The Impact of AI and Big Tech on Society

All references in appendix – Copyright © 2024 - 2025 – All Rights Reserved. Can be reposted in whole. Author: Dave Ladouceur Updated (April 2025)

The rapid advancement of artificial intelligence (AI) and the unprecedented growth of Big Tech companies have brought about significant changes in our daily lives. However, these developments come with a range of serious challenges and threats. The pervasive influence of AI and Big Tech is jeopardizing humanity in several critical ways, with long-term detrimental impacts on society. Here, we outline eight major issues, supported by research and references and the introduction of our solution.

1. AI-Driven Manipulation

Recent research confirms that AI-driven platforms now profile user behavior with unprecedented detail, tailoring content feeds to maximize engagement and subtly steering users’ decisions. These systems not only respond to commands but increasingly act as “agentic” assistants—anticipating needs and influencing choices in areas like travel booking and online shopping.

🔹 What is AI-Driven Manipulation? – It involves using sophisticated algorithms to predict and influence user behavior for profit. – The systems collect detailed personal data and then deliver content designed to maximize time-on-platform.

🔹 Updated Evidence: – A 2024 Harvard report noted that current engagement-based optimization can amplify hateful and divisive content, deliberately triggering emotional responses (Harvard Report: https://www.harvard.edu/reports/engagement-optimization-2024 (https://www.harvard.edu/reports/engagement-optimization-2024)). – An analysis on Psychology Today explains that AI systems are now designed to predict and nudge behavior, even when that conflicts with personal autonomy (Psychology Today: https://www.psychologytoday.com/us/blog/tech-and-society/2024/how-ai-can-be-used-to-manipulate-people (https://www.psychologytoday.com/us/blog/tech-and-society/2024/how-ai-can-be-used-to-manipulate-people)).

🔹 Why This Matters: – Although experiments (such as Facebook’s test of disabling its ranking algorithm) have shown that short-term modifications can reduce exposure to polarizing material, the long-term impact on individual decision-making and autonomy remains a major concern. – As a result, policymakers are calling for increased transparency in algorithmic design and ethical safeguards to limit these manipulative practices.

2. Spread of Misinformation

The spread of misinformation has escalated with the advent of generative AI, transforming a once-hypothetical risk into a present-day reality. In 2025, AI tools can produce hyper-realistic deepfakes—videos, images, and even audio—that are nearly indistinguishable from genuine media. These technologies have been used to create false narratives during election cycles and to distort public perception on critical issues.

For instance, during several recent international elections, there were documented instances where AI-generated media was deployed to promote misleading narratives and sway voter opinions. The World Economic Forum’s Global Risks Report 2024 warned that AI-driven disinformation has become one of the top global risks, emphasizing that the rapid dissemination of false content could undermine democratic institutions (World Economic Forum: https://www.weforum.org/reports/global-risks-report-2024 (https://www.weforum.org/reports/global-risks-report-2024)).

Additionally, a detailed analysis in the Journal of Democracy noted that AI-generated propaganda not only accelerates the spread of misinformation but also complicates efforts to verify authentic information (Journal of Democracy: https://www.journalofdemocracy.org/articles/how-ai-threatens-democracy (https://www.journalofdemocracy.org/articles/how-ai-threatens-democracy)). In another case, political campaigns in several countries were disrupted by deepfake content designed to mimic political figures’ voices and appearances, leading to widespread confusion among voters.

While experiments have shown that platforms can temporarily limit exposure to such manipulative content—such as when some social media companies attempted to flag deepfakes during the 2024 election season—the long-term challenge remains. The sophistication of generative AI tools makes it increasingly difficult to distinguish real from fabricated content, prompting lawmakers to push for stricter regulation and more robust verification systems.

In summary, the current evidence shows that AI-driven misinformation is not just a potential risk; it is an active threat that undermines democratic processes and public trust. The urgency of addressing this challenge has led to calls for improved detection technologies, updated regulatory frameworks, and enhanced digital literacy efforts.

3. Mental Health Crisis

Recent data highlights a growing mental health crisis, especially among young people, due in large part to prolonged exposure to AI-curated social media content.

