Ethical Intelligence in the Era of Al: Navigating the Post-Turing Landscape#

The rapid advancement of artificial intelligence (Al) has ignited a global conversation about its potential benefits and inherent risks. The unease expressed by authors in London regarding the alleged unauthorized use of their work to train Al models underscores a growing concern within the creative ecosystem. This is not an isolated incident, but rather a symptom of a larger challenge: how to ethically integrate increasingly sophisticated Al into the fabric of our society, particularly within creative and political spheres where human values and rights are paramount. The deployment of Al in support of regimes committing atrocities further amplifies the urgency of establishing ethical boundaries for this powerful technology. It is no longer a question of whether unchecked Al will significantly impact these ecosystems, but rather how quickly and with what consequences. This paper will delve into the concept of “Ethical Intelligence” in the context of Al that is reaching, and in some interpretations, surpassing human-level conversational abilities, as symbolized by the Turing Test.

While the term “Ethical Intelligence” lacks a singular, universally accepted definition, it can be understood by examining the well-established field of Al ethics. Al ethics is a multidisciplinary area of study focused on optimizing the beneficial impact of Al while mitigating potential risks and adverse outcomes.¹ This field encompasses principles that govern Al behavior based on human values, including fairness, transparency, accountability, privacy, and security.² Therefore, Ethical Intelligence in Al can be conceptualized as the capacity of an Al system to not only demonstrate human-like conversational abilities, potentially passing the Turing Test, but also to operate in accordance with these established ethical principles and human values. This distinction is critical because an Al might convincingly mimic human conversation without possessing any inherent ethical understanding or moral compass.

The notion of Al reaching or surpassing human-level conversational abilities, as suggested by some interpretations of recent progress in large language models (LLMs), marks a crucial point for ethical considerations.⁷ If Al can convincingly simulate human dialogue, it blurs the lines between human and machine, raising profound ethical questions about trust, deception, and the potential for misuse.¹⁰ The very premise of the user’s query highlights the accelerating impact of Al on creative and political ecosystems, emphasizing the immediate need to address the ethical implications. This paper will explore the ethical challenges arising from Al’s advanced capabilities in the creative and political domains. It will focus on the complex issues surrounding copyright and intellectual property, the multifaceted impact on creators, the significant risks of political manipulation and surveillance, and the pressing need for effective regulatory and ethical frameworks. The central argument of this report is that the ongoing development and widespread deployment of Al, particularly in this post-Turing Test era, demands a strong and unwavering emphasis on ethical considerations. This is essential to proactively prevent potential harm and ultimately ensure a future where technological innovation is thoughtfully balanced with fundamental accountability and the safeguarding of human values.

The Turing Test, proposed by Alan Turing as an “imitation game,” has served for decades as a benchmark for assessing a machine’s ability to exhibit intelligent behavior equivalent to that of a human.⁸ The test involves a human evaluator engaging in text-based conversations with both a human and a machine, attempting to discern which is which.⁸ While the first reported instance of a computer program passing a version of the Turing Test occurred in 2014, with the program “Eugene Goostman” convincing a portion of judges that it was a 13-year-old boy, the validity and rigor of such early claims have been subject to considerable debate.¹³, ¹⁴ Critics have often argued that these instances involved specific setups or relied on the program’s ability to feign ignorance or non-nativeness to mask its artificial nature.¹⁵ The Turing Test, in its original conception and subsequent interpretations, primarily measures a machine’s capacity to mimic human conversation and may not necessarily reflect genuine intelligence or consciousness.¹²

Recent advancements in the field of large language models (LLMs) have led to claims that Al has now surpassed more rigorous versions of the Turing Test.¹⁷ Studies conducted in early 2025, for example, reported that GPT-4.5, when prompted to adopt a human-like persona, was mistaken for a human by judges a significant percentage of the time, even outperforming actual human participants in some scenarios.⁹ This development raises fundamental questions about our understanding of intelligence and consciousness in the context of Al. Are these advanced models merely sophisticated mimics, expertly trained on vast datasets of human language, or do they possess a form of intelligence that warrants deeper ethical consideration?¹⁶ Some argue that achieving this level of conversational fluency signifies a form of sentience, potentially requiring a reevaluation of existing ethical frameworks to encompass non-human intelligent agents.¹², ¹⁶ However, this perspective is not universally accepted. Drawing on philosophical arguments such as John Searle’s “Chinese Room,” many contend that the ability to produce human-like responses, no matter how convincing, does not inherently equate to genuine understanding, consciousness, or subjective experience.¹⁸

The academic debate surrounding Al sentience and its ethical relevance remains ongoing and complex.¹¹ While current LLMs demonstrate remarkable proficiency in natural language processing and generation, some researchers suggest they may still lack crucial aspects of human cognition, such as deep comprehension of the world, continuous memory across interactions, and the grounding of language in sensory perception.¹², ²⁰ In response to the limitations of the traditional Turing Test as a measure of true intelligence or consciousness, alternative frameworks have been proposed. One such framework is the “NeuroAl Turing Test,” which suggests evaluating Al not only on its behavior but also on whether it produces internal neural representations that are empirically aligned with those of the human brain.²² Another proposed alternative is the “Metacognitive Turing Test,” which focuses on assessing an Al’s capacity for metacognition – its ability to think about its own thinking, reflect on its reasoning processes, and understand its limitations.²¹ The ethical relevance of this debate lies in determining the criteria by which we might ascribe moral consideration or even rights to Al systems in the future. As Al capabilities continue to advance, a deeper understanding of what constitutes intelligence and consciousness, and whether these attributes can genuinely emerge in machines, will be essential for navigating the complex ethical landscape ahead.

