Things are moving fast with AI. With concerns over bias, misinformation, and environmental damage, is more regulation needed to make AI sage for brands? APS Group’s Melandra Smith investigates.
In today’s business landscape, artificial intelligence (AI) has become a ubiquitous topic, and opinions about it abound.
Many business owners are eager to embrace AI, recognizing its potential to revolutionize their operations and gain tangible, measurable improvements – not to mention the fear of being left behind in the race to harness the numerous capabilities of this emerging technology.
Within companies, studies suggest employees are somewhat divided about AI. Some are optimistic, viewing AI as a tool that can enhance their work. Others have concerns about job security and the potential shift of human roles.
Regardless of where you stand in that debate, there’s one undeniable truth: AI has firmly engrained itself in our current world; underpinning a plethora of new and powerful technologies with the potential to reshape our lives. But with great power comes great responsibility. It’s now imperative for organizations adopting AI to be alert to the possible negatives, scanning for biases, and helping put legislative safeguarding into effect to protect consumers and users.
The dangers of AI
Much has been said about the potential dangers of AI, with some key figures behind the technology delivering stark warnings around its possible future uses, such as Geoffrey Hinton, ‘the Godfather of AI’ and Elon Musk, who wrote an open letter declaring that it poses “profound risks to society and humanity”.
When we consider that everything within the scope of human imagination could both be automated and made efficiently deadly by harnessing the power of AI, these concerns feel real and immediate. Regulatory attention is essential around areas including bias, misinformation, and environmental costs.
1. Environmental costs
Digital technologies have long been hailed as saviors of the environment, but as their use goes mainstream, this is no longer strictly true. Some large language models produce emissions in line with the aviation industry. Data centers need hundreds of thousands of gallons of water a day for cooling (leading to initiatives to place them next to swimming pools where they can be used to keep the water warm – or, in Finland, to heat hundreds of homes). ICT industry emissions worldwide are expected to account for 14% of global emissions by 2040, with communications networks and data centers being the heaviest users.
2. Misinformation
AI language models make disinformation campaigns much easier, reducing the cost and effort required to create and deliver content. AI also has a history of creating inaccurate content.
StackOverflow, a website used by developers, recently issued a ban on posting generative AI content to the site due to the inaccuracy of the coding the AI generator produced.
3. Bias in AI
It is questionable whether such a thing as ‘neutral’ data exists. AI-powered machines, which learn from the data fed to them by their human creators, inevitably replicate and even emphasize biases within that data.
In some cases, AI even automates the very bias types it was created to avoid. The negative outcomes these biases could have on human lives and livelihoods mean there is still a lot to do before AI can be trusted to make suitably nuanced judgments about individuals.
Avoiding bias in AI
Addressing bias is both a critical and ethical challenge. Bias can be introduced into AI systems unintentionally by biased training data, algorithms, and decision-making processes. Fortunately, there are many strategies and tools for identifying, protecting against, and eliminating bias in AI models, such as disparate impact analyses and algorithmic fairness. Human review and oversight are also still vital parts of any AI decision-making process.
Suggested newsletters for you
Daily Briefing
Daily
Catch up on the most important stories of the day, curated by our editorial team.
Ads of the Week
Wednesday
See the best ads of the last week – all in one place.
Media Agency Briefing
Thursday
Our media editor explores the biggest media buys and the trends rocking the sector.
How is AI regulated?
AI regulation is a complex and rapidly evolving landscape. It varies significantly from one country to another and encompasses aspects from data privacy and ethics to safety and liability. Many countries have data privacy and protection laws that apply to AI systems. AI systems are also subject to safety and security regulations in some industries, such as autonomous vehicles and healthcare, and AI systems used in HR are now regulated to prevent discrimination against protected groups.
While the outlook is positive, determining liability for AI-related incidents remains complex and more safeguards are needed to protect the public. Human and consumer rights groups such as the UK’s Big Brother Watch are continually identifying ways that AI negatively affects or discriminates against people, and the establishment of government and industry bodies and standards to set best practices and ensure the responsible development and use of AI technologies cannot be far away.
Maintaining ethical AI practices requires a holistic approach that encompasses both technical and organizational aspects. It should be seen as an ongoing commitment, one that’s integral to culture and operations. By prioritizing ethics in AI, businesses can build trust, foster innovation and make a positive contribution to society.