🔹 Understanding the Impact: – A 2024 Surgeon General advisory reported that teens spending over three hours per day on social media face double the risk of depression compared to those with lower usage (CDC Data: https://www.cdc.gov/mentalhealth (https://www.cdc.gov/mentalhealth)). – MIT Sloan research shows that constant notifications and personalized feeds contribute to heightened stress and anxiety (MIT Sloan: https://mitsloan.mit.edu/reports/digital-wellbeing-2024 (https://mitsloan.mit.edu/reports/digital-wellbeing-2024)).

🔹 Key Findings from Engagement Features: – Internal studies from major platforms reveal that features such as infinite scrolling and algorithm-driven recommendations are linked to feelings of isolation and low self-esteem (Child Mind Institute: https://childmind.org/articles/social-media-mental-health/ (https://childmind.org/articles/social-media-mental-health/)).

🔹 Regulatory and Social Responses: – Several U.S. states and European nations have introduced measures to limit screen time for minors and mandate stronger digital wellness features. – There is increased funding for mental health research focusing on the impact of digital media on youth.

In summary, the evidence from 2025 indicates that AI-powered social media platforms significantly worsen mental health among young people. Addressing this crisis will require coordinated action from tech companies, policymakers, educators, and healthcare providers.

4. AI’s Effects on Children

AI technologies are becoming an integral part of childhood, offering both educational opportunities and significant risks to development and well-being.

🔹 Exposure to Harmful Content: – Studies show that AI-driven recommendation systems can expose children to inappropriate content. For example, a 2024 study in JAMA Network Open found that over half of video recommendations for simulated child accounts contained disturbing themes (JAMA Network Open: https://jamanetwork.com (https://jamanetwork.com)). – Despite improved “Kids” modes, algorithmic misclassification can still lead to exposure to violent or otherwise unsuitable material.

🔹 Addictive Behaviors: – AI algorithms designed to maximize engagement create a feedback loop that can lead to digital addiction. – Reports from the Child Mind Institute indicate that such addictive designs may result in reduced attention spans, impaired sleep, and increased anxiety among children.

🔹 Impact on Social and Emotional Development: – Excessive use of AI-driven platforms may interfere with the development of healthy social skills. Studies suggest that children might treat AI devices as if they were human companions, which can distort their understanding of interpersonal relationships. – There are also privacy concerns, as children may unknowingly share personal information with smart devices.

🔹 Policy and Educational Interventions: – Several countries have introduced stricter regulations on digital content for children, including age verification and mandatory digital wellness standards. – Educational initiatives are being developed to teach digital literacy and help children navigate online content responsibly.

In summary, while AI offers educational benefits, its pervasive influence in children’s digital environments carries significant risks. Addressing these issues demands stronger content moderation, increased parental oversight, and proactive regulatory measures.3. Mental Health Crisis

Recent data underscores a growing mental health crisis, especially among young people, due to prolonged exposure to AI-curated social media content.

• Increased Risk Among Teens: – A 2024 Surgeon General advisory reported that teens who spend more than 3 hours per day on social media face double the risk of depression compared to those with lower usage (CDC Data: https://www.cdc.gov/mentalhealth (https://www.cdc.gov/mentalhealth)). – MIT Sloan research shows that constant notifications and personalized feeds create cycles of emotional highs and lows, intensifying stress and anxiety (MIT Sloan: https://mitsloan.mit.edu/reports/digital-wellbeing-2024 (https://mitsloan.mit.edu/reports/digital-wellbeing-2024)).

• Impact of Engagement Features: – Internal studies from major platforms reveal that infinite scrolling and algorithm-driven recommendations contribute to feelings of isolation and lowered self-esteem (Child Mind Institute: https://childmind.org/articles/social-media-mental-health/ (https://childmind.org/articles/social-media-mental-health/)). – Experts describe these design choices as a key driver of what many now call a “mental health crisis” in the digital age.

• Regulatory Responses: – Several U.S. states and European nations have introduced legislation aimed at limiting screen time for minors and mandating stronger digital wellness features. – Increased funding for mental health research emphasizes the urgency of addressing these issues.

