Digital Marketing Using Generative AI (Foundational)

What Are Generative AI Ethics?

Generative AI ethics, similar to traditional artificial intelligence ethics, are guiding principles and best practices for developing and using generative AI technology in a way that does no harm. Some of the most important areas that generative AI ethics covers include the following:

• Consumer data privacy and security.
• Regulatory compliance and appropriate use.
• Copyright and data ownership.
• Data and model training transparency.
• Unbiased training processes.
• Environmentally-conscious AI model usage.

Generative AI Laws and Frameworks

While no major generative AI ethical frameworks or policies have passed into law at this point, several pieces of legislation are in the works. Here are some of the foremost examples:

1.      European Union: The EU is the furthest along in its regulation of generative AI, with Italy even briefly banning OpenAI until the company enhanced its data privacy capabilities and standards. The EU’s AI Act is a proposed law that would divide AI apps into unacceptable risk, high risk, and low-to-no-risk categories, with special attention being paid to generative AI and copyright/ownership concerns.

2.      United States: While the U.S. has no official artificial intelligence legislation in the works, a handful of frameworks and best practices have been established that indicate a law could go into effect in the future. Examples include the Biden administration’s Blueprint for an AI Bill of Rights, NIST’s AI Risk Management Framework, and copyright registration guidance for AI-generated content.

3.      United Kingdom: The United Kingdom is likely to pursue AI regulation at a slower pace than the EU but at a faster pace than the United States. The country already has a policy paper called AI regulation: a pro-innovation approach that summarizes its plans for AI regulation.

Ethical Concerns and Challenges with Generative AI

Generative AI can accomplish remarkable feats, like support drug discovery and cancer diagnostics, create beautiful artwork and videos, and guide both consumer and enterprise research in online knowledge bases and search engines. However, generative AI is new and generally unregulated, meaning there are many ways it can be misused. These are some of the biggest ethical concerns surrounding generative AI today:

Copyright and Stolen Data Issues

For generative AI models to produce logical, human-like content regularly, these tools need to be trained on massive datasets from a variety of sources. Unfortunately, this training process has been obscured by most AI companies, and several have used the original artwork, content, and personal data of creators and other consumers in training datasets without the creators’ permission. MidJourney and Stability AI’s Stable Diffusion are two tools that are currently under fire for these issues. Personal and corporate data of other types have also been unintentionally introduced into generative AI training algorithms, which exposes users and corporations to potential theft, data loss, and violations of privacy.

Hallucinations, Bad Behaviour, and Inaccuracies

Generative AI tools are trained to give logical, helpful outputs based on users’ queries, but on occasion, these tools generate offensive, inappropriate, or inaccurate content. So-called “hallucinations” are a unique problem that these tools face: in essence, a large language model gives a confident response to a user’s question that is both entirely wrong or irrelevant and seems to have no basis in the data on which it was trained. Researchers are only just beginning to understand why these hallucinations happen and how — or if — they can be stopped on a reasonable scale.

Other bad behaviours from generative AI tools include the following:

• Generating pornographic images of users without their explicit request for this kind of imagery.
• Making racist and/or culturally insensitive remarks.
• Spreading misinformation — both in written content and deep-fake imagery.

Biases in Training Data

Like other types of artificial intelligence, a generative AI model is only as good as its training data is diverse and unbiased. Biased training data can teach AI models to treat certain groups of people disrespectfully, spread propaganda or fake news, and/or create offensive images or content that targets marginalized groups and perpetuates stereotypes.

Cybersecurity Jailbreaks and Workarounds

Although generative AI tools can be used to support cybersecurity efforts, they can also be jailbroken and/or used in ways that put security in jeopardy.

Environmental Concerns

Generative AI models use up massive amounts of energy very quickly, both as they’re being trained and as they later handle user queries.The latest generative AI tools have not had their carbon footprints studied as closely as other technologies, yet even as early as 2019, research indicated that BERT models had carbon emissions that roughly equated to the emissions of a roundtrip flight for one person in an airplane. Keep in mind this amount is just the emissions from one model during training on a GPU.As these models continue to grow in size, use cases, and sophistication, their environmental impact will surely increase if strong regulations aren’t put in place.

Limited Transparency

Companies like OpenAI are working hard to make their training processes more transparent, but for the most part, it isn’t clear what kinds of data are being used and how they’re being used to train generative AI models.This limited transparency not only raises concerns about possible data theft or misuse but also makes it more difficult to test the quality and accuracy of a generative AI model’s outputs and the references on which they’re based.

Why Are Generative AI Ethics Important?

Generative AI ethics are important because, as with many other emerging technologies, it is all too easy to unintentionally use this technology in a harmful way.Creating an ethical framework and guidelines for how to use generative AI can help your organization do the following:

• Protect customers and their personal data.
• Protect proprietary corporate data.
• Protect creators and their ownership and rights over their work.
• Protect the environment.
• Prevent dangerous biases and falsehoods from being proliferated.

Tips for Using Generative AI Ethically

Generative AI can be used in thoughtful, effective ways in the workplace if your leadership is willing to set up safety nets to protect employees and customers from the technology’s downsides. Consider following these best practices and tips to get the most out of generative AI without compromising your company’s reputation or performance. These guidelines include employee training, transparency with customers, and rigorous fact checking.

Train Employees on the Appropriate Use of Generative AI

If employees are allowed to use generative AI in their daily work, it’s important to train them on what does and doesn’t count as appropriate use of the AI technology. Most important, train your staff on what data they can and absolutely cannot use as inputs in generative AI models. This will be especially important if your organization is subject to regional or industry-specific regulations.

Be Transparent with Your Customers

If generative AI is part of your organization’s internal workflow or operations, it’s best if your customers are aware of this, especially when it comes to their personal data and how it’s used. Explain on your website and to customers directly how you’re using generative AI to make your products and services better, and clearly state what steps you’re taking to further protect their data and best interests.

Implement Strong Data Security and Management Efforts

If your team wants to use generative AI to get more insights from sensitive corporate or consumer data, certain data security and data management steps should be taken to protect any data used as inputs in a generative AI model. To get started, data encryption, digital twins, data anonymization, and similar data security techniques can be helpful methods for protecting your data while still getting the most out of generative AI.

Fact-Check Generative AI Responses

Generative AI tools may seem like they’re “thinking” and generating truth-based answers, but what they’re trained to do is produce the most logical sequence of content based on the inputs users give.Though they generally give accurate and helpful responses through this training, generative AI tools still can produce false information that sounds true. Make sure every member of your team is aware of this shortcoming of generative AI. Staffers should not solely rely on the tool for their research needs. Online and industry-specific resources should be used to fact-check all responses that you receive from a generative AI tool.

Establish and Enforce an Acceptable Use Policy in Your Organization

An acceptable use policy should cover in detail how your employees are allowed to use artificial intelligence in the workplace. If you’re not sure where to start when developing your AI use policy, take a look at these resources for guidance and support:

• NIST’s Artificial Intelligence Risk Management Framework.
• The European Union’s Ethics guidelines for trustworthy AI.
• The Organization for Economic Cooperation and Development’s OECD AI Principles.