The intersection of Al and copyright law has become a particularly contentious ethical minefield, as highlighted by the user’s reference to the protest by authors against Meta [user_query]. The central ethical and legal debate revolves around the use of copyrighted material, such as books, articles, and artwork, to train Al models without the explicit consent or fair compensation of the copyright holders.²³, ²⁷ A fundamental legal question in this context is whether the act of using copyrighted works as training data for Al constitutes “fair use” under existing copyright law.²⁶ This doctrine permits the limited use of copyrighted material without permission for purposes such as criticism, commentary, news reporting, teaching, scholarship, or research.²⁶

Arguments against considering Al training as fair use often emphasize the commercial nature of Al development and the potential for significant market harm to copyright holders.²³, ²⁸ Copyright owners, including authors, artists, and publishers, assert that the unauthorized use of their creative works to train Al models infringes upon their fundamental intellectual property rights and could devalue their work.²⁶, ²⁷ They argue that Al companies are profiting from the use of their creations without providing due compensation.²⁷ Conversely, arguments in favor of fair use often highlight the transformative nature of Al. Proponents suggest that Al models do not directly replicate the copyrighted works they are trained on but rather extract data and patterns to generate entirely new content.²⁶ Some legal scholars propose that text data mining (TDM) practices, especially when conducted for non-profit educational or research purposes, should fall squarely within the scope of fair use.²⁶ However, recent legal rulings, such as the Thomson Reuters v. Ross Intelligence Inc. case, have indicated a less permissive stance, at least in the context of non-generative Al.²⁴, ²⁵, ³⁰, ³¹ In this case, the court found that using copyrighted legal headnotes to train an Al-powered legal search engine did not constitute fair use, particularly because the Al tool directly competed with the copyright owner’s existing services.²³ The court’s analysis focused on the commercial purpose of the use and its potential impact on the market for the copyrighted work.²³ While this ruling specifically addressed non-generative Al, its implications for the ongoing debates surrounding generative Al training are significant.²³

The rapid advancement of Al is having a profound impact on authors, artists, and various other creators concerning their intellectual property and potential for fair compensation.²⁷, ³² Many creators express significant concerns that Al-generated content could lead to a devaluation of their original work and a substantial loss of income.³⁶ There is a widespread belief among artists and authors that current copyright laws are ill-equipped to address the unique challenges posed by generative Al technologies.²⁷, ³⁶ The fear is that the ability of Al to quickly and easily mimic artistic styles and generate vast amounts of content could saturate the market, making it increasingly difficult for human creators to stand out and earn a sustainable living from their creative endeavors.³², ³⁷ This situation is particularly concerning for those who rely heavily on the sale of their art or writing as their primary source of income.³⁶

To address these complex issues, various potential solutions and compensation models for creators are being actively discussed and explored.²⁹ One prominent proposal involves the establishment of comprehensive licensing systems. Under such systems, artists and authors could grant permission for their work to be used in Al training, potentially receiving fair compensation in return.²⁹ This approach mirrors existing licensing models in other creative industries, such as the music industry.²⁹ Other potential models include revenue-sharing mechanisms, where creators receive a portion of the profits generated by Al systems trained on their work, and the development of collective licensing organizations that would manage the rights and distribution of compensation to creators.²⁹ Some creators are also exploring technological solutions aimed at protecting their work from unauthorized use in Al training. For instance, tools like GLAZE have been developed to subtly alter digital artwork in a way that disrupts Al-based imitation while remaining visually imperceptible to humans.³⁷ Ultimately, there is a growing consensus that intellectual property law needs to be significantly reformed to effectively address the specific challenges and ethical considerations arising from the rapid advancement of Al.²⁷, ³⁹ This includes clearly defining the legal distinctions between Al-assisted and fully Al-created works and establishing robust mechanisms for ensuring fair compensation for creators whose original works are utilized in the training of these increasingly powerful artificial intelligence systems.

The integration of Al into the political landscape presents a complex web of opportunities and significant ethical challenges. As highlighted in the user’s query, there are documented instances and growing concerns about Al being utilized in political contexts for purposes such as surveillance and manipulation [user_query]. Numerous reports and studies have detailed how Al technologies, particularly facial recognition systems, are being deployed for political surveillance in various countries.⁴¹, ⁴⁸ For example, China has implemented extensive networks of Al-powered cameras capable of real-time individual identification, often used to monitor public gatherings and suppress dissent.⁴¹ Similarly, Russia has increased its use of Al-driven facial recognition tools to monitor and detain anti-government protesters.⁴¹ In other contexts, Al is being used to monitor social media for signs of dissent, as seen in Egypt and Bahrain, where Al systems analyze online activity to predict and preemptively suppress potential protests.⁴¹ These instances raise serious ethical concerns about the erosion of privacy, freedom of expression, and the potential for abuse of power by governments.⁴², ⁸⁵

