Countless professionals ranging from academics and corporate leaders to policymakers are looking for methods to make good use of generative AI as a technology. Even businesses today are taking a close look at generative AI.
This technology has the capability of producing top-notch content in the best possible manner; images, code, text, and videos with minimal effort.
The modern age has fewer human resource needs. Companies are expected to produce content quickly. They can also do work at lower costs. This creates new content that was previously expensive.
Generative AI can handle huge volumes of data, It can also help with the following activities:
- Summing up articles.
- Creating email drafts.
- Making initial drafts of code.
- Image and video generation.
- Debugging code in programs.
These tasks do make use of intellectual property but this comes with a risk. The organization may be exposed to the risk of fraud, intellectual property theft, or theft of private information.
How Much Does it Cost to Make a Generative AI Tool?
Making a Generative AI tool takes a lot of effort, money, and time. A lot of companies will rely on generative AI solutions made by external third-party companies, especially OpenAI and Stability AI. The following potential risks cannot be ignored:
- Errors.
- Potential cases and incidents of fraud.
- Intellectual property theft.
The following also need to be carefully considered:
- Developing indigenous AI technologies.
- Using Third-Party generative AI solutions.
Is Generative AI a Menace?
Generative AI is becoming popular. It has become part of mainstream technologies. Yet developing and deploying AI ethically is important and many companies want to not only use it but also protect themselves from it.
Let’s now examine the challenges and risks generative AI can bring. We will also see how companies can identify them and implement AI ethically.
What Risks Do Organizations Face When Using Generative AI?
Companies today want to use Generative AI but are somehow skeptical about it. Experts at well-known Dallas-based software firms, especially Branex, are skeptical about the use of generative AI. They have found chatbots and other AI-powered tools being used in cyberattacks, particularly phishing where almost authentic emails were used to deceive users.
Data and financial theft are among the commonly reported incidents involving AI in cyber attacks. Now, we will closely examine why organizations should be careful when using Generative AI for their internal functions.
Intellectual Property Theft
This is ranked as one of the topmost risks. Generative AI tools can cause intellectual property theft. The tech uses neural networks trained on large volumes of data. They are used for making new data too. Moreover, the tech can make new objects based on recognized data patterns in the following forms:
- Audio
- Images
- Text
- Video
This also includes data input by various users. The tech unfortunately retains it to learn continuously and build on its knowledge. The data can be used to answer prompts inputted by another person. This can potentially expose confidential information to the public.
Proprietary information can be lost too. The more businesses use it, the higher the chances their information can be accessed by outsiders.
Intentional and Unintentional Misuse by Employees
Generative AI can help companies achieve more effectiveness. However, it can be tempting to use it for the wrong purposes. Educators have sounded off concerns about students using generative AI for
- Completing assignments.
- Writing Essays.
- Finishing projects before time.
Teachers have reported incidents where students cheated via ChatGPT. They were used for generating erroneous essays. Hence, the output generated by these chatbots is inaccurate and riddled with misinformation. Advertising agencies are worried that copywriters may be using AI-powered chatbots to generate captions and slogans that are erroneous.
One related misuse would be for employees on contract to present work generated by AI as their own. They can charge unfair dues from the company for work they did not do. Another serious misuse is using generative AI for automating legal confirmations and reviews. That can bypass many ethical procedures. It can also cause serious breaches of compliance. It can harm the independence of some employees. This puts regulatory affairs in serious jeopardy.
The Outcomes are Usually Wrong
Using generative AI ethically has risks. The outcomes are usually inaccurate. This is why employees across many companies need to be vigilant about it. AI still requires intensive training and research. This makes many skeptical of AI itself. Emphasis on quality assurance is necessary to ensure wrong information isn’t published anywhere.
Generative AI development isn’t exactly top-class. The technology itself has limitations on learning new results and methods. Additional research and training is required. Plus its outcomes need constant monitoring to ensure things are on the right path.
If generative AI chatbots continue having error-strewn outcomes, then using them is futile. It can write fictitious material maligning innocent people. Here are two examples to note:
- Galactica is Meta’s generative AI chatbot. It was to summarize papers and studies for academics. Instead, it produced large amounts of wrong information. It even cited reputed scientists wrongly.
- CNET came under criticism for using generative AI quietly and unethically. It wrote 73 articles since November 2022 that were inaccurate. Despite covering it up by saying editors were working on it, the company was eventually exposed.
What External Risks Does the Use of Generative AI Create?
The risks of using Generative AI are external too. Dishonest users can create mischief and issues for businesses. However, these malicious actions are already conducted without using AI. Generative AI amplifies their effects. Cyber attacks have become easier to conduct and harder to detect.
Generative AI has been used to create deepfake images and videos. They have been realistic. They also do not leave forensic traces easily. These images and videos are edited digital media. It is often hard for humans and machines to detect. Deepfakes have put many individuals and companies in dangerous situations.
Generative AI poses a risk to cyber security. Hackers have used the technology to make realistic and sophisticated phishing scams. They’ve even made false credentials to hack into various systems and portals.
How Do We Counter Such Menacing Use of Generative AI?
Generative AI’s usage is still in the elementary stages. Companies can take steps to create responsible AI governance and oversight. They can set accountability measures for decisions regarding generative AI. These best generative AI tools are really worthwhile.
Here are some important governance constructs that can help reduce the negative implications of Generative AI:
- Improved policies and procedures for compliance, internal audit, and risk management.
- Improving data standards, practices, Intellectual Property protection, and workplace rules.
- Public Relations responses and actions.
- Improvements in Regulatory Affairs.
- Tactful actions by organizations regarding the usage of Generative AI.
- Providing the right training on technology.
- Developing employees’ viewpoints on Generative AI.
- Prioritizing risks.
- Assigning ownership to various stakeholder groups.
- Considering company principles on AI governance.
Over to You
Generative AI is a robust method. It has advanced analytics capabilities. This is why developing fluency in its usage is a must. Effective governance is also compulsory. Generative AI somehow isn’t trustworthy yet. It still needs further development.