So you want to use artificial intelligence in your company. Before rushing to adopt AI, consider the potential risks, including legal issues related to data protection, intellectual property, and liability. A strategic risk management framework can help companies leverage the latest AI advancements while mitigating key compliance risks and maintaining customer trust.
Check training data
First, assess whether the data used to train AI models complies with relevant laws, such as India’s Act of 2023. Digital Privacy Protection Act And the European Union's General Data Protection Regulation, which addresses data ownership, consent and compliance. Timely review of whether collected data can be legally used for machine learning purposes can help prevent regulatory and legal issues later.
This legal evaluation includes an in-depth analysis of the company's existing terms of service, privacy policy statements, and other contractual terms relevant to the customer to determine what rights it has obtained from the customer or user. The next step is to determine whether such permissions are sufficient for training AI models. Otherwise, additional customer notification or consent may be required.
Consent and liability issues vary depending on the type of data. For example, consider whether the data is personally identifiable information, synthetic content (usually generated by another AI system), or someone else's intellectual property. Data minimization, using only what is necessary, is a good principle to apply at this stage.
Look carefully at how the data was obtained. OpenAI is being sued It involves scraping personal data to train algorithms. And, as explained below, data scraping may raise questions of copyright infringement. Additionally, since scraping may occur, the U.S. Rules of Civil Procedure may apply. violate the website's terms of service;. Laws Focused on America's Security Computer Fraud and Abuse Act It may also be applied outside a country's territory to prosecute foreign companies allegedly stealing data from security systems.
Beware of intellectual property issues
new york times Recently Sue OpenAI Use of newspaper content for educational purposes based on claims of copyright infringement and trademark dilution. This lawsuit holds important lessons for all companies working on AI development. Be careful when using copyrighted content to train your model, especially if you can license that content from the owner. apologize And other companies Licensing Options ConsideredThis is likely to emerge as the best way to mitigate potential copyright infringement claims.
To reduce concerns about copyright microsoft suggested to Standing behind the results of AI assistants, we are committed to protecting our customers from potential copyright infringement lawsuits. This intellectual property protection could become an industry standard.
Companies should also consider the possibility of inadvertent information leaks. Confidential and Trade Secret Information If your AI product allows your employees to use the following technologies internally: ChatGPT (for text) and Github co-pilot (For code generation) Companies should note that these generative AI tools are frequently used. Get user prompts and output It is used as training data to further improve the model. Fortunately, generative AI companies typically offer more secure services and the ability to refuse model training.
Beware of Hallucinations
Copyright infringement claims and data protection issues also appear when generative AI models export training data as output.
This often results in: “Overfitting” model, essentially a learning flaw in which the model remembers specific training data instead of learning general rules for how to respond to prompts. Memorization can cause AI models to regurgitate training data as output, which can be disastrous from a copyright or data protection perspective.
Memorization may lead to inaccurate results, also known as “hallucinations.” In one interesting case new york times The reporter Bing AI chatbot experiment Sydney confesses her love for the reporter. The viral incident has sparked discussion about the need to monitor how these tools are deployed, especially by younger users who are more likely to attribute human characteristics to AI.
Hallucinations have also caused problems in the professional realm. For example, two lawyers were sanctioned after submitting legal briefs written by ChatGPT that cited non-existent case law.
These hallucinations show why companies need to test and validate their AI products to avoid legal risks as well as reputational damage. Many companies have invested engineering resources. Content filter development Improve accuracy and reduce the likelihood of offensive, offensive, inappropriate or defamatory content.
data tracking
If you have access to personally identifiable user data, it is important that you handle that data securely. You must also ensure that you can delete data and prevent its use for machine learning purposes upon user request or direction from a regulator or court. Maintaining data provenance and ensuring a robust infrastructure are of utmost importance to any AI engineering team.
“A strategic risk management framework allows companies to leverage the latest AI advancements while mitigating key compliance risks and maintaining customer trust.”
These technical requirements are linked to legal risks. In the United States, regulatory authorities, including: Federal Trade Commission have been dependent algorithmic distortion, punitive measures. If a company violates relevant laws while collecting training data, it must delete not only the data but also the model that learned the contaminated data. It is a good idea to keep accurate records of the datasets used to train the various models.
Beware of bias in AI algorithms
One of the key challenges of AI is the potential for harmful biases to become ingrained within the algorithms. If bias is not mitigated before a product is launched, the application may perpetuate or even exacerbate existing discrimination.
For example, predictive policing algorithms used by U.S. law enforcement agencies have been shown to reinforce widespread bias. black and latino Communities are disproportionately targeted.
When to use loan approval or employmentBiased algorithms can lead to discriminatory results.
Experts and policymakers say it is important for companies to strive for fairness in AI. Algorithmic bias can have real and troubling implications for civil liberties and human rights.
transparently
Many companies have established ethics review committees to ensure that their business practices are consistent with principles of transparency and accountability. Best practices include being transparent about data use and accurately explaining the capabilities of your AI product to customers.
U.S. regulators frown on companies that: Overpromising AI capabilities In marketing materials. to regulatory agencies Companies warned We oppose silently and unilaterally changing the data license terms of contracts as a way to expand access to customer data.
Take a global, risk-based approach.
Recommended by many AI governance experts Take a risk-based approach To AI development. The strategy includes mapping the company's AI projects, scoring them according to risk ratings, and implementing mitigation measures. Many companies integrate risk assessments into their existing processes to measure the privacy-based impacts of proposed features.
When formulating AI policies, it is important to ensure that the rules and guidance being considered take into account up-to-date international law and are appropriate for mitigating risks in a global manner.
Localized approaches to AI governance are expensive and error-prone. The European Union (EU) was recently passed. artificial intelligence law It contains detailed requirements for companies developing and using AI and has similar laws in place. It will likely appear soon in Asia as well..
Continue with legal and ethical review.
Legal and ethical reviews are important throughout the life cycle of an AI product, from model training, testing and development, to launch, and even beyond. Companies must actively consider how to implement AI to eliminate inefficiencies while maintaining the confidentiality of business and customer data.
For many people, AI is a new frontier. Companies need to invest in training programs to ensure employees understand how to get the most out of new tools and use them to drive their business.