FeatureAI in the Real World: What’s Changing at Work, Online and at Home

While AI promises immense benefits, such as automating tedious tasks, accelerating scientific discoveries and expanding access to education, it also raises pressing concerns about job displacement, bias in algorithms and misinformation on a grand scale.

As AI systems become more embedded in our daily lives, the conversation about their societal impact has moved beyond tech circles and into the mainstream, sparking debates on ethics, governance and the very nature of human life.

Table of Contents

• How AI Is Changing the Way We Work
• When You Can’t Trust What You See or Hear
• The Double Life of AI: Crime Fighter and Accomplice
• The Hidden Inequities in Machine Learning
• The Positive Side of Artificial Intelligence 
• Creativity in the Age of Code
• Where We Go From Here  

How AI Is Changing the Way We Work

While automation has consistently been a driver of efficiency, AI-powered systems are now capable of handling tasks once thought to require human intuition — analyzing data, generating content and even making decisions in areas such as finance and healthcare. This shift has fueled fears of widespread job loss, particularly in traditional white collar roles.  

However, the impact of AI on employment is not simply one of elimination — it is also one of transformation. Many industries are seeing the rise of AI-augmented roles, where human workers collaborate with AI to increase productivity rather than being outright replaced. In sectors such as customer service, marketing and data analysis, AI is taking on repetitive tasks, enabling workers to focus on higher-value, creative and strategic functions.  

Reskill or Be Left Behind

The more pressing challenge isn’t job loss — it’s about the continual shift in required knowledge. As AI transforms industries, workers must continually adapt to new tools and methodologies.

Kathryn Wifvat, AI professor at the University of the Cumberlands, emphasized that "AI will dramatically reshape the workforce in the next decade, but job loss is not the biggest problem. The real challenge is the rapid shift in required skills. Many people will be left behind unless we rethink how we train and reskill workers." She pointed to a need for continuous education and adaptable learning models, ensuring workers can keep pace with evolving AI-driven roles rather than being displaced by them.

The New Class Divide

The divide between blue-collar and white-collar automation is also shifting. While factory workers have faced automation for decades, AI is now encroaching on knowledge-based professions.

For example, legal firms are using AI-powered tools to analyze contracts and conduct research, while financial institutions rely on AI-driven risk assessment models. At the same time, new opportunities are emerging in fields like AI ethics, prompt engineering and AI system oversight, creating jobs that did not exist a decade ago.  

When You Can’t Trust What You See or Hear

The rise of AI-generated content has blurred the line between reality and fiction, making it increasingly difficult to distinguish truth from deception. AI-powered deepfakes — hyper-realistic images, videos and audio clips — have evolved to the point where even trained professionals struggle to verify their authenticity. While these tools have legitimate applications in entertainment and creative industries, they have also become powerful weapons for disinformation campaigns, election manipulation and reputational attacks.  

AI’s ability to generate this realistic content is changing the public's perception of information — sometimes for the worse. 

"AI is already shaping public opinion, especially through news algorithms and social media manipulation," said Wifvat. "With deepfakes and AI-generated content flooding online spaces, we need fact-checking mechanisms and media literacy education to prevent misinformation from eroding trust."

Journalism, once the cornerstone of reliable information, now faces an unprecedented challenge. The image above was quickly created using the Freepik AI image/video generator with a simple prompt: A photorealistic image of Donald Trump standing on the moon next to a moon lander.

AI can generate entire news articles, fake interviews and misleading imagery in seconds, eroding public trust in traditional media. Social platforms, where news is often consumed in quick, unverified bursts, amplify the problem. A single AI-generated video, falsely depicting a world leader making a controversial statement, can spread to millions before fact-checkers can intervene. The consequences extend beyond politics — financial markets, businesses and even personal reputations can be destabilized by convincing but fraudulent AI-generated content.  

Addressing this crisis requires a combination of technological solutions and regulatory frameworks. AI-powered detection tools, such as those being developed by OpenAI, Microsoft and various research institutions, aim to identify manipulated content through digital watermarks and forensic analysis. Social media companies are also under pressure to implement stronger content authentication measures, ensuring that users can verify the origins of videos and images.  

The Disturbing Rise of AI-Generated Exploitation

The rise of AI-generated images and videos has raised serious concerns about consent, digital identity and misinformation. Deepfake technology is now widespread on social media, where altered images of celebrities, politicians and everyday people can be used for everything from entertainment to deception.

One striking example is the case of Billie Eilish. As a global music star, she has deliberately controlled her public image, avoiding sexualization throughout much of her career. In a 2019 Calvin Klein ad, Eilish said that one of the reasons behind her trademark baggy clothes is that “Nobody can have an opinion because they haven’t seen what’s underneath.”