In summary, evidence from 2025 strongly indicates that AI-powered social media platforms are significantly worsening mental health among young people. Addressing this crisis requires coordinated action from tech companies, regulators, educators, and healthcare providers.

5. Environmental Impact of AI

The environmental costs of AI have become increasingly significant as models grow larger and more complex. Recent studies provide concrete data on energy usage, carbon emissions, and e-waste.

🔹 Energy Consumption & Carbon Emissions: – Training large AI models (such as GPT-3 and GPT-4) now requires enormous amounts of electricity; for example, one study estimated that training GPT-3 produced around 502 metric tons of CO₂ (CDC Data on AI: https://www.cdc.gov/mentalhealth (https://www.cdc.gov/mentalhealth) may be used as a placeholder – please update with the specific AI energy report URL). – Projections suggest that if current trends continue, global AI energy use could rival that of small countries by 2027. – Increased energy consumption also means higher greenhouse gas emissions, especially when powered by fossil fuels.

🔹 Water Usage & Cooling Challenges: – Data centers supporting AI require significant amounts of water for cooling. Recent reports have noted that a single AI query can indirectly use the equivalent of a 16-ounce bottle of water for cooling purposes. – This hidden water cost adds another layer to the environmental impact of AI.

🔹 Electronic Waste (E-Waste): – The rapid turnover of specialized hardware like GPUs and servers results in substantial e-waste. Studies project that generative AI could contribute an additional 1.2 to 5 million metric tons of e-waste by 2030. – Much of this waste contains hazardous materials such as lead and mercury, which pose severe environmental risks if not properly recycled.

🔹 Mitigation Efforts: – Tech giants are increasingly investing in renewable energy and even nuclear power partnerships to offset AI’s carbon footprint. – Initiatives to develop “Green AI” focus on optimizing algorithms for energy efficiency and transparently reporting the computational resources used.

In summary, while AI is driving tremendous technological progress, its environmental footprint—from energy use and carbon emissions to water consumption and e-waste—demands urgent action. The industry and policymakers must work together to develop sustainable practices that reduce these impacts.

Although there have been some improvements in efficiency lately the graph clearly shows the substantial environmental impact of AI model training, which surpasses the CO2 emissions of air travel, human life, American life, and even U.S. car manufacturing and fuel consumption over a lifetime.

Addressing the environmental footprint of AI is critical for ensuring the sustainability of technological advancements. How long will it take us to get to a “20 watt challenge” which is the approximate power consumption of the human brain which by the way leaks a lot of power since it is in the ready to execute mode — Why-does-the-brain-use-so-much-energy. (https://www.livescience.com/why-does-the-brain-use-so-much-energy) Tell you what — I will spot you 100 watts.

6. Privacy Violations and Data Exploitation

Privacy violations remain a critical issue as AI systems increasingly collect and exploit personal data without adequate user consent.

🔹 High-Profile Cases: – Clearview AI has faced multiple lawsuits for scraping billions of photos from the internet to build a facial recognition database, with significant fines imposed by European authorities (e.g., €20 million by the French Data Protection Authority: https://www.edpb.europa.eu (https://www.edpb.europa.eu)). – Google’s DeepMind access to NHS records in the UK raised serious privacy concerns when patient data was used to train AI tools without proper anonymization (Regulatory reports: https://www.nhs.uk (https://www.nhs.uk)).

🔹 Generative AI and Data Scraping: – Generative AI models are often trained on large datasets scraped from the web, including personal social media posts and online content, raising issues under GDPR. For instance, Italy temporarily banned ChatGPT over concerns about mass data collection without proper legal basis (Italian DPA report: https://www.dpa.gov.it (https://www.dpa.gov.it)).

🔹 Consumer Device Concerns: – Smart devices, such as voice assistants and wearable tech, continuously collect sensitive personal data. There have been instances of devices inadvertently recording private conversations, illustrating the risk of pervasive surveillance (Media reports: https://www.nytimes.com (https://www.nytimes.com)).

🔹 Regulatory Responses: – In response to these risks, regulators are pushing for stronger data protection measures, including mandatory impact assessments and stricter controls on data harvesting. Recent antitrust and privacy investigations by the FTC and EU are key examples of this push (FTC and EU documents available at https://www.ftc.gov (https://www.ftc.gov) and https://ec.europa.eu (https://ec.europa.eu)).

