Technical AI Ethics: Understanding the Challenges of Today's AI Landscape

Technical AI Ethics: Understanding the Challenges of Today's AI Landscape

The rise of AI technology has brought with it a range of ethical challenges that must be addressed to ensure that AI is developed and used in a safe, fair, and transparent way. The AI landscape is complex and multifaceted, from bias and toxicity in large models to the ethical challenges posed by generative models.

One of the critical challenges facing the industry is the issue of bias and toxicity in large models. While new evidence suggests that these issues can be mitigated to some extent through instruction tuning, there is still much work to be done to ensure that large models are not toxic or biased in ways that could harm individuals or society.

Another area of concern is the ethical challenges posed by generative models. These models are capable of producing impressive results, but they also have the potential to be used for nefarious purposes. For example, text-to-image generators are routinely biased along gender dimensions, and chatbots like ChatGPT can be tricked into serving nefarious aims.

The number of incidents related to the misuse of AI is rapidly increasing, highlighting the need for greater awareness and regulation of AI technologies. The growth in AI incidents is evidence of greater use of AI technologies and an understanding of the potential for misuse.

However, fairer models may not always be less biased. While language models have a clear correlation between performance and fairness, fairness and bias can sometimes be at odds. Language models that perform better on specific fairness benchmarks tend to have worse gender bias, highlighting the need for a more nuanced understanding of fairness in AI development.

Despite these challenges, interest in AI ethics continues to skyrocket. The number of submissions to the leading AI ethics conference FAccT has more than doubled since 2021 and increased by a factor of 10 since 2018, with more submissions than ever before coming from industry actors.

Finally, automated fact-checking with natural language processing is not always as straightforward as it may seem. While several benchmarks have been developed for automated fact-checking, researchers have found that many rely on evidence "leaked" from fact-checking reports that did not exist at the time of the claim surfacing.

Overall, the landscape of technical AI ethics is complex and ever-evolving. It will require ongoing effort and collaboration to ensure that AI is developed and used to benefit society as a whole.