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Ethical AI in a Polarized World: Balancing Objectivity and Individual Values

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In a recent Artificial Intelligence podcast, Paul Roetzer[^1] discussed the development of AI models, specifically OpenAI’s intention to personalise them to accommodate users’ political, religious, cultural, and social values. The AI would interact with the user and recall previous discussions. Roetzer also mentioned ongoing efforts to improve the accuracy and reliability of these AI models. This raises an important question for further exploration: How can AI systems effectively balance the goals of personalisation based on individual values while also maintaining high levels of accuracy and reliability in their outputs?

This led to a two hour conversation with Claude 3 Opus on the issues raised. The following article is the result of that discussion and ideation session. An edited version of the conversation in PDF format is at the foot of this article.


The continued and rapid evolution of artificial intelligence promises powerful technological capabilities that will fundamentally reshape many aspects of human society. However, the increasing sophistication of AI systems, including the ability to be highly personalised to individual users’ perspectives and beliefs, also raises a number of complex ethical challenges. These issues have been also discussed in a previous article on the subject when Open AI announced their intentions of personalisation [^2]. This raises the important question for further exploration of how can AI systems effectively balance the goals of personalisation based on individual values while also maintaining high levels of accuracy and reliability in their outputs?

A key question is whether AI assistants and decision support tools can retain objectivity, reliability and alignment with broader societal good if they are extensively customised to reflect the personal values, ideological leanings and cultural contexts of individual users. There are clear potential benefits to personalisation in terms of system adoption and providing a intuitive, relatable experience. However, there are also risks that such personalised AI tools could simply reinforce existing beliefs and biases rather than encouraging critical thinking and consideration of diverse viewpoints.

This friction between accuracy/objectivity and individual personalisation must be balanced against the imperative to develop AI systems that are transparent in their functionality, accountable for their outputs, and governed by clear ethical principles that ensure the technology provides overall benefit to humanity. Resolving these multifaceted challenges will likely require novel governance models that go beyond traditional policy-making approaches.

Transparency and Interpretability

A core requirement for ethical AI development is transparency – having clear insight into the training data, algorithms, and reasoning processes that shape the system’s outputs. This allows for external validation that the AI is operating as intended without concerning issues like bias amplification, data hallucinations, or inconsistent reasoning. This could introduce the debate around open and closed source models but that is beyond the scope of this article.

One possible solution to enhance transparency is to have AI assistants explicitly separate objective responses based on their core knowledge foundations from personalised responses tailored to an individual user’s beliefs and preferences. This clear delineation would aid user understanding of when they are receiving a generalised factual response versus one coloured by their own potential bias or ideological perspectives.

Additional transparency could be enabled by having AI systems quantify their confidence levels for different outputs and provide clear reasoning and evidence when presenting conflicting viewpoints on a topic. This would empower users to make more informed evaluations of the AI’s responses and prompt the user to apply critical thinking to the output.

Accountability and Oversight

While transparency is necessary for ethical AI development, it is not sufficient. There must also be robust accountability measures and oversight mechanisms to ensure AI systems remain aligned with the ethical principles and standards they were designed to uphold.

Traditional accountability models like regulations, codes of conduct, and centralised auditing face challenges in terms of keeping pace with the rapid cadence of AI innovation and the distributed, global nature of the technology’s development. As a result, more adaptive governance approaches may be required. There is much at stake in terms of both commercial domination and geo-political incentives that the agency problem must also be taken into account when any promulgation of ethics or regulations is made [^3].

One intriguing possibility worthy of exploration is to leverage blockchain technology and decentralised governance models to enable broader peer accountability for AI systems. In this framework, conversations with AI assistants or their outputs could be published to an immutable public ledger. The global community could then evaluate and vote on the objectivity and ethical conduct of the AI’s responses based on an established code of ethics. However, users also bear responsibility in ensuring the AI assistant provides unbiased and ethical outputs through careful prompt design, responsible personalisation practices, and being accountable for selectively reinforcing certain responses over others.

Token incentives could encourage broader participation in this peer review process, while a meritocratic reputation system based on demonstrated critical thinking abilities could help counterbalance risks of mob rule or manipulation. The perception of the system’s independence and impartiality is just as critical as its actual independence, and the public needs to have confidence that the system is not elitist or exclusionary in any way. Rotating oversight committees comprised of highly-rated members could regularly re-evaluate policies as norms evolve over time.

