AI 2023: The Ticking Time Bomb of 2005's Social Media Errors

Are We Truly Wiser, or Will We Sleepwalk Again?

Author

AI Enterprise Wiz
May 20, 2023

The mistakes of social media 2005 ghost us.

  • Is social media all a dark tale?

  • Is it all a tale of regrets?

Absolutely not, the story is never one-sided. Social media weaves both sorrows and joys of human experience.

But that's not the narrative here.

Why?

Because today, a new more powerful sheriff is in town.

and

It’s now in the hands of everyday people.

AI 2023 is People’s Darling. 

  • Richly Deserved. Kudos. Well Done.

  • Humanity's gains? Absolutely confirmed.

  • Wield it wisely, and our lives shall thrive.

  • Havoc potential? Locked and loaded.

  • Unrestrained, we endure the consequences.

Critical question: Will we sleepwalk and repeat the mistakes of social media 2005?

OR

Can we learn from the past and be wiser this time around?

The clock is ticking.

The choice is ours.

Harsh Realities of Social Media

Social media's abundance of mistakes is overwhelming. It's a toxic blend of

  • spreading falsehoods

  • manipulative tactics

  • invasive privacy breaches

  • addictive traps

  • divisions within society.

1. The Dark Art of Attention Manipulation

Social media's appeal:

  • stunning visuals [24]

  • addictive alerts [25]

  • captivating algorithms [26]

that keep us hooked.

This cycle thrives on emotional tactics [27]:

  • personalized content

  • persuasive messaging

  • click-worthy headlines [28]

  • calculated viral campaigns [29]

Teaming up with social media experts, ChatGPT revolutionizes engagement,

  • crafting compelling content

  • igniting meaningful conversations

  • extracting invaluable insights from user data.

Now,we need to turn the tide. Prioritize users.

Unleash ChatGPT for a social media experience that champions happiness and well-being.

2. Sharing Wrong Information and Fake News

Social media platforms spread fake news rapidly [31], leading to increased disagreements among people and potentially undermining democratic processes [32].

It’s vital to equip AI systems like ChatGPT and LLM with effective measures to halt the dissemination of false information and ensure that content originates from reliable sources.

3. Privacy and Using Data Wrongly

The rise of social media platforms exposed critical issues of privacy infringement and data mishandling [33].

Instances of unauthorized access [34], data mining, and third-party sharing highlight strikingly the pressing need for responsible data management in AI applications like ChatGPT and LLM.

4. Hurting Mental Health

Social media’s addictive nature fuels heightened anxiety [35], deepening depression [36], and eroding self-esteem [37].

With ChatGPT and LLM, it is crucial to emphasize promoting positive experiences and safeguarding mental health.

5. Not Controlling Content Well

Lessons from Social Media’s unchecked spread of hate speech, bullying, and violent content serves as a crucial reminder of the urgent need for stringent content control with ChatGPT and LLM.

Lessons for LLM Applications

A. Good Design and Responsible AI

Respect users' rights and prevent harm. This includes:

  • Clear AI Explanations: Use techniques like LIME or SHAP to explain AI decisions. Provide clear documentation or interfaces [9].

  • AI Accountability: Strong auditing processes and clear AI use guidelines within the organization [10].

  • Fairness and Impartiality: Use fairness metrics and regularly test AI systems for fairness [11].

  • Bias Mitigation: Careful data collection and preprocessing practices. Apply techniques like oversampling or bias correction algorithms to reduce bias [12].

  • Regular AI Checks: 24/7 monitor and evaluate AI systems. Track performance, collect user feedback, and ensure alignment with ethical guidelines [13].

B. Protecting Privacy and Handling Data

To address privacy concerns and data misuse, ChatGPT and LLM apps should:

  • Strong Privacy: Protect user data with encryption and let users control their privacy settings [14].

  • Safe Data Handling: Use robust security measures like secure storage, regular audits, and strong access controls to keep user data safe [15].

  • Clear Consent: Provide user-friendly consent mechanisms that clearly inform users about data use and offer opt-in or opt-out choices [16].

  • Transparent Practices: Keep users informed about privacy rules and any changes through clear policies and regular communication [17].

C. User Control and Power

Drawing from social media's impact, these applications should prioritize:

  • Customizable Interactions: Enable users to tailor AI systems to their needs and preferences with customizable settings and interfaces [18].

  • User Data Control: over their data usage and sharing via opt-in/opt-out mechanisms and data deletion options [19].

  • AI Transparency: Offer clear, user-friendly explanations about AI operations through documentation, tutorials, or in-system explainability features [20].

D. Responsible Information Sharing

To stop the spread of wrong information, these apps should focus on:

  • Fact-checking: through automated tools and human moderation to ensure information accuracy [21].

