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That is to say, we&#;re in the dot-com boom, circa . Many companies will go bust. It may take years before we see this era&#;s Facebook (now Meta), Twitter (now X), or TikTok emerge. &#;People are reluctant to imagine what could be the future in 10 years, because no one wants to look foolish,&#; says Alison Smith, head of generative AI at Booz Allen Hamilton, a technology consulting firm. &#;But I think it&#;s going to be something wildly beyond our expectations.&#;

&#;Here&#;s the catch: it is impossible to know all the ways a technology will be misused until it is used.&#;

The internet changed everything&#;how we work and play, how we spend time with friends and family, how we learn, how we consume, how we fall in love, and so much more. But it also brought us cyber-­bullying, revenge porn, and troll factories. It facilitated genocide, fueled mental-health crises, and made surveillance capitalism&#;with its addictive algorithms and predatory advertising&#;the dominant market force of our time. These downsides became clear only when people started using it in vast numbers and killer apps like social media arrived.

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Generative AI is likely to be the same. With the infrastructure in place&#;the base generative models from OpenAI, Google, Meta, and a handful of others&#;people other than the ones who built it will start using and misusing it in ways its makers never dreamed of. &#;We&#;re not going to fully understand the potential and the risks without having individual users really play around with it,&#; says Smith.

Generative AI was trained on the internet and so has inherited many of its unsolved issues, including those related to bias, misinformation, copyright infringement, human rights abuses, and all-round economic upheaval. But we&#;re not going in blind. 

Here are six unresolved questions to bear in mind as we watch the generative-AI revolution unfold. This time around, we have a chance to do better. 

1

Will we ever mitigate the bias problem?

Bias has become a byword for AI-related harms, for good reason. Real-world data, especially text and images scraped from the internet, is riddled with it, from gender stereotypes to racial discrimination. Models trained on that data encode those biases and then reinforce them wherever they are used.

Chatbots and image generators tend to portray engineers as white and male and nurses as white and female. Black people risk being misidentified by police departments&#; facial recognition programs, leading to wrongful arrest. Hiring algorithms favor men over women, entrenching a bias they were sometimes brought in to address. 

Without new data sets or a new way to train models (both of which could take years of work), the root cause of the bias problem is here to stay. But that hasn&#;t stopped it from being a hot topic of research. OpenAI has worked to make its large language models less biased using techniques such as reinforcement learning from human feedback (RLHF). This steers the output of a model toward the kind of text that human testers say they prefer.

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