At Workday Rising EMEA in November, a distinguished panel started a debate about AI ethics. They talked about the dangers of AI, not the Terminator type, but the real deal that’s already here. Generative AI is the big buzzword of the year, and in just a single year, this bad boy ChatGPT has blown up way more than anyone anticipated.
The panel had a heavy focus on the need to be super careful about how we deploy this technology. The AI tool is wicked smart, solving all kinds of problems, but it’s got some kinks to work out. The biggest issue is the unfairness and bias in the data that the AI learns from. This means that in critical areas like hiring or customer service, AI can carry on and even make worse the inequalities that already exist. So it’s on businesses to fight these biases and make sure the AI doesn’t just do what’s smart, but what’s fair.
Transparency is another biggie they touched on. AI can be super tricky, making decisions that people can’t even begin to figure out. So, transparency is essential, making the decision-making process transparent and accountable is essential. Also, privacy and security are lit when it comes to keeping all that data safe. It’s not just about following the rules, it’s about keeping your customers’ confidence and trust.
But what about when things go wrong? Who’s accountable for those snafus? These are the kinds of challenges we’ve got to face head-on as we let AI take on more big boy roles in the workplace.
Of course, it ain’t just about business. The ripple effects of AI can spread much further and deeper, acting as a massive disruptor of the workforce. It’s pumping out efficiency but at what cost when it shoves folks out of their jobs?
The public chat also stood forth ideas about powerful AI models that can influence what we think and how we think, and that’s a game-changer, man. The consequences when these things flop are huge and need to be thought about carefully.
We’re in the beginning phases of really understanding this AI beast and have so many questions, we haven’t even begun to answer. The issue of model collapse that we’re seeing is just one problem we’re seeing with AI models, and we haven’t even touched upon what happens when it all goes Pete Tong.
Apart from that, what about all that precious data these models are leaning on? Not everyone’s happy to share their deets, so how do we make sure it’s all peachy keen when we do? The question of who benefits from it all was a big issue, as it seems like big honcho companies are the ones raking it in, not the folks who gave up the data in the first place.
It’s totally normal too for these models to pick up biases from their data, but that mess can’t be happening. We gotta figure out how to dial ’em back on those prejudices and stereootypes because fair and just AI is the only kind we’re interested in.
And you bet that stringency around regulation has gotta get faster, too. The public has woken up and smelt the coffee about the big bad world of data privacy. The EU is cracking things down hard with their AI Act, setting the pace for the rest of the world.
The US is off doing its own thing doesn’t have a general standard like the EU. But, the National Institute of Standards and Technology just released the AI Risk Management Framework advising everyone to keep a left eye on their AI practices.
The FTC and FDA are also stepping up their game to make sure AI is playing by the rules, because nobody’s happy when it doesn’t. We ain’t just talking about abiding by the law, but about creating AI that people are comfortable with.