Beyond surveillance, Al is also playing an increasingly significant role in political manipulation.⁴³, ⁴⁴ The ability of Al to generate highly realistic deepfakes – including audio, video, and images – has created new avenues for spreading disinformation and influencing public opinion.⁴³, ⁴⁵ Examples abound, from Al-generated audio messages impersonating political figures to dissuade voters⁴³ to manipulated videos designed to smear candidates.⁴³ In the lead-up to elections in various countries, Al has been used to create fake endorsements, spread false information about voting processes, and amplify partisan narratives through networks of bots and automated accounts.⁴¹, ⁴⁶, ⁴⁷ The speed and scale at which Al can generate and disseminate misleading content pose a significant threat to the integrity of democratic processes.⁴⁵

The implications of these developments for democratic processes and fundamental human rights are profound.⁴¹ The use of Al for political surveillance can stifle dissent, create a climate of fear, and undermine the ability of citizens to engage in free and open political discourse.⁴¹ The manipulation of public opinion through Al-generated disinformation can erode trust in legitimate news sources, sow confusion among voters, and ultimately distort election outcomes.⁴³, ⁸⁶ This is particularly concerning given the increasing difficulty in distinguishing between authentic and Al-generated content.⁴³ The deployment of such technologies by authoritarian regimes further exacerbates these concerns, potentially enabling more sophisticated forms of repression and control.⁴²

Table 1: Examples of Al Use in Political Contexts (2024-2025)

As Al technologies become more deeply integrated into various aspects of society, the need for effective governance mechanisms becomes increasingly critical. Several existing and proposed regulatory frameworks aim to address the ethical challenges associated with the development and deployment of Al technologies.⁵⁰ One of the most comprehensive is the European Union’s Al Act, which adopts a risk-based approach to regulation.⁵², ⁵⁷ This act categorizes Al systems based on their potential to cause harm, with stricter requirements for high-risk applications such as those in healthcare, education, and critical infrastructure.⁵² Certain Al practices deemed to pose an unacceptable risk, such as social scoring systems and the untargeted scraping of facial images, are prohibited outright.⁵² The Al Act also includes specific transparency obligations for Al systems with limited risk, such as chatbots and deepfakes.⁵⁶

Another significant framework is the set of Artificial Intelligence Principles developed by the Organisation for Economic Co-operation and Development (OECD).⁵⁰, ⁵⁸, ⁵⁹ First adopted in 2019 and updated in May 2024, these principles promote the innovative and trustworthy use of Al while respecting human rights and democratic values.⁵⁰, ⁶⁰, ⁶¹ The OECD AI Principles are built upon five core values: inclusive growth, sustainable development and well-being; human rights and democratic values, including fairness and privacy; transparency and explainability; robustness, security and safety; and accountability.⁵⁰ Alongside these values, the OECD provides recommendations for policymakers focused on fostering an Al-enabling ecosystem through investment in research and development, building human capacity, and promoting international cooperation.⁵⁰ In contrast to the EU’s more regulatory approach, the United States has adopted a more fragmented landscape, primarily relying on executive orders and sector-specific guidance rather than comprehensive federal legislation.⁴⁹, ⁵³

In addition to formal regulatory frameworks, various organizations and researchers have proposed ethical guidelines for the development and deployment of Al.⁴, ⁶², ⁷⁰ These guidelines often emphasize principles such as fairness and bias mitigation, transparency in decision-making, accountability for outcomes, privacy and data protection, and the safety and security of Al systems.¹⁹, ⁶³ The importance of human oversight in Al systems is also frequently highlighted.⁶² Establishing effective governance mechanisms for Al presents numerous challenges.⁵², ⁷⁵ The rapid pace of technological advancement often outstrips the ability of legal and ethical frameworks to keep pace.³⁹ The inherent complexity of many Al systems can make it difficult to ensure transparency and accountability.⁷³, ⁷⁴ Furthermore, achieving a global consensus on ethical standards for Al remains a significant hurdle, given differing cultural values and regulatory priorities across nations.⁶⁹ Despite these challenges, the development of effective governance mechanisms is crucial for ensuring the responsible and beneficial use of Al. This includes not only establishing clear regulations and ethical guidelines but also fostering a culture of responsibility and accountability among those who develop and deploy Al technologies.⁶³

Transparency and accountability are widely recognized as foundational pillars for the ethical development and deployment of Al systems.¹⁹, ⁶⁵ Transparency in Al refers to the clarity and openness with which Al systems operate, including the disclosure of data sources, algorithms, and decision-making processes.⁷⁸, ⁷⁹ This transparency is essential for building trust among users and stakeholders, as it allows for scrutiny and understanding of how Al systems function and arrive at their conclusions.⁷⁷ Accountability in Al involves establishing clear lines of responsibility for the outcomes and impacts of Al systems, ensuring that developers, deployers, and users can be held responsible for any harm or errors they may cause.⁶⁴ Regulations like the EU AI Act place a strong emphasis on transparency and explainability, particularly for high-risk Al applications, requiring detailed documentation and the ability to provide explanations for Al-driven decisions.⁵⁰, ⁵⁴, ⁵⁵ The OECD AI Principles also underscore the importance of transparency and accountability as key values for trustworthy Al.⁵⁰ By fostering transparency and establishing clear mechanisms for accountability, societies can better navigate the ethical complexities of Al and work towards ensuring its responsible and beneficial integration into the future.⁷⁸