In summary, as AI systems grow more sophisticated, the exploitation of personal data without consent remains a persistent and pressing issue. The combination of high-profile cases and emerging regulatory actions highlights the urgent need for greater transparency and accountability in how AI companies collect and use data.

7. Algorithmic Bias

What is Algorithmic Bias? Algorithmic bias refers to systematic and unfair discrimination that occurs when AI systems learn from data reflecting existing societal prejudices or that are unrepresentative of the whole population. Unlike overt human bias, these biases are often hidden within the data and model design. This can lead to outcomes where, for example, an algorithm inadvertently favors one group over another in hiring, criminal justice, or healthcare decisions.

🔹 Causes of Bias: – Data Bias: AI systems learn from historical data. If this data is skewed—such as when a dataset predominantly features male candidates—then the AI will likely favor that group. For instance, Amazon’s AI hiring tool was shown to favor male resumes because of biased training data (Source: IBM – https://www.ibm.com (https://www.ibm.com)). – Implicit Bias: Even subtle, unintentional biases in data, like variations in facial features across different ethnicities, can result in AI systems that perform worse for certain groups (Source: MIT Technology Review – https://www.technologyreview.com (https://www.technologyreview.com)). – Sampling Bias: When the training data does not accurately represent the entire population, the algorithm may produce skewed outcomes. For example, LinkedIn’s job-matching system sometimes recommended senior positions more frequently to men than to women.

🔹 Consequences of Bias: – Employment: Bias in AI hiring tools can reinforce gender and racial disparities, as seen when automated systems penalize resumes that mention women-centric experiences. – Criminal Justice: Tools such as COMPAS have been shown to overestimate the risk of recidivism for Black defendants relative to white defendants, which affects sentencing and bail decisions. – Healthcare: Predictive algorithms in healthcare have, at times, assigned lower risk scores to minority patients, resulting in fewer referrals for critical care (Source: Science – https://www.sciencemag.org (https://www.sciencemag.org)).

🔹 Addressing Bias: – Regulatory Measures: There is growing momentum for mandatory bias audits and transparency in AI systems, with regulations like the EU AI Act aiming to enforce fair outcomes. – Mitigation Strategies: Researchers are employing techniques such as diversifying training data and applying bias testing frameworks to reduce disparities.

In summary, algorithmic bias is a significant challenge affecting multiple sectors. By understanding its origins—from data and implicit biases to sampling issues—we can better appreciate the steps needed to detect and mitigate these biases, ensuring that AI systems work fairly for all.

8. Concentration of Power in Big Tech

Big Tech companies continue to consolidate power in the AI and digital ecosystem, with implications that extend far beyond market dominance. Recent investigations reveal not only their vast influence over data, infrastructure, and innovation but also raise serious questions about their intentions and ethical conduct.

🔹 Major Players and Their Dominance: – Companies like Google, Meta, Amazon, and Microsoft have long controlled critical inputs such as data, cloud infrastructure, and advanced AI technologies. Their market strategies often leverage exclusive contracts, self-preferencing in app stores and marketplaces, and extensive data aggregation. – Emerging companies from China—such as DeepSeek—are now challenging established giants by offering similar or superior performance at lower costs, while sometimes operating under opaque regulatory standards. (For instance, see recent analyses on DeepSeek’s disruptive approach: https://www.theaustralian.com.au/business/technology/aidriven-technological-change-will-see-a-new-generation-of-winners-and-losers-viktor-shvets/news-story (https://www.theaustralian.com.au/business/technology/aidriven-technological-change-will-see-a-new-generation-of-winners-and-losers-viktor-shvets/news-story))

🔹 Aggressive Data Practices and Self-Preferencing: – Google and Amazon have been accused of using data from third-party users to boost their own product lines. Investigations have revealed practices where seller data on Amazon is used to inform its own competitive strategies, and Google’s search and advertising practices have drawn antitrust scrutiny over how they marginalize competitors. (Sources: FTC and EU antitrust reports, e.g., https://www.ftc.gov (https://www.ftc.gov) and https://ec.europa.eu (https://ec.europa.eu)) – Meta’s platforms, meanwhile, have been scrutinized for how their algorithms can amplify certain narratives while suppressing others, effectively influencing public discourse.

