Artificial intelligence (AI) has been used to create background music, enhance existing songs, or compose original melodies. However, some AI music platforms have been accused of violating the copyrights of major record labels, who claim that the AI-generated music infringes on their original works.
The plaintiffs, Universal Music Group, Sony Music Entertainment, and Warner Music Group have countered that the AI systems used by the defendants are not capable of generating truly original and creative music and that they rely on the musical data and inputs provided by the plaintiffs and other sources. They have also asserted that their songs have distinctive and recognizable features that are copied or reproduced by the AI systems without authorization.
The lawsuits have raised complex and novel legal issues regarding the nature and scope of copyright protection for AI-generated music, and the criteria and standards for determining the originality, creativity, and ownership of AI-generated music. The outcomes of the lawsuits could have significant implications for the future of the AI music industry and the rights and interests of the musicians, composers, producers, and consumers involved.
Examples of AI Music Platforms Sued by Major Record Labels
Amper Music: Amper Music is an AI music platform that allows users to create custom music for their videos, podcasts, games, or other projects. In 2020, Amper Music was sued by Universal Music Group, Sony Music Entertainment, and Warner Music Group, who alleged that Amper Music’s AI system copied the melodies, rhythms, harmonies, and lyrics of their songs without authorization.
Mubert: Mubert is an AI music platform that generates adaptive music streams for various scenarios, such as meditation, fitness, gaming, or studying. In 2021, Mubert was sued by Sony Music Entertainment, who claimed that Mubert’s AI system used the samples, loops, and stems of their songs without permission.
Boomy: Boomy is an AI music platform that enables users to create and sell their own songs, which are generated by an AI system based on the user’s preferences and inputs. In 2021, Boomy was sued by Warner Music Group, who alleged that Boomy’s AI system reproduced the melodies, structures, and styles of their songs without consent.
Conclusion
AI music platforms have been facing legal challenges from major record labels, who argue that the AI-generated music infringes on their copyrights. The lawsuits raise questions about the originality, creativity, and ownership of AI-generated music, and how the existing laws and regulations can address these issues.
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How technology can create and combat synthetic media
What are Deep Fakes?
Deep Fakes are a type of synthetic media that uses artificial intelligence (AI) to manipulate or generate audio, video, or images. They can create realistic-looking content that appears to show people doing or saying things that they never did or said. For example, a Deep Fake video could show a politician making a controversial statement, a celebrity endorsing a product, or a person’s face swapped with another person’s face.
Below are examples of one with an Arnold Schwarzenegger Deep Fake starring in James Cameron’s Titanic
How do Deep Fakes work?
Deep Fakes are created by using deep learning, a branch of AI that involves training neural networks on large amounts of data. Neural networks are mathematical models that can learn patterns and features from the data and apply them to new inputs. There are different methods to create Deep Fakes, but one of the most common ones is called generative adversarial networks (GANs).
GANs consist of two neural networks: a generator and a discriminator. The generator tries to create fake content that looks like real content, while the discriminator tries to distinguish between the real and the fake content. The two networks compete, improving their skills over time. The result is fake content that can fool both humans and machines.
What are the threats of Deep Fakes?
Deep Fakes pose several threats to individuals, organizations, and society. Some of the potential harms of Deep Fakes are:
Disinformation and propaganda: Deep Fakes can be used to spread false or misleading information, influence public opinion, undermine trust in institutions, and incite violence or conflict.
Identity theft and fraud: Deep Fakes can be used to impersonate someone’s voice, face, or biometric data, and gain access to their personal or financial information, accounts, or devices.
Blackmail and extortion: Deep Fakes can be used to create compromising or embarrassing content that can be used to coerce or threaten someone.
Privacy and consent violation: Deep Fakes can be used to create non-consensual or invasive content that can harm someone’s reputation, dignity, or mental health.
An example of how close a Deep Fake can be to the original
The people below were generated by the website www.thispersondoesnotexist.com such images can be used in fake social media accounts.
How are companies dealing with Deep Fakes?
While Deep Fakes pose a serious challenge, they also offer an opportunity for innovation and collaboration. Many companies are developing tools and solutions to detect, prevent, and mitigate the impact of Deep Fakes. Some of the examples are:
Adobe: Adobe has created a tool called Content Authenticity Initiative (CAI) that aims to provide a secure and verifiable way to attribute the origin and history of digital content. CAI uses cryptography and blockchain to create a tamper-proof record of who created, edited, or shared the content and allows users to verify the authenticity and integrity of the content.
Meta: Meta, formerly known as Facebook, has launched a program called Deep Fake Detection Challenge (DFDC) that aims to accelerate the development of Deep Fake detection technologies. DFDC is a global competition that invites researchers and developers to create and test algorithms that can detect Deep Fakes in videos. DFDC also provides a large and diverse dataset of real and fake videos for training and testing purposes.
Microsoft: Microsoft has developed a tool called Video Authenticator that can analyze videos and images and provide a confidence score of how likely they are to be manipulated. Video Authenticator uses a machine learning model that is trained on a large dataset of real and fake videos, and can detect subtle cues such as fading, blurring, or inconsistent lighting that indicate manipulation. Microsoft also provides a browser extension that can apply the same technology to online content.
