The road to arriving at the perfect balance between necessary regulation and innovation in AI will likely be long and complex.
Artificial Intelligence (AI) is on the path to becoming ubiquitous in every aspect of our lives. It has already made significant in-roads in several sectors, including financial services, insurance, education, retail, manufacturing, etc. The most recent wave of excitement surrounding the technology, spawned by innovations in large language models – such as OpenAI’s ChatGPT, Google’s Bard AI, and Microsoft copilot – has given an even stronger impetus to AI adoption, with organizations across verticals actively exploring AI applications for their business functions.
This exponential growth of AI has led to an increase in discourse around technology, with a key area of interest being the responsible and ethical use of AI. Data privacy and security, transparency, accountability, and the potential for bias are some of the major issues that enterprises, governments, and individual citizens and consumers are concerned with as we enter this bold new era of AI-enabled digital transformation. At the heart of this debate lies two pertinent questions – Does AI need to be regulated? And if so, then how do we strike a balance between regulation and promoting free innovation?
The Need for Ethical AI
There has already been significant concern around data privacy in the digital age, and AI adoption only exacerbates this. AI systems are dependent on sourcing and processing vast amounts of data for their effective functioning. There is a need to ensure that this data is collected, stored, and used responsibly, safeguarding individual privacy and the security of personal information.
The potential for AI to amplify societal biases that may be present in its training data is another key area of concern. For example, it has been found that facial recognition systems have a higher error rate when it comes to identifying people with darker skin tones. Such biases can help perpetuate existing disparities in terms of race, ethnicity, gender, and other factors, and lead to discriminatory outcomes against individuals or groups. For AI to be a force for good in the world, it is crucial that systems are audited and tested to ensure fairness and bias-free decision-making.
Apprehensions about biases in AI also highlight the need for transparency in these systems. Machine Learning models and other aspects of AI technology can seem very complex and opaque to many consumers and the public. Ensuring greater transparency in decision-making by AI – for instance, by requiring a system to provide an explanation for the outcomes it provides – will go a long way toward building consumer confidence in the safety, reliability, and fairness of the technology.
The first step to addressing these concerns, of course, is establishing accountability and responsibility. Who is responsible when an AI system causes harm? Who is expected to provide explanations and/or recourse to affected parties in case of such an eventuality? The ethical issues surrounding the technology certainly present a strong case for regulation in some form to enforce this accountability. At the same time, it’s important to ensure that regulation does not become a barrier to innovation in the AI space.
AI Regulation – A Hard or Soft Law Approach?
While there is growing consensus over the need for some form of governance of AI technology, the debate still rages over the optimal approach to this. Broadly speaking, there are two possibilities – a ‘hard law’ approach involving clear-cut regulations that are legally binding on industry players; or a ‘soft law’ approach involving guidelines and principles to shape the development of socially-responsible and ethical AI systems.
The ‘hard law’ approach offers a clear roadmap to researchers, developers and organizations who are designing and implementing AI systems – ensuring that these systems function in a manner consistent with societal norms and values, including transparency and fairness. It makes organizations legally accountable for any harm caused by their systems, providing them with a strong incentive to adhere to ethical practices while working with AI. It also creates a level playing field for all players in the AI space.
However, this approach does have its limitations. Traditionally, legislation has always struggled to keep up with technological advancement, and this is likely to be the case with AI as well, given the rapid pace at which the technology is evolving. Increased regulation imposes significant compliance costs on organizations, particularly smaller businesses, and can thus significantly slow down or even stifle innovation in the space. Moreover, considering the global scale at which the AI industry operates, effective enforcement of regulations across multiple jurisdictions, each with its own legal frameworks, can prove to be a complex, if not insurmountable, task.
A ‘soft law’ approach, on the other hand, allows for a more flexible and adaptive regulatory framework that provides some guardrails for ethical AI development, while leaving ample scope for experimentation and innovation. A drawback of this approach, however, would be the risk of under-regulation, which could pave the way for misuse of the technology, as well as inadequate safeguards against any potential ill-effects.
Moving Towards an Innovative and Responsible AI Space
The road to arriving at the perfect balance between necessary regulation and innovation in AI will likely be long and complex. Complementing ‘soft law’ guidelines or codes of conduct to ultimately reach the hard law regulations could be the optimal approach. The participation of key stakeholders, including policymakers, regulators, industry leaders, think tanks, and researchers from across the globe, will be essential in order to arrive at a nuanced solution that places core principles of accountability, responsibility, transparency, and fairness at the forefront of any AI development.