AI-generated images that artificially alter her body or place her in suggestive scenarios violate that personal agency — and they have been flooding social media. These fake visuals not only mislead audiences but also fuel clickbait-driven engagement, allowing social media pages to profit from deceptive content at the expense of the individual portrayed. 

The implications extend beyond celebrities. AI-generated content has been used to manipulate political figures — as shown below in this deepfake of former President Obama — spread false narratives and even create entirely fake influencers with millions of followers.  

AI Ethics Needs Answers — and Fast 

When people can no longer trust what they see and hear, it erodes confidence in legitimate sources and makes it easier for bad actors to spread falsehoods. Worse, it creates plausible deniability — if everything can be faked, then anything inconvenient or damaging can be dismissed as AI-generated, even when it’s real.

As these tools become more sophisticated, society faces pressing questions:

• Should AI-generated images and videos require mandatory labeling?
• What legal protections should exist for individuals falsely depicted in AI content?
• How can platforms be held accountable for hosting and spreading AI-driven misinformation?

Paul DeMott, chief technology officer at Helium SEO, explained that beyond better AI detection systems and the addition of digital watermarks, there's also a need for media literacy training. "The public should learn about AI content generation methods to develop better information analysis competence." 

The Double Life of AI: Crime Fighter and Accomplice

AI is playing a dual role when it comes to online crime — acting both as a tool for law enforcement and platform moderation while simultaneously being exploited by criminals to evade detection.

Major platforms like Facebook, Instagram and YouTube rely on AI-driven moderation to scan for illicit content, including drug sales, weapons trafficking and financial fraud. These AI systems analyze images, text and metadata to flag suspicious activity, but their effectiveness is far from perfect. While platforms claim AI is central to keeping their ecosystems safe, many illegal operations continue to thrive, often by using AI themselves to bypass detection.  

Criminal enterprises have become increasingly sophisticated in their use of AI. Scammers deploy AI to generate realistic deepfake identities, automate phishing schemes and create misleading content that tricks moderation algorithms. AI-generated ads promoting illegal goods, from counterfeit passports to firearms to cloned credit cards, slip through detection systems by using carefully crafted wording and images that evade automated scrutiny. Additionally, many illicit marketplaces have shifted from the dark web to encrypted messaging platforms such as Telegram and Signal, where AI-generated messages and bots facilitate anonymous transactions. These platforms offer criminals an added layer of protection, making it harder for authorities to trace and disrupt operations. 

AI Can’t Police Itself — So Who Should?

To combat these growing threats, law enforcement agencies are deploying AI for digital forensics, pattern recognition and predictive analytics. AI-driven surveillance tools can scan vast amounts of online data to identify suspicious transactions, track cryptocurrency movements linked to criminal activity and even detect trafficking networks based on social media interactions.

However, the rapid evolution of encryption technology and decentralized marketplaces presents an ongoing challenge. Even as AI helps police crack down on certain operations, criminals continuously adapt, using machine learning (ML) to refine their evasion tactics.  

The ethical and regulatory implications of AI’s role in online crime raise pressing questions, like: 

• Should platforms like Meta be held accountable when AI fails to prevent illicit activity?
• How can regulators balance privacy and encryption rights with law enforcement’s need to track criminal networks?

Governments and tech companies face increasing pressure to strengthen AI policies that curb illicit activity while preserving online freedoms. The future of AI-driven crime prevention may depend not just on better technology but on clearer accountability measures for the platforms that serve as the battleground for these digital conflicts.

The Hidden Inequities in Machine Learning

AI is often seen as an objective decision-maker, but the reality is far more complicated. Algorithmic bias — when AI systems produce unfair or discriminatory outcomes — has become a major ethical concern, especially in hiring, lending and law enforcement. Bias in AI models often stems from the data they are trained on, reinforcing existing inequalities.

Wifvat pointed to a striking example from Lensa, an AI avatar generator that exaggerated female body proportions — an issue that only became apparent after public backlash. “Women were shocked to see that their avatars all had exaggerated large chests. After people called it out, the model was updated to tone it down.”

Fighting Bias Starts With Better Data

The first step in fixing bias, she continued, is to look at the AI system in question and figure out where the bias actually is. "Once you know that, you can refine the model to reduce it." She also emphasized the role of diverse development teams in mitigating bias. If more women had been involved in Lensa’s development, the issue might have been caught earlier. 

Because AI models learn from historical data, they can inherit and amplify societal biases, leading to real-world consequences that disproportionately affect marginalized groups. Human oversight, said Wifvat, is non-negotiable. "Any high-stakes decision-making needs a human subject matter expert to review and sign off on the results. AI can be an incredibly powerful tool, making experts far more productive, but it cannot replace them."

Ultimately, Wifvat warned, "AI is only as good as the data it is trained on, and when bias is baked into that data, it reinforces real-world disparities." This makes bias audits, fairness checks and transparency in AI decision-making critical to ensuring equitable outcomes, particularly in high-stakes fields.