  • Source Verification: Employ trusted databases and machine learning to confirm source credibility [22].

  • User Communication: Clearly explain fact-checking and verification practices through user-friendly guides [23].

Reflecting on social media saga was enlightening.

Important lessons exist.

So.

How will we now shape the development and use of ethical ChatGPT and LLM apps?

Decision is ours.

We have the knowledge, the tools, and the choice.

Will we prioritize

  • superior design

  • user well-being,

  • robust privacy protection

  • vigilant content control

  • responsible information sharing?

OR

Will we sleepwalk and rewrite the social media storybook, again?

References

  1. McNamee, R. (2019). Zucked: Waking Up to the Facebook Catastrophe.

  2. "The Social Dilemma" (documentary film).

  3. Kirkpatrick, D. (2010). Vanity Fair

  4. Sunstein, C. R. (2017). #Republic: Divided Democracy in the Age of Social Media.

  5. Tufekci, Z. (2017). Twitter and Tear Gas: The Power and Fragility of Networked Protest.

  6. O'Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.

  7. Cadwalladr, C. (2019, April). The Great British Brexit Robbery: How our Democracy was Hijacked.

  8. Ghoshal, N. (2019, July). "YouTube's recommendation algorithm amplifies extreme content, study finds." The Guardian.

  9. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable" by Christoph Molnar

  10. Accountability in Algorithmic Decision Making by Mulligan, Koopman, and Doty.

  11. Fairness and Abstraction in Sociotechnical Systems by Selbst et al.

  12. Data Preprocessing Techniques for Classification without Discrimination by Kamiran and Calders.

  13. Ethics of Artificial Intelligence and Robotics by Vincent C. Müller in Stanford Encyclopedia of Philosophy.

  14. Encryption Works: How to Protect Your Privacy in the Age of NSA Surveillance by Freedom of the Press Foundation.

  15. Data Security: Top Threats to Data Protection by the International Association of Privacy Professionals.

  16. Guidelines on Consent under Regulation 2016/679 by the Article 29 Data Protection Working Party.

  17. Transparency as a Fundamental Right and Key Principle in Data Protection Law by the International Association of Privacy Professionals.

  18. Personalization in Human-Computer Interaction by Alfred Kobsa.

  19. Privacy and Data Management Control in the Age of Big Data by R. Agrawal and R. Srikant.

  20. Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI by Riccardo Guidotti et al.

  21. Automated Fact-Checking: The State of the Art and Perspectives by Giovanni Da San Martino et al.

  22. Source Credibility: A New Machine Learning Approach by A. B. Ayed et al.

  23. The Role of Transparency in Interactive User Assistance Systems by Malin Eiband et al.

  24. Bakhshi, S., Shamma, D. A., & Gilbert, E. (2014). Faces engage us: Photos with faces attract more likes and comments on Instagram. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.

  25. Thorisdottir, I. E., Sigurvinsdottir, R., Asgeirsdottir, B. B., Allegrante, J. P., & Sigfusdottir, I. D. (2019). Active and passive social media use and symptoms of anxiety and depressed mood among Icelandic adolescents.

  26. Cyberpsychology, Behavior, and Social Networking, 22(8), 535-542.

  27. Pariser, E. (2011). The filter bubble: How the new personalized web is changing what we read and how we think. Penguin.

  28. Matz, S. C., Kosinski, M., Nave, G., & Stillwell, D. J. (2017). Psychological targeting as an effective approach to digital mass persuasion. Proceedings of the National Academy of Sciences, 114(48), 12714-12719.

  29. Chen, Y., Conroy, N. J., & Rubin, V. L. (2015). Misleading online content: Recognizing clickbait as false news. Proceedings of the 2015 ACM on Workshop on Multimodal Deception Detection, 15-19.

  30. Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192-205.

  31. Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146-1151.

  32. Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2), 211-36.

  33. Cadwalladr, C., & Graham-Harrison, E. (2018). Revealed: 50 million Facebook profiles harvested for Cambridge Analytica in major data breach. The Guardian.

  34. Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs.

  35. Vannucci, A., Flannery, K. M., & Ohannessian, C. M. (2017). Social media use and anxiety in emerging adults. Journal of Affective Disorders, 207, 163-166.

  36. Lin, L. Y., Sidani, J. E., Shensa, A., Radovic, A., Miller, E., Colditz, J. B., ... & Primack, B. A. (2016). Association between social media use and depression among US young adults. Depressive and Anxiety Disorders, 33(4), 323-331.

  37. Fardouly, J., Diedrichs, P. C., Vartanian, L. R., & Halliwell, E. (2015). Social comparisons on social media: the impact of Facebook on young women's body image concerns and mood. Body Image, 13, 38-45.