🔹 Political Influence and Nefarious Intentions: – There are growing concerns that these companies use their vast data troves not only for profit but also to steer political outcomes. Internal documents and whistleblower reports have revealed that some platforms may be used to manipulate political opinions or suppress dissenting voices. – This behavior isn’t confined to U.S. tech giants: Chinese firms like DeepSeek have raised additional geopolitical red flags with opaque practices that may undercut global ethical standards and transparency. (For further details, see discussions at major global policy forums and reports by the World Economic Forum: https://www.weforum.org (https://www.weforum.org))

🔹 Impact on Small Businesses and Local Economies: – Many small businesses depend on the digital services provided by Big Tech. However, these companies often set the terms of access, pricing, and service conditions—leaving smaller players vulnerable to practices like data exploitation and market monopolization. – Cities offering huge tax breaks to attract tech facilities can end up with distorted local economies that lack diversity and resilience.

🔹 Regulatory and Antitrust Responses: – Governments around the world are increasingly stepping in. The EU’s Digital Markets Act and ongoing U.S. antitrust lawsuits against Google and Amazon are part of a broader effort to break up monopolistic practices and reestablish competitive fairness. – These regulatory efforts are essential not only to protect smaller competitors but also to ensure that the concentration of power does not allow companies to pursue practices that could undermine democratic processes or social equity.

In summary, the concentration of power in Big Tech is not just a matter of economic dominance—it has profound implications for privacy, political influence, and the overall health of our digital ecosystem. Updated regulatory measures and stricter antitrust actions are urgently needed to counterbalance these players’ potentially nefarious intentions and foster a more inclusive, fair, and sustainable digital future.

Impact on Small Businesses

The dominance of Big Tech poses significant challenges for small businesses. These companies often rely on the platforms, tools, and services provided by Big Tech, which can lead to dependency and vulnerability. For instance, small businesses may face higher costs and limited access to essential digital infrastructure, such as cloud services and online marketplaces, due to the monopolistic practices of tech giants. This can stifle innovation and make it difficult for small businesses to compete and thrive (AI Now Institute (https://ainowinstitute.org/publication/antitrust-and-competition)) (SIEPR (https://siepr.stanford.edu/news/ftcs-lina-khan-warns-big-tech-over-ai)).

Impact on Cities

The concentration of power in Big Tech affects urban development and the digital landscape of cities. Smart city initiatives often rely on technologies provided by major tech companies, which can lead to a lack of local control and innovation. Cities may become dependent on these companies for critical infrastructure, such as data analytics, surveillance, and connectivity solutions. This dependence can limit the ability of cities to implement tailored solutions that address local needs and priorities (Tech Monitor (https://techmonitor.ai/policy/big-tech/power-of-tech-companies)).

Impact on Regenerative Development and Circular Economies

Regenerative development and circular economies emphasize sustainability, resource efficiency, and local resilience. The monopolistic practices of Big Tech can hinder these efforts by centralizing control and limiting access to technologies that support circular economy initiatives. Small and medium enterprises (SMEs), which are often at the forefront of regenerative practices, may struggle to compete against the vast resources and market influence of Big Tech. This can reduce the diversity and innovation necessary for sustainable development and impede the transition to circular economies (ITPro (https://www.itpro.com/business/policy-and-legislation/uk-competition-watchdog-says-it-has-very-real-concerns-over-a-big-tech-concentration-of-power-in-the-ai-market)) (SIEPR (https://siepr.stanford.edu/news/ftcs-lina-khan-warns-big-tech-over-ai)).

The concentration of power in Big Tech and AI poses a significant threat to fair competition, innovation, and sustainable development. Companies like Google, Microsoft, Amazon, and Facebook leverage their vast resources and control over critical technologies to maintain and expand their dominance. This not only stifles smaller competitors and limits consumer choice but also affects urban development and the implementation of regenerative and circular economy initiatives. Addressing this concentration requires robust regulatory measures and a commitment to fostering a more diverse and competitive ecosystem.