One well-documented example comes from automated hiring systems. Some AI-powered recruiting tools have been found to favor male candidates over female applicants because they were trained on past hiring data that reflected gender imbalances in the workforce. Amazon, for instance, scrapped an AI hiring tool after discovering that it systematically downgraded résumés containing words like "women’s" (as in "women’s chess club") while favoring language more commonly found in male applicants' submissions. 

To address these issues, researchers and policymakers are pushing for greater transparency and explainability in AI — often referred to as explainable AI (XAI). The goal is to ensure that AI systems can not only make decisions but also provide clear, interpretable reasons for how they arrived at those decisions. Some brands are implementing bias audits and fairness checks, requiring independent assessments of AI models before they are used in high-stakes scenarios.  

The Positive Side of Artificial Intelligence 

AI has seen exceptional results in three areas, according to DeMott: climate modeling, drug discovery and personalized education.

Improving Diagnostic Detection & Accuracy 

"The utilization of AI for health diagnostics stands out as a particularly interesting field that enables doctors to discover diseases earlier,” he said. “AI systems by themselves do not address social problems effectively since human decisions together with proper regulations inside and between industries remain essential. AI functions as a tool for solution growth more effectively than it serves as an all-in-one perfect answer."

In medical diagnostics, AI-powered imaging systems are now capable of detecting cancers, neurological disorders and cardiovascular diseases earlier and with greater accuracy than traditional methods. AI models trained on large datasets of X-rays and MRIs can identify subtle anomalies that even experienced radiologists might miss, leading to earlier interventions and improved patient outcomes. Similarly, AI-driven drug discovery platforms can analyze millions of molecular compounds in a fraction of the time it takes human researchers, accelerating the development of life-saving treatments for conditions like cancer, Alzheimer’s and rare genetic disorders.  

Aiding Those With Disabilities 

Beyond medicine, AI is also transforming accessibility for people with disabilities. Voice recognition software, real-time speech-to-text transcription and AI-powered sign language interpretation tools are breaking down communication barriers for individuals with hearing and speech impairments. Meanwhile, computer vision and AI-guided navigation systems are improving independence for the visually impaired, providing real-time descriptions of surroundings and guiding users through unfamiliar environments.  

Supercharging Personalized Learning 

AI is also playing a major role in expanding access to education. Adaptive learning platforms use AI to personalize lessons based on individual learning styles, helping students grasp concepts at their own pace. AI tutors and chatbots assist with homework, language learning and skill development, making high-quality education more accessible to underserved communities. In regions where teachers are scarce, AI-driven remote learning solutions ensure that more students have access to the resources they need.  

AI’s role in education goes beyond automation — it can be used to enhance critical thinking rather than replace traditional learning methods. "AI can play a huge role in data-driven decision-making," said Wifvat. "Online courses use Learning Management Systems like Blackboard and Canvas, which collect data on how many students are viewing lectures, files and other materials."

Creativity in the Age of Code

AI is also reshaping the creative world, raising both excitement and existential questions about the role of human ingenuity. AI-powered tools such as ChatGPT, MidJourney and Stable Diffusion can generate art, music, literature and even film scripts, blurring the lines between human and machine-made creativity.

While some see AI as a collaborative tool that enhances artistic expression, others fear it could undermine originality, devalue human craftsmanship and replace creative professionals, hence the 2023 Writers Guild of America strike against AI script writing.

DeMott told VKTR that artists — together with writers and musicians — face both positive prospects and hazards from AI content generation, explaining that generative AI presents access to creative expression to all people, while conversely creating dilemmas regarding content originality and artists' payment. “AI could function as an instrument which helps creative professionals rather than doing their tasks in their place to generate ideas quickly without replacing their artistic abilities." 

In industries such as graphic design, music production and content writing, AI is already being used to speed up workflows, generate ideas and automate repetitive tasks. Writers use AI-assisted drafting tools to overcome creative blocks, musicians remix AI-generated melodies and visual artists explore AI-powered image generation to push creative boundaries. However, these advancements raise concerns: If AI can create paintings, songs and even bestselling novels, what does that mean for artistic identity, authorship and intellectual property rights?  

At the heart of the debate is a philosophical question: Does AI enhance or dilute human creativity? Some would argue that AI serves as an amplifier of human imagination, helping creators explore new styles, break creative ruts and push artistic boundaries. Others worry that mass-produced, AI-generated content could flood the market, leading to a decline in originality and making it harder for human artists to compete.  

Where We Go From Here  

AI is reshaping society in ways that go far beyond technological progress — it’s influencing how we work, learn, create and connect. While it holds enormous promise in fields like healthcare, education and climate action, it also raises serious concerns about job displacement, bias, misinformation and privacy.

The challenge ahead isn’t just about advancing AI — it’s about ensuring it serves humanity rather than deepening inequalities.