The user’s query raises a critical point about the potential “moral bankruptcy” of the tech elite in the context of Al development, suggesting a concern that the pursuit of technological supremacy and profit might be overshadowing fundamental ethical considerations [user_query]. The concept of “moral bankruptcy” in this context refers to a perceived ethical failing within the technology industry, where the drive for innovation and financial gain may lead to the neglect or downplaying of significant ethical implications associated with powerful Al systems.⁷¹, ⁸⁰, ⁸², ⁸³ There is a growing body of criticism suggesting that profit-driven motives can indeed create tensions with ethical considerations in the development and deployment of Al.⁷¹, ⁷⁶ For instance, concerns have been raised about Al being used to optimize engagement on social media platforms in ways that may prioritize addiction over user well-being.⁷¹ Allegations of healthcare systems using Al to wrongfully deny medical claims for financial benefit further illustrate this potential conflict.⁸¹ The rapid pace of technological advancement, coupled with intense market competition, can sometimes incentivize companies to prioritize speed and scale over thorough ethical evaluation and mitigation of potential harms.⁷¹, ⁸⁷

This tension between profit-driven motives and ethical considerations presents a significant challenge in the field of Al development.⁶⁹ While the pursuit of innovation and economic growth are important drivers in the technology sector, there is a growing recognition that these goals must be balanced with a strong commitment to ethical principles.⁷¹, ⁷² The pressure to rapidly develop and deploy Al technologies can sometimes lead to a lack of sufficient attention to potential biases in algorithms, the protection of user privacy, and the broader societal impacts of these systems.⁷¹ The increasing prevalence of Al research within corporate environments, where access to resources is often greater than in academia, also raises questions about the potential influence of commercial interests on the direction and priorities of Al development.⁶⁸

The potential societal consequences of prioritizing profit over ethics in the realm of Al are far-reaching and deeply concerning.⁷¹, ⁸⁴ A focus solely on maximizing profit could lead to the widespread deployment of Al systems that perpetuate and even amplify existing societal biases, resulting in unfair or discriminatory outcomes in areas such as hiring, lending, and criminal justice.⁷¹ The erosion of individual privacy through the unchecked collection and use of personal data by Al systems is another significant risk.⁷¹ Furthermore, the prioritization of engagement and profit on online platforms driven by Al algorithms can contribute to the spread of misinformation and the erosion of trust in reliable sources of information.⁷¹ Ultimately, a failure to adequately address the ethical implications of Al development in favor of purely profit-driven motives could lead to a future where the immense power of this technology is not harnessed for the benefit of humanity as a whole, but rather exacerbates existing inequalities and creates new forms of societal harm, echoing the user’s concern about the “human cost” of unchecked Al advancement [user_query].

The journey towards ethical integration of Al, especially in a world where it exhibits human-like conversational abilities, presents numerous key challenges. Synthesizing the findings from the literature reveals that ensuring ethical Al development and deployment requires addressing the fundamental issue of defining and effectively enforcing ethical standards for these complex systems.⁷¹, ⁷⁴ Balancing the imperative for technological innovation with the critical need for accountability remains a central challenge, as the rapid pace of Al advancement often outstrips the capacity of regulatory and ethical frameworks to adapt.⁵² The pervasive issue of bias and discrimination within Al systems, often stemming from biased training data, requires ongoing attention and robust mitigation strategies to prevent unfair or discriminatory outcomes.⁴, ⁷ In the creative ecosystem, protecting intellectual property rights in the face of increasingly sophisticated Al-generated content and ensuring fair compensation for creators whose work is used for Al training are paramount concerns.²⁷ Within the political sphere, mitigating the significant risks of Al being used for manipulation, surveillance, and the spread of harmful content to undermine democratic processes and human rights demands urgent attention and proactive measures.⁴¹ Finally, ensuring transparency and explainability in the decision-making processes of Al systems is crucial for building trust and enabling effective oversight and accountability.¹⁹, ⁷⁷ The persistent tension between profit-driven motives within the technology industry and the overarching need for ethical considerations remains a significant hurdle that must be carefully navigated to ensure a responsible and beneficial future for Al.⁶⁹

To chart an ethical course for the future of Al, several potential solutions and recommendations can be proposed for policymakers, technology developers, and creators. For policymakers, it is crucial to develop and implement comprehensive and adaptable regulatory frameworks for Al, drawing inspiration from models like the EU Al Act and the OECD Principles, while also ensuring flexibility to keep pace with rapid technological advancements.⁵⁰ Increased investment in interdisciplinary research on Al ethics and safety is essential to better understand the societal implications of this technology and to develop effective solutions for mitigating potential harms.⁶⁷ Fostering international cooperation on Al governance is vital to ensure a harmonized global approach to addressing the ethical challenges that transcend national borders.⁵², ⁸⁷ Establishing clear mechanisms for accountability and providing avenues for redress when Al systems cause harm or perpetuate bias are also critical for building public trust.¹⁹