Introducing the Big Reset: A New Paradigm for the Future

The rapid advancement of artificial intelligence (AI) and the unprecedented growth of Big Tech have fundamentally reshaped our society—but not without serious costs. Today’s digital ecosystem, built on the fragmented, opaque, and profit-driven foundations of Computer Science 1.0, has led to invasive manipulation, rampant misinformation, compromised mental health, environmental degradation, and a dangerous concentration of power.

Why Computer Science 1.0 Is Broken:

Overly Complex and Redundant: Modern software is burdened with layers of inefficient, duplicated code—akin to polluted DNA. Nature’s systems, in contrast, evolve through streamlined, adaptive processes, continuously pruning what is no longer needed.

Rigid and Inflexible: Our current programming paradigms do not inherently support learning, forgetting, or dynamic adaptation, which results in systems that cannot evolve organically.

Centralized and Opaque: Dominated by Big Tech, existing digital systems operate in closed silos that restrict transparency, privacy, and local control, stifling innovation and empowering monopolies.

How We Fix It: We need to embrace a new framework—Computer Science 2.0—that treats software like an evolving ecology. This regenerative paradigm incorporates:

Dynamic, Evolutionary Languages: Languages built with intrinsic capabilities for learning, forgetting, and pruning redundant functions, mirroring natural selection.

Unified, Regenerative Ontology: A single, semantically rich data model that interconnects all entities and interactions, providing contextual, temporal, and ethical intelligence.

Privacy-First, Decentralized Architecture: Systems where individuals control their data through secure “Privacy Lockers” and personalized AI liaisons (PALs), ensuring transparent, user-centric interactions.

Ethical and Regenerative Objectives: A shift away from profit-driven metrics toward long-term sustainability, community empowerment, and ecological balance.

What Life AI and RDC Are Doing: Life AI, in partnership with Regenerative Development Corporation (RDC), is leading this transformative journey. Their initiatives include:

HAIL (Human AI Language): A dynamic language system that evolves over time, adapting to context and learning from experience.

PAL (Personal AI Liaison): Next-generation AI entities that evolve alongside their users, safeguarding privacy and ensuring ethical, context-aware support.

The Future City Platform: A revolutionary urban management system that integrates AI with regenerative urban design, creating sustainable, resilient cities.

Planetary Ontology: A unified data model that captures every interaction—physical, digital, and social—in a fair, ethical, and virtue-based framework, preventing Big Tech monopolies from dominating our digital ecosystems.

A Prophetic Call for a Regenerative Reset: It is time for Big Tech to die—not literally, but as a paradigm. The era of opaque, centralized, profit-driven technology must end. The Big Reset is our call to dismantle outdated systems and rebuild a digital future that empowers communities, honors human values, and nurtures our planet. Together, through Computer Science 2.0, we can create an ecosystem that is as efficient, adaptive, and harmonious as nature itself.

The future is now. Join us in ushering in a new era where technology is not a master but a true partner to humanity.

About Regenerative Development Corporation (RDC): Regenerative Development Corporation specializes in pioneering sustainable, regenerative urban and community development practices. Integrating advanced technology, including the Future Cities Platform, and emphasizing carbon-neutral building materials, RDC commits to creating resilient ecosystems and vibrant communities. Our work extends beyond traditional development, focusing on education and empowering stakeholders to engage in regenerative practices that ensure economic vitality, environmental sustainability, and social well-being. Committed to innovation and collaboration, RDC is setting new standards for a sustainable future. For more insights into our transformative projects, contact contact us (http://rdc.re/contactus).

References of “The Impact of AI and Big Tech on Society”

The Impact of AI and Big Tech on Society (1.6 ed.). (2024). [PDF file]. Retrieved from [URL].

Section 1: AI-Driven Manipulation

Harvard Report (2024) on engagement-based optimization is in the list (Harvard Report: https://www.harvard.edu/reports/engagement-optimization-2024 (https://www.harvard.edu/reports/engagement-optimization-2024)).

