Responsible Technology Use in the AI Age
upstart writes:
AI presents distinct social and ethical challenges, but its sudden rise presents a singular opportunity for responsible adoption:
Technology use often goes wrong, Parsons notes, "because we're too focused on either our own ideas of what good looks like or on one particular audience as opposed to a broader audience." That may look like an app developer building only for an imagined customer who shares his geography, education, and affluence, or a product team that doesn't consider what damage a malicious actor could wreak in their ecosystem. "We think people are going to use my product the way I intend them to use my product, to solve the problem I intend for them to solve in the way I intend for them to solve it," says Parsons. "But that's not what happens when things get out in the real world."
AI, of course, poses some distinct social and ethical challenges. Some of the technology's unique challenges are inherent in the way that AI works: its statistical rather than deterministic nature, its identification and perpetuation of patterns from past data (thus reinforcing existing biases), and its lack of awareness about what it doesn't know (resulting in hallucinations). And some of its challenges stem from what AI's creators and users themselves don't know: the unexamined bodies of data underlying AI models, the limited explainability of AI outputs, and the technology's ability to deceive users into treating it as a reasoning human intelligence.
Parsons believes, however, that AI has not changed responsible tech so much as it has brought some of its problems into a new focus. Concepts of intellectual property, for example, date back hundreds of years, but the rise of large language models (LLMs) has posed new questions about what constitutes fair use when a machine can be trained to emulate a writer's voice or an artist's style. "It's not responsible tech if you're violating somebody's intellectual property, but thinking about that was a whole lot more straightforward before we had LLMs," she says.
The principles developed over many decades of responsible technology work still remain relevant during this transition. Transparency, privacy and security, thoughtful regulation, attention to societal and environmental impacts, and enabling wider participation via diversity and accessibility initiatives remain the keys to making technology work toward human good.
Read more of this story at SoylentNews.