X (formerly known as Twitter): X/Twitter has implemented a policy that requires users to label synthetic or manipulated media that are shared on its platform. The policy also states that X/Twitter may remove or flag such media if they are likely to cause harm or confusion. Twitter uses a combination of human review and automated systems to enforce the policy and provide context and warnings to users.
Deeptrace: Deeptrace is a startup that specializes in detecting and analyzing Deep Fakes and other forms of synthetic media. Deeptrace offers a range of products and services, such as Deeptrace API, Deeptrace Dashboard, and Deeptrace Intelligence, that can help clients identify, monitor, and respond to malicious or harmful uses of Deep Fakes. Deeptrace also publishes reports and insights on the trends and developments of synthetic media.
These are just some of the examples of how companies are tackling the problem of Deep Fakes. There are also other initiatives and collaborations from academia, government, civil society, and media that are working to raise awareness, educate users, and promote ethical and responsible use of synthetic media.
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Challenges in implementing ethical Artificial Intelligence
Artificial intelligence (AI) is a powerful technology that can enhance human capabilities, improve social welfare, and solve complex problems. However, AI also poses significant ethical challenges that need to be addressed to ensure the responsible use of AI technologies.
One of the main ethical challenges of AI is bias and fairness. Bias refers to the systematic deviation of an AI system from the truth or the desired outcome, while fairness refers to the ethical principle that similar cases should be treated similarly by an AI system. Bias and fairness are intertwined, as biased AI systems can lead to unfair or discriminatory outcomes for certain groups or individuals [1].
Bias and fairness issues can arise at various stages of an AI system’s life cycle, such as data collection, algorithm design, and decision making. For example, an AI system that relies on data that is not representative of the target population or that reflects existing social biases can produce skewed or inaccurate results. Similarly, an AI system that uses algorithms that are not transparent, interpretable, or explainable can make decisions that are not justified or understandable to humans. Moreover, an AI system that does not consider the ethical implications or the social context of its decisions can cause harm or injustice to be affected parties [1].
To address bias and fairness issues, several strategies can be employed, such as:
Data auditing: Checking the quality, diversity, and representativeness of the data used by an AI system and identifying and correcting any potential sources of bias.
Algorithm auditing: Testing and evaluating the performance, accuracy, and robustness of the algorithms used by an AI system, and ensuring they are transparent, interpretable, and explainable.
Impact assessment: Assessing the potential impacts and risks of an AI system’s decisions on various stakeholders, and ensuring they are aligned with ethical principles and societal values.
Human oversight: Providing mechanisms for human intervention, review, or feedback in the AI system’s decision-making process, and ensuring accountability and redress for any adverse outcomes [1].
Privacy: Another ethical challenge of AI is privacy. Privacy refers to the right of individuals to control their personal information and how it is collected, used, and shared by others. Privacy is a fundamental human right that is essential for human dignity, autonomy, and freedom [3].
Privacy Issues
Privacy issues can arise when AI systems process vast amounts of personal data, such as biometric, behavioral, or location data, that can reveal sensitive or intimate details about individuals. For example, an AI system that uses facial recognition or voice analysis to identify or profile individuals can infringe on their privacy rights. Similarly, an AI system that collects or shares personal data without the consent or knowledge of the individuals can violate their privacy rights. Moreover, an AI system that does not protect the security or confidentiality of the personal data it handles can expose individuals to the risk of data breaches or misuse3.
To address privacy issues, several strategies can be employed, such as:
Principle
Description
Privacy by design
Incorporating privacy principles and safeguards into the design and development of an AI system and minimizing the collection and use of personal data.
Privacy by default
Providing individuals with the default option to opt-in or opt-out of the data collection and use by an AI system and respecting their preferences and choices.
Privacy by law
Complying with the relevant laws and regulations that govern the privacy rights and obligations of the AI system and its users and ensuring transparency and accountability for any data practices.
Privacy by education
Raising awareness and educating the AI system and its users about the privacy risks and benefits of the AI system and providing them with the tools and skills to protect their privacy 3.
The accountability Challege
A third ethical challenge of AI is accountability. Accountability refers to the obligation of an AI system and its users to take responsibility for the decisions and actions of the AI system, and to provide explanations or justifications for them. Accountability is a key principle that ensures trust, legitimacy, and quality of an AI system [2].
Accountability issues can arise when an AI system makes decisions or actions that have significant impacts or consequences for humans or society, especially when they lead to unintended or harmful outcomes. For example, an AI system that makes medical diagnoses or legal judgments can affect the health or rights of individuals. Similarly, an AI system that operates autonomously or independently can cause damage or injury to humans or property. Moreover, an AI system that involves multiple actors or intermediaries can create ambiguity or confusion about who is responsible or liable for the AI system’s decisions or actions [2].
To address accountability issues, several strategies can be employed, such as:
Governance: Establishing clear and consistent rules, standards, and procedures for the development, deployment, and use of an AI system, and ensuring compliance and enforcement of them.