For technology developers, it is paramount to embed ethical considerations into the very design and development process of Al systems from the outset, rather than treating ethics as an afterthought.¹⁹, ⁶⁶ Prioritizing fairness, transparency, and the protection of user privacy should be guiding principles throughout the Al lifecycle.¹⁹ Conducting regular audits and comprehensive impact assessments of Al systems is essential to identify and mitigate potential biases and unintended consequences.¹⁹ Engaging with ethicists, social scientists, and diverse groups of stakeholders can provide valuable insights and perspectives to help ensure the responsible development and deployment of Al.¹⁹

For creators, including authors and artists, it is important to actively advocate for stronger intellectual property rights in the digital age to address the unique challenges posed by Al-generated content.²⁷ Exploring and supporting the development of new licensing and compensation models that fairly recognize and reward the use of their work in Al training is crucial for their economic sustainability.²⁹ Furthermore, creators can leverage technological tools and strategies designed to protect their original creations from unauthorized scraping and replication by Al systems.³⁷

In conclusion, the future of Al hinges on achieving a delicate balance between fostering rapid technological innovation and upholding fundamental ethical principles. While Al that can convincingly mimic human intelligence, potentially surpassing the Turing Test, offers immense potential benefits across various sectors, realizing these benefits responsibly necessitates a concerted and collaborative effort from all stakeholders. Policymakers, technology developers, and creators must work together to ensure that Al is developed and deployed in a manner that aligns with human values, promotes the common good, and safeguards against potential harms. The increasing sophistication of Al underscores the urgency of this task, as its growing ability to replicate human intelligence demands a corresponding and unwavering commitment to ensuring its inherent ethical intelligence.