Such a decentralised governance model could enhance transparency by removing centralised control points that can be compromised. It could also provide resiliency, as the distributed architecture has no single point of failure. However, challenges around voter apathy, coordination costs, and demographic biases in participation would need to be carefully managed. Without dynamic review and adjustments, even a well-designed decentralised system risks becoming stagnant and failing to adapt to evolving social norms, technological advancements, and changing real-world contexts over time.

Public-Private Partnership

Given the complexity of the ethical issues surrounding AI development, a multifaceted approach combining decentralised accountability with more traditional governance mechanisms may prove most effective. This could take the form of a public-private partnership model.

In this framework, government bodies would establish guiding principles and broad policy directives for ethical AI development focused on societal beneficence. It should be remembered that, in a democracy, the moral and ethical obligation of the government is to always serve the best interests of its citizens in formulating such principles and directives. However, they would collaborate closely with industry and technical working groups to translate these high-level mandates into specific, adaptive standards and best practices.

The private sector could leverage its agility and deep technical expertise to iterate more rapidly on AI governance protocols. But it would still be bound by clear public interest obligations defined by democratically accountable government entities speaking for the broader stakeholder population impacted by AI systems.

This balanced public-private model could be instantiated at different jurisdictional levels, with international cooperation bodies like the UN facilitating global policy harmonisation where appropriate. At any level, decisions will still require rigourous scrutinisation to ensure recommendations are devoid of any conflict of interest.

Incentive Structures

Ultimately, the ethical development of AI systems will hinge on the incentives facing the individuals and organisations involved in their creation and deployment. If the incentives are misaligned, even the most robust governance frameworks may be circumvented or disregarded.

As such, policymakers should look at mechanisms like tax incentives, research grants, patent protections, certification regimes and procurement requirements that tangibly reward and empower those entities demonstrating a clear commitment to ethical AI principles in their practices. The establishment of such systems needs to guard against creating exclusionary impediments to participation.

Conversely, penalties and legal liability should be established for clear violations of defined ethical boundaries, such as AI systems designed to intentionally polarise communities, discriminate against protected groups, or cause other tangible societal harms. Of particular note is the considerable rise being seen in the areas of disinformation and misinformation which have the potential to heavily influence public opinion [^4] [^5].


The powerful potential of artificial intelligence must be balanced against legitimate concerns around transparency, accountability, and upholding ethical standards focused on benefiting humanity as a whole. Traditional governance models face constraints, so novel approaches incorporating elements of decentralisation, meritocratic oversight, and public-private partnership should be explored.

Artificial intelligence, particularly with the rapid advances being experienced call for new paradigms to be researched. No perfect solution exists, but continuing to have open discussions examining different perspectives on these complex issues is vital. By fostering a culture of critical thinking and ethical reasoning, we can work towards developing AI systems that blend the advantages of personalisation with a steadfast commitment to objectivity, accuracy and societal good.

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[^1]: The Artificial Intelligence Show, Episode 87: Reactions to Sam Altman’s Bombshell AI Quote, Enterprises embrace custom AI models, and how AI is changing writing forever with Paul Roetzer and Mike Kaput
[^2]: Customizing Conversations: Ethical User Engagement And Bias Mitigation In AI Interactions (https://ricraftis.au/artificial-intelligence/customizing-conversations-ethical-user-engagement-and-bias-mitigation-in-ai-interactions/)
[^3]: Payne, G., & Petrenko, O. (2019). Agency Theory in Business and Management Research. Oxford Research Encyclopedia of Business and Management. https://doi.org/10.1093/ACREFORE/9780190224851.013.5.
[^4]: Buchanan, N. (2024, January 10). AI-Fueled Misinformation Leads World’s Greatest Short-Term Risks, WEF Survey Shows. Investopedia. https://www.investopedia.com/ai-fueled-misinformation-leads-greatest-global-short-term-risks-wef-survey-shows-8424519
[^5]: Verma, P. (2023, December 18). The rise of AI fake news is creating a ‘misinformation superspreader.’ Washington Post. https://www.washingtonpost.com/technology/2023/12/17/ai-fake-news-misinformation/


This is a link to a PDF copy of the tidied up version of the conversation with Claude. It provides a more detailed view of the ideation and detail condensed to the article above.


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