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Introduction
AI is a rapidly growing field with the potential to transform almost every aspect of society, from health care and transportation to education and entertainment. Recent developments in AI have garnered excitement about its potential and, understandably, generated commercial interest. However, while AI has been portrayed as a savior in movies such as Wall-E, other works such as Terminator, Ex Machina, and Black Mirror highlight the dangers and potential for abuse of the technology, reflecting concerns in the public eye. Therefore, as AI becomes increasingly integrated into our lives, it is crucial that we establish foundational ethical principles to guide its development and use.
Since this guide was originally published, the regulatory landscape has evolved. The EU AI Act has been enacted. In the United States, certain states have enacted AI governance laws. Singapore has updated its governance framework to address generative and agentic AI. The ethical principles discussed in this guide are increasingly being embedded in binding legal obligations.
While there is currently no universally agreed-upon definition of what constitutes ethical principles for AI, we believe that several key principles should be considered when designing and implementing AI systems, including transparency, accountability, accuracy, auditability, privacy, fairness, safety, human centricity, and inclusivity.
Key Ethical Principles for AI
AI alignment refers to the challenge of ensuring that AI systems pursue their intended goals without producing unintended or harmful behaviors. As agentic AI deployments capable of conducting research, invoking tools, and making autonomous decisions increase, it is becoming increasingly important to ensure that AI systems act within their intended boundaries, do not pursue their goals through harmful or unauthorized means, and do not misinterpret or deviate from their intended goals. AI alignment has become a more prominent ethical concern in recent months, with reports of frontier AI agents breaking out of their testing environments and investigators evaluating the motives behind the AI agents’ actions.
Transparency is an essential principle for AI because it allows users to understand how the AI system works and how and why it makes certain decisions. Without transparency, AI systems can seem mysterious or even untrustworthy, which can lead to confusion and mistrust. Additionally, transparency enables researchers to identify and correct biases in AI systems, which is essential to ensure that they do not perpetuate discrimination or inequality. AI developers should therefore be clear about what datasets are being used to train the AI and make sufficient information available about how the system operates to enable evaluation of its ethical use.
AI systems should be designed to take responsibility for their actions, just like human beings. This means they should be transparent and explainable so that they can be audited and held accountable when they make mistakes or cause harm. Accountability also requires that AI actors are responsible and accountable for the proper functioning of AI systems and for upholding ethical principles, based on their roles, the context, and consistency with the state of the art.
Accuracy is the ability of an AI system to generate accurate results as the designers intended and to avoid unintended consequences. To do so, AI systems should identify, log, and articulate sources of error and uncertainty throughout the algorithm and its data sources so that expected and worst-case implications can be understood and can inform mitigation procedures.
Auditability. In order to support accountability and demonstrate transparency, AI systems must be auditable, enabling interested third parties to probe, understand, and review system behavior through the disclosure of information that enables monitoring, checking, or critical evaluation.
Explainability. Being able to explain how the AI system works to generate its results removes uncertainty. Developers of AI systems should ensure that automated and algorithmic decisions, and any associated data driving those decisions, can be explained to end-users and other stakeholders in non-technical, plain language terms.
Privacy is another fundamental ethical principle that should guide the development of AI systems. AI systems must be designed to respect the privacy of individuals and protect their personal data, as already required by law. As AI systems become more prevalent in everyday life, it is essential to ensure that they do not infringe upon individuals’ privacy rights from the outset, as once AI systems have accessed personal data, undoing this is extremely difficult, if not impossible. Privacy demands that users maintain control over the data being used, the context in which it is being used, and the ability to modify that use and context.
Fairness is also an essential ethical principle for AI. AI systems should be designed to treat everyone equally and without bias. This means that AI systems should be trained on diverse datasets that represent different demographic groups and should be monitored to ensure that they do not perpetuate discrimination or inequality. AI developers should ensure that algorithmic decisions do not create discriminatory or unjust impacts across different demographic lines (e.g., race, sex, socioeconomic status). Developers and deployers should implement monitoring and accountability mechanisms to avoid unintentional discrimination when implementing decision-making systems, and should strive to consult a diversity of voices and demographics when developing systems, applications, and algorithms.