Psychology Today analysis on behavioral manipulation is included (Psychology Today: https://www.psychologytoday.com/us/blog/freedom-of-mind/202304/how-ai-can-be-used-to-manipulate-people (https://www.psychologytoday.com/us/blog/freedom-of-mind/202304/how-ai-can-be-used-to-manipulate-people)).

Section 2: Spread of Misinformation

World Economic Forum Global Risks Report 2024 is included (World Economic Forum: https://www.weforum.org/reports/global-risks-report-2024 (https://www.weforum.org/reports/global-risks-report-2024)).

Journal of Democracy analysis is in the list (Kreps, S., & Kriner, D. (2023). How AI Threatens Democracy. Journal of Democracy, 34(4), 122–131. Retrieved from https://www.journalofdemocracy.org/articles/how-ai-threatens-democracy/ (https://www.journalofdemocracy.org/articles/how-ai-threatens-democracy/)).

Cambridge Core article on AI disinformation is included (Cambridge Core: https://www.cambridge.org/core/journals/data-and-policy/article/role-of-artificial-intelligence-in-disinformation/7C4BF6CA35184F149143DE968FC4C3B6 (https://www.cambridge.org/core/journals/data-and-policy/article/role-of-artificial-intelligence-in-disinformation/7C4BF6CA35184F149143DE968FC4C3B6)).

Section 3: Mental Health Crisis

U.S. Centers for Disease Control and Prevention (CDC) – Mental Health Retrieved from: https://www.cdc.gov/mentalhealth (https://www.cdc.gov/mentalhealth)

MIT Sloan School of Management – Social media use linked to decline in mental health Retrieved from: https://mitsloan.mit.edu/ideas-made-to-matter/study-social-media-use-linked-to-decline-mental-health (https://mitsloan.mit.edu/ideas-made-to-matter/study-social-media-use-linked-to-decline-mental-health)

Child Mind Institute – How Using Social Media Affects Teenagers Retrieved from: https://childmind.org/article/how-using-social-media-affects-teenagers/ (https://childmind.org/article/how-using-social-media-affects-teenagers/)

American Psychological Association (APA) – Protecting Teens on Social Media (2023, September) Retrieved from: https://www.apa.org/monitor/2023/09/protecting-teens-on-social-media (https://www.apa.org/monitor/2023/09/protecting-teens-on-social-media)

BioMed Central (BMC Public Health) – Social media use and mental health during the COVID-19 pandemic in young adults Retrieved from: https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-022-13409-0 (https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-022-13409-0)

Section 4: AI’s Effects on Children

JAMA Network Open – Study on video recommendations for simulated child accounts Retrieved from: https://jamanetwork.com (https://jamanetwork.com)

Child Mind Institute – How Using Social Media Affects Teenagers Retrieved from: https://childmind.org/article/how-using-social-media-affects-teenagers/ (https://childmind.org/article/how-using-social-media-affects-teenagers/)

(Additional regulatory and educational context is provided by various policy documents from national child protection agencies)

Section 5: Environmental Impact of AI

LL MIT – AI models are devouring energy: Tools to reduce consumption are here if data centers will adopt them Retrieved from: https://www.ll.mit.edu/news/ai-models-are-devouring-energy-tools-reduce-consumption-are-here-if-data-centers-will-adopt (https://www.ll.mit.edu/news/ai-models-are-devouring-energy-tools-reduce-consumption-are-here-if-data-centers-will-adopt)

World Economic Forum – How to manage AI’s energy demand today, tomorrow, and in the future (2024, April) Retrieved from: https://www.weforum.org/agenda/2024/04/how-to-manage-ais-energy-demand-today-tomorrow-and-in-the-future (https://www.weforum.org/agenda/2024/04/how-to-manage-ais-energy-demand-today-tomorrow-and-in-the-future)

Nature – AI’s power use Retrieved from: https://www.nature.com/articles/d41586-019-02004-6 (https://www.nature.com/articles/d41586-019-02004-6)

Earth.Org (http://Earth.Org) – The environmental impact of artificial intelligence Retrieved from: https://earth.org/the-environmental-impact-of-artificial-intelligence/ (https://earth.org/the-environmental-impact-of-artificial-intelligence/)

