job Loss

That said, some industries may experience direct job losses. For example, self-driving trucks could replace human drivers, which would increase unemployment in the trucking industry. However, this also reduces accidents and improves efficiency, showing that automation brings both risks and benefits.

Shifting Job Trends

As AI grows, the job market will likely shift from labor-based roles to creative, managerial, and technical positions. This means new opportunities will open in areas such as data analysis, AI system management, and digital innovation.

Preparing for the Future

The key challenge is ensuring that future generations have the right skills to adapt to an AI-driven world. Education and training programs must focus on creativity, problem-solving, and technical expertise to prepare workers for evolving industries.

The Takeaway

It is wrong to assume that automation always causes mass unemployment. While AI may reduce jobs in some industries, it also lowers costs for businesses, which can lead to more hiring and growth in other areas.

Unemployment due to AI is a valid ethical concern, but instead of fearing it, society must focus on reskilling and upskilling workers to thrive in this new landscape.

Fake News and Misinformation

Social media platforms and search engines use AI to give users a personalized experience. By analyzing search trends and online behavior, AI decides which content such as news, ads, or trending topics to show first.

The downside is that this same system can spread fake news. AI-driven algorithms often prioritize engagement over accuracy, which allows misleading or false information to spread quickly, creating serious social consequences.

The Challenge of Stopping Fake News

Fake news has become one of the biggest threats in today’s digital world. Unlike traditional media, online misinformation spreads fast, with little accountability. Solving this issue is not easy because:

• Self-regulation is limited: Platforms like Facebook and Twitter have taken steps to remove harmful content, but internal controls are not always enough.
• Training AI to detect lies is difficult: While some statistical errors or clear falsehoods can be caught, misinformation often exists in “gray areas.” This makes it harder for AI to separate truth from opinion or manipulation.

The Risk of Biased Solutions

Another challenge is that teaching AI to recognize “truth” often depends on the beliefs of its creators. If not handled carefully, this could add even more bias to the system instead of solving the problem. Creating a universal standard for detecting fake news requires advanced data science and global cooperation.

Why Fake News Matters

Fake news is more than just misleading content — it can damage democracy, harm public trust, and misinform decision-making at a global scale. It has become a powerful tool to influence opinions, often with dangerous results.

A Way Forward

The good news is that governments, businesses, and tech companies are increasingly recognizing the social impact of AI in spreading misinformation. There is a growing push to design AI responsibly, implement fact-checking systems, and prevent platforms from being misused.

While the challenge is huge, collective action and smarter AI systems offer hope for reducing the dangers of fake news in the future.

Wealth Distribution in a Post-Labor World

One major outcome of automation is the concentration of wealth. As businesses replace human workers with AI and automation, their operational costs drop. This means profits rise but instead of being shared widely, they often stay concentrated at the top.

The result? A growing wealth gap, with the middle class facing the greatest risks in a highly automated economy.

The Idea of a “Tech Tax”

To reduce this imbalance, many experts suggest a “tech tax.” This tax would target companies that benefit from automation while reducing human jobs. The revenue from such a tax could be used to:

• Strengthen government safety nets.
• Fund retraining programs for displaced workers.
• Support the transition into new industries.

This approach ensures that the gains from AI are not just enjoyed by corporations but also used to help workers adapt and thrive.

Universal Basic Income (UBI) as a Solution

Another proposed solution is Universal Basic Income (UBI) a fixed income provided to all citizens regardless of employment. This idea has gained support from high-profile leaders like Elon Musk and Andrew Yang, who argue that as automation grows, UBI could become necessary for economic stability.

While controversial, UBI could provide a safety net for people in a world where jobs are less secure or more automated.

Preparing for the Future

There’s no doubt that automation will continue to benefit businesses by increasing efficiency and profits. But the challenge is ensuring that progress doesn’t come at the expense of workers.

The best path forward may be a mix of solutions:

• Tax policies that fairly redistribute gains from automation.
• Reskilling and training programs to prepare workers for new roles.
• Exploring long-term options like UBI for a post-labor economy.
  

The Takeaway

AI and automation will create incredible opportunities, but also big challenges for wealth distribution and economic equality. The key is to design systems that support workers, narrow the wealth gap, and build a fairer economy in a post-labor world.

Conclusion

At vativeApps, we believe the impact of AI on society brings both great opportunities and serious responsibilities. AI is reshaping how we live and work, but it also raises concerns around bias, unemployment, misinformation, and wealth inequality.

We see AI not as a threat but as a tool that must be used with ethics, transparency, and fairness. Businesses, governments, and communities must work together, ensuring unbiased data, reskilling workers, and adopting fair policies like tech taxes or universal basic income.

While challenges like fake news and inequality exist, AI also has the power to improve healthcare, education, and everyday life. With the right balance of innovation and accountability, AI can help build a more inclusive and prosperous future for all.