Entertainment and Media Guide to AI

Legal issues in AI part 2 - Judicial scales icon

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The explosive adoption of artificial intelligence and, in particular, large language models as a result of the success of OpenAI’s ChatGPT, Google’s Bard and GitHub’s Copilot have not gone unnoticed by entrepreneurs and those who fund them. Big tech and media companies see making an investment in this technology as getting a “seat at the table.” As nearly every venture capitalist or private equity investor will likely see and have to quickly evaluate many “we use AI” claims in investment documents, due diligence – especially legal due diligence – may become increasingly important for managing fraud and other nascent risks, such as false intellectual property claims. In this regard, lawyers with technology, consulting and similar backgrounds may have an important role to play. At the same time, the pressure for certain deals may be greater as competition for the best deals increases. With this in mind, we set out some of the emerging due diligence considerations for investors and those seeking investment to be prepared for generally. Many are familiar and involve engaging specialists familiar with the issues that can – and have – blocked or stopped machine learning, AI and data-centric investments. Others are more specific to the technology itself and require a broader lens that can grasp emerging trends. What remains clear is that data and rights to its collection, use and disclosure will remain centrally important, especially for businesses seeking to scale globally where legal rules differ widely – a trend we expect to continue.

And as any technology start-up will know, the only constant is change. The same goes for AI companies – they exist in a field where the technology and regulation of the technology will change and are expected to change. The response is then one of watchfulness – to always keep an eye on developments.

When considering investments in AI, there are several key due diligence issues that should be thoroughly examined. These issues are crucial to assess the potential risks, opportunities and overall viability of the AI investment. Key due diligence considerations include:

  1. Technology and intellectual property. Evaluate the technology behind the AI product or service. Understand the uniqueness and competitive advantage of the AI algorithms, models or software. Assess the IP protection, such as patents, copyrights or trade secrets, to ensure it is robust and defensible.
  2. Data quality and access. AI heavily relies on data. Assess the quality, quantity and diversity of the data used to train the AI system. Understand the data sources, including whether they are proprietary, licensed or publicly available. Evaluate the data collection processes, data privacy compliance and potential risks of biased or incomplete data.
  3. Team and expertise. Evaluate the team behind the AI company. Assess their experience, qualifications and expertise in AI development, data science and relevant domains. Look for a track record of successful AI projects or prior industry experience. Consider the depth of the team, including technical, research and business expertise.
  4. Business model and market opportunity. Assess the AI company’s business model, revenue streams and market potential. Understand the target market, competition and barriers to entry. Evaluate the scalability and sustainability of the business model, as well as potential risks and challenges in commercializing the AI technology.
  5. Regulatory and ethical considerations. Understand the regulatory landscape and potential legal implications related to AI. Assess compliance with data protection, privacy regulations and any industry-specific regulations. Evaluate the company’s approach to ethical considerations, such as bias mitigation, transparency, fairness and accountability.
  6. Performance and validation. Seek evidence of the AI system’s performance, accuracy and reliability. Request access to independent validation or third-party evaluations of the AI technology. Consider pilot projects, customer testimonials or case studies that demonstrate the value and effectiveness of the AI solution.
  7. Financials and funding. Analyze the financial health of the AI company. Review financial statements, revenue projections and funding history. Assess the company’s financial viability, burn rate and potential need for additional funding. Understand the ownership structure and any existing investors.
  8. Partnerships and collaborations. Assess existing partnerships or collaborations that the AI company has established. Evaluate the strategic alliances, customer relationships or integration with other technologies. Understand the potential risks and benefits of these partnerships for the AI investment.
  9. Risk management and cybersecurity. Evaluate the AI company’s approach to risk management and cybersecurity. Understand the measures in place to protect sensitive data, ensure system integrity and prevent unauthorized access. Assess the company’s response plans for potential risks, such as system failures and security breaches.
  10. Exit strategy. Consider the potential exit options for the investment. Understand the company’s plans for growth, acquisition or IPO. Evaluate the potential for liquidity and return on investment.

By conducting thorough due diligence on these key areas, investors can gain a comprehensive understanding of the AI company, its technology and the associated risks and opportunities. This information will help investors make informed decisions and mitigate potential pitfalls in AI investments.

Key takeaways
  • Due diligence is a key evolving feature for AI investments
  • Data quality, cybersecurity and ethics are more important than ever
  • Keep tabs on changes