Works Cited#

1. www.ibm.com, accessed April 6, 2025, https://www.ibm.com/think/topics/ai-ethics#:~:text=Ethics%20is%20a%20set%20of,reducing%20risks%20and%20adverse%20outcomes.
2. What Is Al ethics? The role of ethics in Al | SAP, accessed April 6, 2025, https://www.sap.com/resources/what-is-ai-ethics
3. What is Al Ethics? | IBM, accessed April 6, 2025, https://www.ibm.com/think/topics/ai-ethics
4. Al Ethics: What It Is, Why It Matters, and More | Coursera, accessed April 6, 2025, https://www.coursera.org/articles/ai-ethics
5. Ethics of artificial intelligence - Wikipedia, accessed April 6, 2025, https://en.wikipedia.org/wiki/Ethics_of_artificial_intelligence
6. Understanding artificial intelligence ethics and safety - The Alan Turing Institute, accessed April 6, 2025, https://www.turing.ac.uk/sites/default/files/2019-08/understanding_artificial_intelligence_ethics_and_safety.pdf
7. Bias in Decision-Making for Al’s Ethical Dilemmas: A Comparative Study of ChatGPT and Claude - arXiv, accessed April 6, 2025, https://arxiv.org/html/2501.10484v1
8. Turing test - Wikipedia, accessed April 6, 2025, https://en.wikipedia.org/wiki/Turing_test
9. Al Beat the Turing Test by Being a Better Human | Psychology Today, accessed April 6, 2025, https://www.psychologytoday.com/us/blog/the-digital-self/202504/ai-beat-the-turing-test-by-being-a-better-human
10. [2310.20216] Does GPT-4 pass the Turing test? – arXiv, accessed April 6, 2025, https://arxiv.org/abs/2310.20216
11. Passing the Turing Test Does Not Mean the End of Humanity - PMC, accessed April 6, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC4867147/
12. Artificial Intelligence and the Turing Test - Institute for Citizen-Centred Service -, accessed April 6, 2025, https://iccs-isac.org/assets/uploads/research-repository/Research-report-December-2023-Al-and-Turing-Test.pdf
13. Can Al really pass the Turing test? - Wildfire PR, accessed April 6, 2025, https://www.wildfirepr.com/blog/can-ai-really-pass-the-turing-test
14. Computer Al Passes the Turing Test for the First Time in History - AlleyWatch, accessed April 6, 2025, https://www.alleywatch.com/2014/06/computer-ai-passes-the-turing-test-for-the-first-time-in-history/
15. The Turing Test: From Inception to Passing - Servo Magazine, accessed April 6, 2025, https://www.servomagazine.com/magazine/article/february2015_Hood
16. Could general-Al language generation be a test for sentience, sapience, or consciousness?, accessed April 6, 2025, https://philosophy.stackexchange.com/questions/106968/could-general-ai-language-generation-be-a-test-for-sentience-sapience-or-consc
17. Al passed the Turing Test : r/singularity - Reddit, accessed April 6, 2025, https://www.reddit.com/r/singularity/comments/1jpoib5/ai_passed_the_turing_test/
18. What Is Strong AI? | IBM, accessed April 6, 2025, https://www.ibm.com/think/topics/strong-ai
19. Al Ethics in Action: How to Ensure Fair Practices in Your Organization - Inclusion Cloud, accessed April 6, 2025, https://inclusioncloud.com/insights/blog/implementing-responsible-ai-practices/
20. Al forces us to think about what consciousness means - Mathew Ingram, accessed April 6, 2025, https://mathewingram.com/work/2025/02/27/ai-forces-us-to-think-about-what-consciousness-means/
21. Beyond the Turing Test: Unleashing the Metacognitive Core of Al - Medium, accessed April 6, 2025, https://medium.com/michael-for-president/beyond-the-turing-test-unleashing-the-metacognitive-core-of-ai-a214cc3ae1ac
22. Brain-Model Evaluations Need the NeuroAl Turing Test - arXiv, accessed April 6, 2025, https://arxiv.org/html/2502.16238
23. Court Rules Al Training on Copyrighted Works Is Not Fair Use — What It Means for Generative Al - Davis+Gilbert LLP, accessed April 6, 2025, https://www.dglaw.com/court-rules-ai-training-on-copyrighted-works-is-not-fair-use-what-it-means-for-generative-ai/
24. Use of Copyrighted Works in Al Training Is Not Fair Use: Thomson Reuters Enterprise Centre GmbH v. Ross Intelligence Inc. | Carlton Fields, accessed April 6, 2025, https://www.carltonfields.com/insights/publications/2025/use-of-copyrighted-works-in-ai-training-is-not-fair-use
25. Al Training Using Copyrighted Works Ruled Not Fair Use, accessed April 6, 2025, https://www.pbwt.com/publications/ai-training-using-copyrighted-works-ruled-not-fair-use
26. What Is Fair Use? — The Impact of Al on Fair Use - Originality.ai, accessed April 6, 2025, https://originality.ai/blog/fair-use-and-ai
27. Artificial Intelligence and Copyright: Navigating the New Legal Landscape - Senior Executive, accessed April 6, 2025, https://seniorexecutive.com/ai-copyright-law-ownership-intellectual-property-rights/
28. Al, Copyright, and the Law: The Ongoing Battle Over Intellectual Property Rights, accessed April 6, 2025, https://sites.usc.edu/iptls/2025/02/04/ai-copyright-and-the-law-the-ongoing-battle-over-intellectual-property-rights/
29. Copyright Battles Erupt as Artists Face Off Against Al | Al News - OpenTools, accessed April 6, 2025, https://opentools.ai/news/copyright-battles-erupt-as-artists-face-off-against-ai
30. Court Issues First Decision on Al and Fair Use | Alerts and Articles | Insights | Ballard Spahr, accessed April 6, 2025, https://www.ballardspahr.com/insights/alerts-and-articles/2025/02/court-issues-first-decision-on-ai-and-fair-use
31. Court Rejects Fair Use for Al Training - Creative Law Center, accessed April 6, 2025, https://creativelawcenter.com/no-fair-use-for-ai-training-on-copyrighted-material/
32. Al and Copyright in the Publishing World: Challenges, Opportunities, and the Road Ahead, accessed April 6, 2025, https://publishdrive.com/ai-and-copyright-in-the-publishing-world-challenges-opportunities-and-the-road-ahead.html
