So, if you’re thinking about starting to use artificial intelligence in your company, consider the potential risks, including legal issues around data protection, intellectual property, and liability, before rushing in to adopt AI. Through a strategic risk management framework, businesses can mitigate key compliance risks and maintain customer trust while taking advantage of recent advances in AI.
Check your training data
First, assess whether the data used to train the AI model complies with applicable laws such as India's 2023 Act. Digital Personal Data Protection Bill and the European Union General Data Protection Regulationdata ownership, consent, and compliance. Timely legal review to determine whether collected data can be lawfully used for machine learning purposes can prevent regulatory or legal issues later.
This legal assessment will include a detailed review of the company's existing terms of use, privacy policy statements, and other customer-facing terms and conditions to determine what permissions have been obtained from customers or users. The next step will be to determine whether such permissions are sufficient to train AI models. If not, additional customer notices or consents may be required.
Different types of data will have different issues around consent and liability – for example, is the data personally identifiable information, synthetic content (usually generated by another AI system), or someone else's intellectual property? Data minimization (only use what you need) is a good principle to apply at this stage.
Be careful how you get your data. OpenAI has been sued Data scraping is done for the purpose of scraping personal data to train algorithms, and as explained below, data scraping may raise copyright infringement concerns, which may be subject to U.S. civil procedure laws. Violate the website's terms of useLaws focused on U.S. security, e.g. Computer Fraud and Abuse Act It could conceivably also be applied outside national territory to prosecute foreign entities alleged to have stolen data from secure systems.
Beware of intellectual property issues
The New York Times recently Sued OpenAI The company sued, alleging copyright infringement and trademark dilution for using newspaper content for training purposes. This case serves as an important lesson for all companies involved in AI development: be careful when using copyrighted content for training models, especially when you can obtain a license from the owner. apple Other companies Licensing options consideredThis is likely to be the best way to mitigate potential copyright infringement claims.
To alleviate copyright concerns, Microsoft Offered Supporting the output of the AI assistantThe company is committed to defending its customers against claims of copyright infringement, and such intellectual property protection could become an industry standard.
Companies also need to consider the possibility of inadvertent leaks. Confidential and Trade Secret Information AI products can empower employees to use the following technologies within their companies: Chat GPT (for text) and Github Copilot (For code generation) Companies say that generative AI tools like this Capture user prompts and output It is then used as training data to further improve the model. Fortunately, generative AI companies usually offer more secure services and the ability to opt out of training their models.
Beware of hallucinations
Copyright infringement claims and data protection issues also arise when generative AI models spit out training data as output.
In many cases, “Overfitting” modelsEssentially, it's a training flaw where the model memorizes specific training data, rather than learning general rules about how to respond to prompts. Memorization can cause an AI model to repeat training data as output, which could be disastrous from a copyright and data protection perspective.
Memorization can also lead to inaccuracies in the output, sometimes called “hallucinations.” An interesting case is The New York Times The reporter Bing AI chatbot experiment Sydney made the tweet go viral when she professed her love for reporters. The viral incident sparked discussion about the need to monitor the deployment of such tools, especially by younger users who are more likely to attribute human characteristics to AI.
Hallucinations have also caused problems in the professional field: for example, two lawyers were punished for submitting legal briefs created by ChatGPT that cited non-existent precedents.
Such illusions demonstrate why companies need to test and validate their AI products to avoid legal risks as well as reputational damage. Many companies are dedicating engineering resources to Content Filter Development This increases accuracy and reduces the likelihood of offensive, abusive, inappropriate, or libelous output.
Tracking Data
When you have access to personally identifiable user data, it is important that you handle it securely. You also need to ensure that you can delete the data and prevent it from being used for machine learning purposes upon user request or upon regulator or court order. Maintaining data provenance and ensuring a robust infrastructure is of paramount importance to all AI engineering teams.
“Through a strategic risk management framework, companies can leverage recent advances in AI while mitigating key compliance risks and maintaining customer trust.”
These technical requirements are associated with legal risks. In the United States, Federal Trade Commission I have relied on Repaying algorithmic gainsThis is a punitive measure. If a company violates applicable law when collecting training data, it must delete not only the data but also the models that were trained on the tainted data. We recommend that you keep records of exactly which datasets were used to train different models.
Beware of bias in AI algorithms
One of the great challenges of AI is the potential for harmful biases to be ingrained in algorithms. If biases aren't mitigated before products launch, applications could perpetuate or exacerbate existing discrimination.
For example, predictive policing algorithms employed by US law enforcement agencies have been shown to reinforce common biases. Blacks and Latinos This means communities are unfairly targeted.
Intended use Loan Approval or RecruitmentBiased algorithms can lead to discriminatory outcomes.
Experts and policymakers say it's important for companies to strive for fairness in AI, as algorithmic bias can have concrete and troubling effects on civil liberties and human rights.
Be transparent
Many companies are establishing ethics review boards to ensure their business practices are aligned with principles of transparency and accountability. Best practices include being transparent about data use and accurately describing the capabilities of AI products to customers.
US regulators Overpromising AI's capabilities It's in the marketing materials. Regulators Companies that issued warnings We oppose quiet, unilateral changes to data license terms in contracts as a means of expanding access to customer data.
Adopt a global, risk-based approach
Many AI governance experts Take a risk-based approach Approaching AI development. This strategy involves mapping internal AI projects, scoring them on a risk scale, and implementing mitigating actions. Many companies incorporate risk assessments into existing processes that measure the privacy-based impact of proposed features.
When establishing AI policies, it is important to take into account the latest international laws to ensure that the rules and guidelines you are considering are appropriate to mitigate risks globally.
Regional approaches to AI governance could be costly and error-prone. The European Union recently Artificial Intelligence Law It contains detailed requirements for companies that develop and use AI, and similar legislation It is likely to appear in Asia soon..
Continuing legal and ethical review
Legal and ethical reviews are important throughout the lifecycle of an AI product: during model training, testing and development, release, and beyond. Companies must proactively consider how they implement AI to eliminate inefficiencies while maintaining the confidentiality of business and customer data.
For many, AI is uncharted territory, and companies will need to invest in training programs to help employees understand how to make the most of the new tools and use them to drive business forward.
