The Ethical Challenges of Generative AI: What Companies Must Know

What are some ethical considerations when using Generative AI

Deepfakes

In April 2024, the NSE found itself in the midst of a troubling situation. On a Monday, they issued a warning about several fake audio and video clips making the rounds online. These clips were incredibly convincing, showing what seemed to be Chauhan, the NSE’s Managing Director and CEO, offering stock tips. The NSE logo was there, and his voice and facial expressions were eerily accurate.

But here’s the truth: these videos were created using advanced technology, designed to trick even the most cautious investors. The NSE was quick to alert the public, urging them not to trust these videos or the investment advice they appeared to offer. They emphasized that no NSE employee, including Chauhan, is authorized to recommend stocks or participate in stock trading.

So, next time you see a video that seems too good to be true, especially when it comes to your investments, think twice. It might just be another clever fake.

Misinformation like this can easily spread, damaging reputations and causing real harm. For companies, the risk is high—one misleading video could tank stock prices overnight.

To combat this, companies should invest in tools that can spot fake content. Big names like Facebook are already working on this, and it's a smart move for any business looking to protect its image and the trust of its customers.

As exciting as the AI technology is, it also comes with significant ethical challenges that companies need to be aware of. Here’s a look at some of the key issues and why they matter.

Bias and discrimination

Generative AI learns from data, and if that data is biased, the AI will be too. This means the AI could unintentionally reinforce harmful stereotypes or make unfair decisions. For example, facial recognition software that’s biased could misidentify people, leading to serious legal and PR problems.

The solution? Companies should make sure their AI is trained on diverse, unbiased data and regularly check for any unintended biases. Partnering with organizations that specialize in ethical AI, like OpenAI, can also help ensure these checks are thorough and effective.

Copyright and intellectual property

Generative AI can create content that closely resembles existing works, like a new song that sounds almost identical to a popular track. This raises serious copyright issues. Imagine the backlash if a famous artist accused a company of stealing their work—it could lead to expensive legal battles and damage the company’s reputation.

To avoid this, businesses must ensure that the data used to train their AI is properly licensed. They should also keep track of where their generated content comes from, which can help prove that no rules were broken.

Privacy and data security

Generative AI often uses vast amounts of data to learn, sometimes including personal information. This raises privacy concerns, especially if the AI ends up creating synthetic profiles that resemble real people. For instance, if an AI trained on medical records generates a profile that looks like a real patient, it could violate privacy laws.

Companies should anonymize data wherever possible and enhance their security measures to protect user information. Following principles like GDPR’s data minimization—using only the data that’s absolutely necessary—can help keep personal data safe.

Accountability

With so many steps involved in creating and using generative AI, it can be unclear who’s responsible when something goes wrong. Without clear accountability, problems can lead to legal trouble and hurt a company’s credibility. Consider the controversies around AI chatbots that have made inappropriate or harmful comments. Without a clear plan for who’s responsible, these issues can quickly spiral out of control.

Companies need to establish clear policies for the use of generative AI, similar to the guidelines social media platforms use to manage content. They should also set up systems where users can report any issues with AI-generated content.

The business angle

Ignoring these ethical issues isn’t just a moral mistake—it’s a business risk. Companies that overlook the ethical implications of generative AI could face damaged reputations, loss of customer trust, and financial instability.

Moving forward

The first step is awareness. Companies need to understand the ethical challenges generative AI presents and take proactive steps to address them. This means creating policies that promote responsible use, being transparent, and fostering a culture of ethical AI both within the company and in the wider community.

As we continue to explore the possibilities of generative AI, it’s crucial to remember that how we create is just as important as what we create. Companies leading this technological revolution have a responsibility not only to innovate but also to ensure that their innovations are ethical and beneficial to society.