33. Identifying the Economic Implications of Artificial Intelligence for Copyright Policy, accessed April 6, 2025, https://www.copyright.gov/economic-research/economic-implications-of-ai/Identifying-the-Economic-Implications-of-Artificial-Intelligence-for-Copyright-Policy-FINAL.pdf
34. Artificial Intelligence Impacts on Copyright Law - RAND Corporation, accessed April 6, 2025, https://www.rand.org/pubs/perspectives/PEA3243-1.html
35. cdn.dacs.org.uk, accessed April 6, 2025, https://cdn.dacs.org.uk/uploads/documents/News/DACS-Al-and-artists-briefing.pdf?v=1708424212#:~:text=Machine%20learning%20consists%20of%20scraping,of%20remuneration%20for%20those%20uses.
36. Survey Reveals 9 out of 10 Artists Believe Current Copyright Laws are Outdated in the Age of Generative Al Technology, accessed April 6, 2025, https://bookanartist.co/blog/2023-artists-survey-on-ai-technology/
37. Al’s Impact on Artists – LMU Magazine, accessed April 6, 2025, https://magazine.lmu.edu/articles/mimic-master/
38. Artists Win Landmark Intellectual Property Case Against Al - Expert Institute, accessed April 6, 2025, https://www.expertinstitute.com/resources/insights/artists-victory-intellectual-property-case-ai-generated-content-companies/
39. Al-generated content and IP rights: Challenges and policy considerations - Diplo, accessed April 6, 2025, https://www.diplomacy.edu/blog/ai-generated-content-and-ip-rights-challenges-and-policy-considerations/
40. Guarding the News Media’s Intellectual Property in the Age of Generative Al - Journal Article, accessed April 6, 2025, https://law.stanford.edu/publications/guarding-the-news-medias-intellectual-property-in-the-age-of-generative-ai/
41. How Autocrats Weaponize Al — And How to Fight Back | Journal of Democracy, accessed April 6, 2025, https://www.journalofdemocracy.org/online-exclusive/how-autocrats-weaponize-ai-and-how-to-fight-back/
42. Artificial intelligence (Al) and human rights: Using Al as a weapon of repression - European Parliament, accessed April 6, 2025, https://www.europarl.europa.eu/RegData/etudes/IDAN/2024/754450/EXPO_IDA(2024)754450(SUM01)_EN.pdf
43. How Al-generated disinformation might impact this year’s elections and how journalists should report on it | Reuters Institute for the Study of Journalism, accessed April 6, 2025, https://reutersinstitute.politics.ox.ac.uk/news/how-ai-generated-disinformation-might-impact-years-elections-and-how-journalists-should-report
44. Synthetic Media: The New Frontier of Political Manipulation - Temple iLIT, accessed April 6, 2025, https://law.temple.edu/ilit/synthetic-media-the-new-frontier-of-political-manipulation/
45. Can Democracy Survive the Disruptive Power of AI? | Carnegie Endowment for International Peace, accessed April 6, 2025, https://carnegieendowment.org/research/2024/12/can-democracy-survive-the-disruptive-power-of-ai
46. Election disinformation takes a big leap with Al being used to deceive worldwide - AP News, accessed April 6, 2025, https://apnews.com/article/artificial-intelligence-elections-disinformation-chatgpt-bc283e7426402f0b4baa7df280a4c3fd
47. CANDIDATE AI: THE IMPACT OF ARTIFICIAL INTELLIGENCE ON ELECTIONS, accessed April 6, 2025, https://news.emory.edu/features/2024/09/emag_ai_elections_25-09-2024/index.html
48. Al Poses Risks to Both Authoritarian and Democratic Politics | Wilson Center, accessed April 6, 2025, https://www.wilsoncenter.org/blog-post/ai-poses-risks-both-authoritarian-and-democratic-politics
49. An Agenda to Strengthen U.S. Democracy in the Age of Al | Brennan Center for Justice, accessed April 6, 2025, https://www.brennancenter.org/our-work/policy-solutions/agenda-strengthen-us-democracy-age-ai
50. The Al Governance Frontier Series Part 1 - Decoding Global and …, accessed April 6, 2025, https://medium.com/@adnanmasood/the-ai-governance-frontier-series-part-1-decoding-global-and-u-s-6a9d0781ba80
51. Groundbreaking Framework for the Safe and Secure Deployment of Al in Critical Infrastructure Unveiled by Department of Homeland Security, accessed April 6, 2025, https://www.dhs.gov/archive/news/2024/11/14/groundbreaking-framework-safe-and-secure-deployment-ai-critical-infrastructure
52. Al Regulations around the World - 2025 - Mind Foundry, accessed April 6, 2025, https://www.mindfoundry.ai/blog/ai-regulations-around-the-world
53. US Federal Regulation of Al Is Likely To Be Lighter, but States May Fill the Void | Insights, accessed April 6, 2025, https://www.skadden.com/insights/publications/2025/01/2025-insights-sections/revisiting-regulations-and-policies/us-federal-regulation-of-ai-is-likely-to-be-lighter
54. www.ey.com, accessed April 6, 2025, https://www.ey.com/en_ch/insights/forensic-integrity-services/the-eu-ai-act-what-it-means-for-your-business#:~:text=The%20Al%20Act%20aims%20to,single%20EU%20market%20for%20Al.
55. The EU Al Act: What it means for your business | EY - Switzerland, accessed April 6, 2025, https://www.ey.com/en_ch/insights/forensic-integrity-services/the-eu-ai-act-what-it-means-for-your-business
56. From regulation to innovation: What the EU Al Act means for EdTech - FeedbackFruits, accessed April 6, 2025, https://feedbackfruits.com/blog/from-regulation-to-innovation-what-the-eu-ai-act-means-for-edtech
57. What is the Artificial Intelligence Act of the European Union (EU Al Act)? - IBM, accessed April 6, 2025, https://www.ibm.com/think/topics/eu-ai-act
58. OECD Updates Al Principles - American National Standards Institute, accessed April 6, 2025, https://ansi.org/standards-news/all-news/2024/05/5-9-24-oecd-updates-ai-principles