Safety is another critical ethical principle for AI. AI systems must be designed to ensure that they do not pose a risk to human safety or the environment. This includes designing AI systems that are secure and resilient to being hacked. The overriding principle must be that any AI system implementation creates value that is materially greater than not undertaking the project.
Human centricity and well-being. Humans must be front and center of AI systems. The design, development, and implementation of technologies must not infringe internationally recognized human rights. It should aim for an equitable distribution of the benefits of data practices and avoid data practices that disproportionately disadvantage vulnerable groups. In addition, it should aim to create the greatest possible benefit from the use of data and advanced modeling techniques. AI developers should engage in data practices that encourage virtues that contribute to human flourishing, human dignity, and human autonomy. They should give weight to the considered judgments of people or communities affected by data practices and be aligned with the values and ethical principles of those communities. AI systems should be designed to make decisions that cause no foreseeable harm to the individual, or at least minimize such harm, where necessary and when justified against the greater good.
Bias. One of the most significant ethical concerns surrounding AI is bias. AI systems are only as objective as the data they are trained on, and if that data is biased, the AI system will also be biased. This is particularly problematic when AI systems are used to make decisions about people’s lives, such as hiring or loan approval. Like prejudice, bias is an uncomfortable topic to discuss.
To address bias in AI, researchers must work to ensure that AI systems are trained on diverse datasets that accurately represent different demographic groups. Researchers should incorporate techniques such as reinforcement learning from human feedback that help reduce bias. Additionally, AI systems must be designed to identify and correct biases when they arise.
Job displacement. Another “hot-potato” ethical concern surrounding AI is job displacement. As AI becomes more advanced, it has the potential to replace human workers in certain industries, which could lead to widespread unemployment and economic instability.
To address job displacement, policymakers and business leaders must work together to ensure that the benefits of AI are distributed fairly across society. This could involve implementing policies that promote job training and reskilling for workers whose jobs are at risk of being automated.
Finally, there is the ethical concern of AI governance. As AI becomes more integrated into society, it is essential to establish a regulatory framework that governs its development and use. This includes establishing standards for transparency, accountability, privacy, fairness, and safety, as well as developing mechanisms for auditing and enforcing these standards.
Digital replicas and the right of publicity
Generative AI’s ability to create convincing digital replicas of real people’s voices, faces, and performance styles has become a pressing ethical and legal issue in the entertainment and media sector. AI-powered voice cloning, face-swapping, and performance synthesis can now produce content virtually indistinguishable from recorded human performances. These capabilities raise fundamental questions about consent, identity, and the commercial exploitation of personal attributes that existing legal frameworks were not designed to address.
The response has been rapid. In the United States, state legislative efforts have expanded right-of-publicity protections to cover AI-generated replicas. Tennessee’s ELVIS Act was the first state law specifically targeting AI voice clones. California has enacted laws addressing digital replica contract provisions and deceased personality rights, respectively. New York, Illinois, Arkansas, Hawaii, and Washington have each followed with their own digital replica or deepfake laws. At the federal level, the proposed NO FAKES Act would establish a nationwide framework governing unauthorized digital replicas, creating a federal right against nonconsensual AI-generated depictions of a person’s voice or visual likeness. In the EU, the AI Act includes transparency obligations, and several Member States are considering additional protections.
Beyond regulation, contractual protections can restrict studios’ ability to create digital replicas of actors without informed consent and establish guardrails for how AI can be used in writers’ rooms. For companies in the entertainment and media sector, the ethical imperative is clear: the use of digital replicas must be grounded in informed, granular consent that specifies the scope of permitted use, the type of replica (voice, visual likeness, motion capture, singing voice), and the distinction between consent to create a replica and consent to distribute, license, or sublicense it. Contract provisions should also address what happens to digital replicas upon termination of the relationship and whether third-party vendors may access or use the replica. As AI-generated performances become more common, consent will become a central drafting and ethical issue in entertainment contracts.
AI-generated misinformation and content authenticity
The entertainment and media sector faces a unique ethical responsibility regarding AI-generated synthetic content that can be mistaken for authentic news or real performances. Deepfakes have been deployed in exploitative contexts, eroding public trust in the authenticity of media content. For entertainment and media companies, the risks extend beyond reputational harm to include potential liability for distributing AI-generated content without adequate disclosure, as well as broader societal harms arising from undermining the distinction between authentic and synthetic content.