The Ethics of Generative AI: Ensuring Responsible Use and Implementation
Generative AI is not just a buzzword, it’s a game changer. With its colossal potential, it is transforming how we interact, work, and understand our world.
Generative AI can accomplish remarkable feats, ranging from drug discovery to software development, knowledge search to creative arts. At a societal level, generative AI can bring in sustainable and equitable solutions to global challenges that have proven difficult to tackle at scale until recently. It is no surprise that generative AI will increase global productivity by trillions of dollars. According to an industry report, generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually. However, generative AI is new and generally unregulated, which means that there are several ways it can be misused. Generative AI ethics is a rising concern as this AI technology gains rapid adoption.
Ethical Concerns and Challenges with Generative AI
Generative AI solutions and offerings are transforming the operational, functional, and strategic landscapes of several industries. But there are many ethical concerns surrounding generative AI today, which include copyright or stolen data issues, hallucinations and inaccuracies, biases in training data, cybersecurity jailbreaks, and environmental concerns.
3.5 quintillion bytes of data are created every day. Since AI models rely heavily on user data, there is often apprehension around their use. There can be issues with data privacy and security, particularly when it comes to AI being deployed in industries like finance and health care. Personal and corporate data can also be unintentionally introduced into generative AI training algorithms, which exposes users and corporations to potential theft, data loss, and violations of privacy.
“Hallucinations” is another unique issue when a large language model gives a confident response to a user’s prompt that is both entirely wrong or irrelevant and seems to have no basis in the data on which it had been trained. Besides, the advanced training that the generative AI powered tools have received to produce human-like content gives them the ability to convincingly manipulate humans through phishing attacks, adding a non-human and unpredictable element to an already volatile cybersecurity landscape.
There are also ethical considerations, such as AI systems possibly being trained on biased data, which can lead to discriminatory outcomes. It can be a significant ethical concern too when AI is used in decision-making processes such as hiring, lending and criminal justice. Besides, generative AI models use massive amounts of energy, both as they are being trained and as they later handle user queries. As these models continue to grow and be more sophisticated, their environmental impact will surely increase if intense regulations are not put in place.
Key Resolutions
While explainability, fairness and bias were the primary axes of responsible AI so far, now elements like accountability, ethical, and fake detection will need a prominent focus for generative AI. Negative use of generative AI can lead to criminal and fraudulent activities which can potentially cause social unrest.
Key ecosystem players need to play a critical role in AI governance to make it responsible. Regulatory bodies need to play their role, technology creators need to bring tech interventions to ensure technology is safe, secure, and suitable for work, including addressing aspects like copyrights.
Generative AI can be used in thoughtful, effective ways in the organizations if the leadership has been encouraging the setup of safety nets to protect employees and customers from the technology’s hazards. Creating an ethical framework and guidelines for how to use generative AI can help organizations prevent harmful biases and falsehoods from being proliferated, protect customers and their personal data, proprietary corporate data, environment, and the creators, their ownership, and rights over their work. Clear guidelines for data diversity requirements, fairness measures, and the identification of advantaged and disadvantageous data sets can ensure data and delivery processes consistently run smoothly. This end-to-end traceability and accountability are foundational for auditing, tracing, identifying, and fixing problems in real-time. For operational purposes, practices should be put in place to flag potential bias issues and assign steps for manual intervention.
Furthermore, while enterprises need to train data engineers, data scientists, ML modelers, and operations personnel, it is important to educate the employees on the appropriate use of generative AI. The people implementing this technology—companies like ours at Tech Mahindra or any other service provider—must recognize their own roles in this. They know how this technology works and must safeguard it. For example, if certain data is not to be used for a particular purpose, they must put in technical safeguards to prevent this. If output can be harmful or offensive, they must filter that out. If malicious content comes out, they must block that instantly.
Given the prioritization of ethics in the generative AI world, enterprises are now creating an ethics officer role who can be responsible for ensuring compliance at all levels across people, process, and technology. This dedicated position helps companies spend more time and energy figuring out what their company's problems are and what solutions will work best.
At Tech Mahindra, our ethical practices include a ‘responsible-first' approach while designing and developing generative AI use cases. We adhere to our structured and comprehensive responsible AI maturity assessment, and we follow a human-in-loop approach to critical AI-led inferences and actions, which we believe, is also essential. With TechM amplifAI, our suite of artificial intelligence (AI) offerings and solutions, we help enterprises amplify human potential and solve complex problems to future-proof business operations responsibly. We have a well-developed framework that incorporates industry principles and AI and generative AI ethics through three areas of service delivery: people, process, and technology.
The AI Path Ahead
The era of generative AI is just the beginning. Since ChatGPT was rolled out in November 2022, we have already seen swathes of updates and fine-tuning; just four months later, Chat-GPT4 arrived with significantly improved capabilities. What this means is that – just as a full realization of a technology’s benefits takes time – so too does getting the ethical framework correct. Managing the inherent risks of emerging technologies, hiring the right talent, upskilling the people, and redefining process and technology policies cannot be rushed.
It’s important to remember that while with its tremendous potential, generative AI has the capability of creating issues, it also has the power to resolve them. It’s like an antidote. While generative AI can lead to cyberattacks, at the same time, it can also defend against them. Technology can trigger disruption, but it can also provide protection.