Traceability: Maintaining records and logs of the data, algorithms, and processes involved in the AI system’s decision making, and enabling verification and validation of them.
Explainability: Providing meaningful and understandable explanations or justifications for the AI system’s decisions or actions and enabling feedback and correction of them.
Liability: Assigning and apportioning the legal or moral responsibility or liability for the AI system’s decisions or actions and ensuring compensation or remedy for any harm or damage caused by them [2].
A fourth ethical challenge of AI is safety and security. Safety refers to the ability of an AI system to avoid causing harm or damage to humans or the environment, while security refers to the ability of an AI system to resist or prevent malicious attacks or misuse by unauthorized parties. Safety and security are essential for ensuring the reliability, robustness, and resilience of an AI system1.
Safety and security issues can arise when an AI system is exposed to errors, failures, uncertainties, or adversities that can compromise its functionality or performance. For example, an AI system that has bugs or glitches can malfunction or behave unpredictably. Similarly, an AI system that faces novel or complex situations can make mistakes or errors. Moreover, an AI system that is targeted by hackers or adversaries can be manipulated or corrupted [1].
To address safety and security issues, several strategies can be employed, such as:
Testing: Conducting rigorous and extensive testing and evaluation of the AI system before, during, and after> its deployment, and ensuring its quality and correctness.
Monitoring: Observing and supervising the AI system’s operation and behavior and detecting and reporting any anomalies or problems.
Updating: Maintaining and improving the AI system’s functionality and performance and fixing and resolving any issues or defects.
Defense: Protecting and securing the AI system from malicious attacks or misuse and mitigating and recovering from any damage or harm caused by them [1].
In conclusion, AI is a powerful technology that can bring many benefits to humans and society, but it also poses significant ethical challenges that need to be addressed to ensure the responsible use of AI technologies. By applying various strategies and methods, such as data auditing, algorithm auditing, impact assessment, human oversight, privacy by design, privacy by default, privacy by law, privacy by education, governance, traceability, explainability, liability, testing, monitoring, updating, and defense, we can mitigate the ethical challenges of AI and foster trust, confidence, and acceptance of AI systems.
Implementing ethical AI presents several challenges that need to be addressed to ensure the responsible use of AI technologies. Here are some of the key challenges:
Bias and Fairness: Ensuring AI systems are free from biases and make fair decisions is a significant challenge. This includes addressing biases in data, algorithms, and decision-making processes [1].
Transparency: AI systems often operate as “black boxes,” with opaque decision-making processes. Making these systems transparent and understandable to users and stakeholders is a complex task [2].
Privacy: Protecting the privacy of individuals when AI systems process vast amounts of personal data is a critical concern. Balancing data utility with privacy rights is a delicate and challenging issue [3].
Accountability: Determining who is responsible for the decisions made by AI systems, especially when they lead to unintended or harmful outcomes, is a challenge. Establishing clear lines of accountability is essential [2].
Safety and Security: Ensuring AI systems are safe and secure from malicious use or hacking is a challenge, especially as they become more integrated into critical infrastructure [1].
Ethical Knowledge: There is a lack of ethical knowledge among AI developers and stakeholders, which can lead to ethical principles being misunderstood or not applied correctly [2].
Regulatory Compliance: Developing and enforcing regulations that keep pace with the rapid advancements in AI technology is a challenge for policymakers and organizations [4].
Social Impact: AI technologies can have profound impacts on society, including job displacement and changes in social dynamics. Understanding and mitigating these impacts is a complex challenge [5].
These challenges highlight the need for ongoing research, dialogue, and collaboration among technologists, ethicists, policymakers, and the public to ensure ethical AI implementation.
Ethical uses of AI are crucial for ensuring that the technology benefits society while minimizing harm. Here are some key points regarding the ethical use of AI:
Global Standards: UNESCO has established the first-ever global standard on AI ethics with the ‘Recommendation on the Ethics of Artificial Intelligence’, adopted by all 193 Member States1. This framework emphasizes the protection of human rights and dignity, advocating for transparency, fairness, and human oversight of AI systems1.
Algorithmic Fairness: AI should be developed and used in a way that avoids bias and discrimination. This includes ensuring that algorithms do not replicate stereotypical representations or prejudices2.
Transparency and Accountability: AI systems should be transparent in their decision-making processes, and there should be accountability for the outcomes they produce3.
Privacy and Surveillance: Ethical AI must respect privacy rights and avoid contributing to invasive surveillance practices4.
Human Judgment: The role of human judgment is paramount, and AI should not replace it but rather augment it, ensuring that human values and ethics guide decision-making4.
Environmental Considerations: AI development should also consider its environmental impact and strive for sustainability1.
Guiding Principles: Stakeholders, from engineers to government officials, use AI ethics as a set of guiding principles to ensure responsible development and use of AI technology5.
Social Implications: The ethical and social implications of AI use include establishing ethical guidelines, enhancing transparency, and enforcing accountability to harness AI’s power for collective benefit while mitigating potential harm6.
These points reflect a growing consensus on the importance of ethical considerations in AI development and deployment, aiming to maximize benefits while addressing potential risks and ensuring that AI serves the common good.
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