What are the ethical considerations when using generative AI?
Deepfakes
In April 2024, the NSE found itself in the middle of a troubling situation. On a Monday, it issued a warning about several fake audio and video clips circulating online. The clips were incredibly convincing, showing what appeared to be Chauhan, the NSE’s managing director and CEO, giving stock tips. The NSE logo was there, and his voice and facial expressions were eerily accurate.
But the truth is this: These videos were created using cutting-edge technology, designed to fool even the most cautious investors. The NSE quickly issued a warning to the public, urging them not to trust these videos or the investment advice they appeared to offer. It stressed that no NSE employee, including Chauhan, is authorised to recommend stocks or engage in stock trading.
So, the next time you see a video that seems too good to be true, especially when it comes to your investments, think twice. It could just be a clever scam.
This kind of misinformation can easily spread, harming companies’ reputations and causing real damage. For companies, the risk is high: a single misleading video can cause stock prices to plummet overnight.
To combat this phenomenon, companies must invest in tools that can detect fake content. Big names like Facebook are already working on this, and it’s a smart move for any company looking to protect its image and customer trust.
As exciting as AI technology is, it also poses significant ethical challenges that businesses need to be aware of. Here’s a look at some of the key issues and why they matter.
Prejudice and discrimination
Generative AI learns from data, and if that data is biased, so is the AI. This means that AI could unintentionally reinforce harmful stereotypes or make unfair decisions. For example, biased facial recognition software could misidentify people, leading to serious legal and public relations issues.
The solution? Companies must ensure their AI is trained on diverse, unbiased data and regularly check for unintentional bias. Partnering with ethical AI organizations 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, such as a new song that sounds nearly 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 costly legal battles and damage the company’s reputation.
To avoid this, companies must ensure that the data used to train their AI is properly licensed. They must also keep track of the provenance of the content they generate, which can help prove that no rules have been broken.
Data Privacy and Security
Generative AI often uses large 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 example, if an AI trained on medical records generates a profile that resembles that of a real patient, it could violate privacy laws.
Businesses should anonymize data as much as possible and strengthen their security measures to protect user information. Adhering to principles such as GDPR data minimization (using only data that is absolutely necessary) can help protect personal data.
Responsibility
With so many steps involved in creating and using generative AI, it can be difficult to know who is responsible if something goes wrong. Without clear accountability, problems can lead to legal trouble and damage a company’s credibility. Think of the controversies surrounding AI chatbots that have made inappropriate or damaging comments. Without a clear plan for accountability, these issues can quickly spiral out of control.
Companies should establish clear policies for the use of generative AI, similar to the guidelines used by social media platforms to manage content. They should also put in place systems for users to report any issues with AI-generated content.
The commercial angle
Ignoring these ethical issues is not only a moral mistake, it is also a business risk. Companies that ignore the ethical implications of generative AI risk reputational damage, diminished customer trust, and financial instability.
Moving forward
The first step is awareness. Companies must understand the ethical challenges posed by generative AI and take proactive steps to address them. This means creating policies that promote responsible use, being transparent, and fostering a culture of ethical AI within the company and in the broader community.
As we continue to explore the possibilities of generative AI, it’s essential 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.
