Trade Bodies to Enforce AI Licensing and Copyright Legislation
Trade associations like the newly formed Dataset Providers Alliance (DPA) could play a crucial role in enforcing legislation such as the NO FAKES Act and the Generative AI Copyright Disclosure Act.
Seven companies specializing in licensing music, images, videos, and other data for training AI systems have joined forces to create the DPA. This association aims to promote responsible and ethical licensing of intellectual property, addressing a growing concern for developers of generative AI models and the enterprises that use them. The origins of some datasets used in AI training are legally and ethically ambiguous, causing musicians, authors, actors, and website operators to protest unauthorized use of their content.
Founders and Objectives
The DPA’s founding members include Rightsify, Global Copyright Exchange (GCX), vAIsual, Calliope Networks, ado, Datarade, and Pixta AI. Their mission is to standardize intellectual property licensing for AI and machine learning (ML) datasets, promote ethical data practices, foster industry collaboration, advocate for content creators’ rights, and support innovation in AI and ML technologies while protecting intellectual property.
“The DPA will serve as a powerful voice for dataset providers, ensuring that the rights of content creators are protected while AI developers get access to large amounts of high-quality AI training data,” said Alex Bestall, CEO of Rightsify and GCX.
Implications for AI Companies
AI companies often train their models using vast quantities of content sourced from the internet without the consent of original creators or rights holders, leading to numerous disputes. There are also growing concerns over unauthorized digital replication of individuals’ voices or likenesses, as highlighted by Scarlett Johansson’s complaint about an OpenAI bot mimicking her voice.
In response to these issues, the US introduced the NO FAKES Act last year and the Generative AI Copyright Disclosure Act this year. Trade associations like the DPA may support the enforcement of such legislation and advocate for similar measures.
Compliance and Operational Adjustments
Charlie Dai, VP and principal analyst at Forrester, notes that while these regulations emphasize the need for transparency and responsible AI practices, they will also impact compliance costs and necessitate operational adjustments. “To comply with the Generative AI Copyright Disclosure Act, organizations will need to allocate workforce and budget for tracking and reporting copyrighted content, ensuring transparency, and complying with the disclosure requirements,” said Dai. This will require new operational processes to document and disclose copyright-related information during dataset creation.
Effective risk management will be crucial for addressing legal and reputational risks, and innovation strategies may need adjustments to meet regulatory standards. The situation could become even more complex for multinational companies.
Challenges for Multinational Firms
Swapnil Shende, associate research manager for AI at IDC Asia/Pacific, points out that multinational organizations face additional complications. “Established markets like the US and Europe lead the way in setting regulatory standards that may influence other countries, but each nation will have to customize its rules to fit local markets,” Shende said. This regulatory diversity presents challenges for multinational firms operating across borders, requiring them to navigate varying compliance requirements while striving for consistency.
Strategic Adjustments
With the increasing demand for licensed data amidst ongoing copyright disputes, enterprise tech companies may need to adjust their strategies for acquiring and using training data to mitigate legal and financial risks.
Dai advises that AI security and governance leaders align with business strategy and develop comprehensive risk mitigation frameworks. These frameworks should identify, evaluate, and address potential risks in AI projects and initiatives. “They should implement robust security measures to safeguard sensitive data and comply with regulations, revisiting the capabilities of their data and AI vendors on AI compliance in the meantime,” Dai said.
Shende adds that enterprises should prioritize licensed data from compliant providers and verify ownership with clear contracts and indemnification clauses. “By embracing rigorous standards for data sourcing and management, enterprises can set new industry benchmarks, enhance their operational integrity, and build greater trust with consumers and regulatory bodies,” Shende said. “Their ongoing engagement and innovation in ethical AI practices will be crucial in achieving sustainable growth and maintaining a competitive edge in the technology sector.”
Source: cio.com