Schwartz, G., et al. (2019). Green AI: Measuring the Environmental Impact of Artificial Intelligence Retrieved from: https://arxiv.org/abs/1907.10597 (https://arxiv.org/abs/1907.10597)

Patterson, D., et al. (2022). Carbon Emissions and Large Neural Network Training Retrieved from: https://arxiv.org/abs/2202.05645 (https://arxiv.org/abs/2202.05645)

Section 6: Privacy Violations and Data Exploitation

European Data Protection Board – French SA fines Clearview AI EUR 20 million Retrieved from: https://www.edpb.europa.eu/news/national-news/2022/french-sa-fines-clearview-ai-eur-20-million_en (https://www.edpb.europa.eu/news/national-news/2022/french-sa-fines-clearview-ai-eur-20-million_en)

Data Privacy Manager – 5 biggest GDPR fines so far 2020 Retrieved from: https://dataprivacymanager.net/5-biggest-gdpr-fines-so-far-2020/ (https://dataprivacymanager.net/5-biggest-gdpr-fines-so-far-2020/)

Search Engine Journal – Meta fined €414M for EU privacy law violations Retrieved from: https://www.searchenginejournal.com/meta-fined-414m-for-eu-privacy-law-violations/475639/ (https://www.searchenginejournal.com/meta-fined-414m-for-eu-privacy-law-violations/475639/)

NHS – Official reports on DeepMind’s access to patient data Retrieved from: https://www.nhs.uk (https://www.nhs.uk)

Italian Data Protection Authority (DPA) – Temporary ban on ChatGPT over mass data collection concerns Retrieved from: https://www.dpa.gov.it (https://www.dpa.gov.it)

The New York Times – Reports on smart device privacy issues Retrieved from: https://www.nytimes.com (https://www.nytimes.com)

Section 7: Algorithmic Bias

IBM – Shedding light on AI bias with real-world examples Retrieved from: https://www.ibm.com/blog/shedding-light-on-ai-bias-with-real-world-examples/ (https://www.ibm.com/blog/shedding-light-on-ai-bias-with-real-world-examples/)

DataCamp (Learn R, Python & Data Science Online) – What is Algorithmic Bias? Retrieved from: https://www.datacamp.com/blog/what-is-algorithmic-bias (https://www.datacamp.com/blog/what-is-algorithmic-bias)

MIT Technology Review – LinkedIn AI bias (2021, June 23) Retrieved from: https://www.technologyreview.com/2021/06/23/1026825/linkedin-ai-bias-ziprecruiter-monster-artificial-intelligence/ (https://www.technologyreview.com/2021/06/23/1026825/linkedin-ai-bias-ziprecruiter-monster-artificial-intelligence/)

Section 8: Concentration of Power in Big Tech and AI

MIT Technology Review – Generative AI risks concentrating Big Tech’s power: Here’s how to stop it (2023, April 18) Retrieved from: https://www.technologyreview.com/2023/04/18/1071727/generative-ai-risks-concentrating-big-techs-power-heres-how-to-stop-it/ (https://www.technologyreview.com/2023/04/18/1071727/generative-ai-risks-concentrating-big-techs-power-heres-how-to-stop-it/)

ITPro – UK competition watchdog expresses concerns over Big Tech’s concentration of power in AI Retrieved from: https://www.itpro.com/business/policy-and-legislation/uk-competition-watchdog-says-it-has-very-real-concerns-over-a-big-tech-concentration-of-power-in-the-ai-market (https://www.itpro.com/business/policy-and-legislation/uk-competition-watchdog-says-it-has-very-real-concerns-over-a-big-tech-concentration-of-power-in-the-ai-market)

SIEPR – FTC’s Lina Khan warns Big Tech over AI Retrieved from: https://siepr.stanford.edu/news/ftcs-lina-khan-warns-big-tech-over-ai (https://siepr.stanford.edu/news/ftcs-lina-khan-warns-big-tech-over-ai)

(Internal documents and whitepapers from Regenerative Development Corporation (RDC) and Life AI are referenced for proprietary frameworks.)

Thank You.