59. The 2024 update to the OECD Al Principles - Digital Policy Alert, accessed April 6, 2025, https://digitalpolicyalert.org/ai-rules/2024-update-OECD-principles
60. OECD Al Principles 2024: Addressing Generative Al New Risks, accessed April 6, 2025, https://www.private-ai.com/en/2024/06/12/oecd-ai-principles-2024/
61. Evolving with innovation: The 2024 OECD Al Principles update, accessed April 6, 2025, https://oecd.ai/en/wonk/evolving-with-innovation-the-2024-oecd-ai-principles-update
62. Top 10 Ethical Considerations for Al Projects | PMI Blog, accessed April 6, 2025, https://www.pmi.org/blog/top-10-ethical-considerations-for-ai-projects
63. Ethical considerations of Al: Fairness, transparency, and frameworks | Future of responsible Al | Lumenalta, accessed April 6, 2025, https://lumenalta.com/insights/ethical-considerations-of-ai
64. How to Use Artificial Intelligence Ethically and Responsibly - Kindo Al, accessed April 6, 2025, https://www.kindo.ai/blog/how-to-use-ai-ethically-responsibly
65. Ethical Al vs. Responsible Al, accessed April 6, 2025, https://sigma.ai/ethical-ai-responsible-ai/
66. (PDF) Artificial Intelligence (AI) Ethics: Ethics of Al and Ethical Al - ResearchGate, accessed April 6, 2025, https://www.researchgate.net/publication/340115931_Artificial_Intelligence_Al_Ethics_Ethics_of_Al_and_Ethical_Al
67. Shaping the Future of Al | National Academies, accessed April 6, 2025, https://www.nationalacademies.org/topics/artificial-intelligence
68. Future of Al Research - AAAI, accessed April 6, 2025, https://aaai.org/wp-content/uploads/2025/03/AAAI-2025-PresPanel-Report_FINAL.pdf
69. Experts Doubt Ethical Al Design Will Be Broadly Adopted as the Norm Within the Next Decade, accessed April 6, 2025, https://www.pewresearch.org/internet/2021/06/16/experts-doubt-ethical-ai-design-will-be-broadly-adopted-as-the-norm-within-the-next-decade/
70. Seven elements of ethical Al to guide its implementation by compliance - Saifr, accessed April 6, 2025, https://saifr.ai/blog/seven-elements-of-ethical-ai-to-guide-its-implementation-by-compliance
71. What is Al Ethics? Why is It Important? – New Horizons - Blog, accessed April 6, 2025, https://www.newhorizons.com/resources/blog/what-is-ai-ethics
72. Ethical concerns mount as Al takes bigger decision-making role - Harvard Gazette, accessed April 6, 2025, https://news.harvard.edu/gazette/story/2020/10/ethical-concerns-mount-as-ai-takes-bigger-decision-making-role/
73. Sincerity and Honesty towards my own research as seen from Teilhard de Chardin’s research attitude Research on Al Ethics, accessed April 6, 2025, https://fst.sophia.ac.jp/wp/wp-content/uploads/2025/03/3-%E9%8A%85%E8%B3%9E%E3%80%80B2478049-MUKULU-JOHN-FRANCIS%E3%81%95%E3%82%93-Sincerity-and-Honesty-The-Essential-Ethics-of-Artificial-Intelligence-Teilhard-De-Chardin-Award.pdf
74. annenberg.usc.edu, accessed April 6, 2025, https://annenberg.usc.edu/research/center-public-relations/usc-annenberg-relevance-report/ethical-dilemmas-ai#:~:text=The%20ethical%20challenge%20lies%20in,difficult%20to%20understand%20or%20interpret.
75. Common ethical challenges in Al - Human Rights and Biomedicine - Council of Europe, accessed April 6, 2025, https://www.coe.int/en/web/human-rights-and-biomedicine/common-ethical-challenges-in-ai
76. The ethical dilemmas of Al | USC Annenberg School for Communication and Journalism, accessed April 6, 2025, https://annenberg.usc.edu/research/center-public-relations/usc-annenberg-relevance-report/ethical-dilemmas-ai
77. Full article: Al Ethics: Integrating Transparency, Fairness, and Privacy in Al Development, accessed April 6, 2025, https://www.tandfonline.com/doi/full/10.1080/08839514.2025.2463722
78. Building Trust in Al: The Role of Transparency and Accountability - BABL AI, accessed April 6, 2025, https://babl.ai/building-trust-in-ai-the-role-of-transparency-and-accountability/
79. The Role of Transparency and Accountability in Al Systems - ResearchGate, accessed April 6, 2025, https://www.researchgate.net/publication/386083234_The_Role_of_Transparency_and_Accountability_in_Al_Systems
80. OpenAl’s Controversial For-Profit Pivot: Tech Titans Push Back | Al News - OpenTools.ai, accessed April 6, 2025, https://opentools.ai/news/openais-controversial-for-profit-pivot-tech-titans-push-back
81. A Healthcare System’s Moral Bankruptcy Goes Viral - MedCity News, accessed April 6, 2025, https://medcitynews.com/2024/12/a-healthcare-systems-moral-bankruptcy-goes-viral/
82. The MAGA Mess: Moral Bankruptcy and Nostalgia Gone Wild | by Christian Baghai | Medium, accessed April 6, 2025, https://christianbaghai.medium.com/the-maga-mess-moral-bankruptcy-and-nostalgia-gone-wild-d79f1222f930
83. The Rise of Tech Ethics: Approaches, Critique, and Future Pathways - PMC, accessed April 6, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC11464588/
84. Top 9 ethical issues in artificial intelligence - The World Economic Forum, accessed April 6, 2025, https://www.weforum.org/stories/2016/10/top-10-ethical-issues-in-artificial-intelligence/
85. Artificial Intelligence, Social Media, and Political Violence Prevention, accessed April 6, 2025, https://kroc.nd.edu/research/artificial-intelligence-social-media-and-political-violence-prevention/
86. Al and the 2024 Election Part III: Many Uses and Minor Impacts - R Street Institute, accessed April 6, 2025, https://www.rstreet.org/commentary/ai-and-the-2024-election-part-iii-many-uses-and-minor-impacts/
87. Sovereign remedies: Between Al autonomy and control - Atlantic Council, accessed April 6, 2025, https://www.atlanticcouncil.org/in-depth-research-reports/issue-brief/sovereign-remedies-between-ai-autonomy-and-control/