- Training data for AI models can contain harmful biases and misinformation which then get reflected in the model’s outputs.
- Blockchain offers transparency into an AI model’s training data so developers can audit for issues.
- Blockchain also enables rolling back problematic AI learning to earlier, more vetted versions.
Blockchain: The Key to Keeping AI Accountable and Unbiased?
How Blockchain Could Prevent Bias and Misinformation in AI Models
Artificial intelligence models like ChatGPT have sparked excitement about the potential of AI, but also raised concerns about bias and misinformation. This is because the data used to train AI models can unintentionally contain harmful assumptions or falsehoods. Now, blockchain technology is emerging as a promising way to mitigate these risks and ensure more ethical AI development.
By storing AI training data on a blockchain, developers can create an immutable record of what information was used to train an AI model. This transparency allows them to audit data sets and pinpoint issues, whereas previously the origins of biases were obscured. For example, if an AI chatbot begins displaying racist assumptions, developers can review the blockchain-verified training data to identify and correct problems.
Medha Parlika, CTO and co-founder of the blockchain company Casper Labs, explained how their new blockchain-based AI product allows rolling back problematic learning. “If it’s learning and you find that the AI is starting to hallucinate, you can actually roll back the AI. And so you can undo some of the learning and go back to a previous version of the AI,” she said. This ability to revert AI models to earlier, vetted versions of themselves provides crucial oversight.
Sheila Warren, CEO of the Crypto Council for Innovation, predicts such blockchain verification will become standard practice. “I actually do think that the verification of an AI and sort of the checks and balances…within an AI system, are going to be blockchain driven and blockchain backed,” she stated.
By enhancing transparency and accountability in AI development, blockchain technology could help the field progress responsibly. Developers can leverage its capabilities for data tracking and auditing to demonstrate how they train ethical, unbiased models. For an industry rife with “black box” opacity, this emerging solution shines some necessary light. Adoption of blockchain may prove key in making sure today’s AI breakthroughs also uphold moral standards.
The Rise of Blockchain as an AI Accountability Tool
As artificial intelligence continues advancing at a blistering pace, the need for oversight grows increasingly urgent. Systems like ChatGPT demonstrate AI’s potential, yes, but also raise pressing ethical questions. If AI models absorb and propagate the same biases and misinformation latent in their training data, even their creators struggle to trace or constrain the damage. Fortunately, remedy may lie in blockchain’s emergence as an AI accountability tool.
By recording training data transparently on tamper-proof ledgers, blockchain enables unprecedented monitoring of AI development. Audit trails grant developers granular insight into model provenance while retaining key contextual details past approaches obscured. Should issues emerge, they can swiftly investigate, identify deficiencies in underlying data sets, and resolve them through targeted retraining or rollbacks. The ability to directly confirm, assess and correct AI behavior promises to accelerate innovation’s pace without sacrificing rigor or responsibility.
The recent viral audio recording allegedly of a Baltimore high school principal making racist remarks highlights the alarming pace at which AI-generated fake audio, known as deepfakes, are advancing. While the recording’s authenticity remains unverified, experts say it likely was created using readily available AI tools that clone a person’s voice from just a minute or two of sample audio.
Believe Your Ears No More: AI Audio Deepfakes Go Mainstream
“It’s trivial. All you need is about a minute to two minutes of a person’s voice,” said Hany Farid, a digital forensics expert at UC Berkeley who has developed deepfake detection tools. Using text-to-speech or speech-to-speech services costing as little as $5 per month, anyone can now generate convincing fake audio in seconds.
Unlike previous high-profile deepfake targets, who tended to be celebrities and politicians, this incident represents an ominous turning point – the democratization of a technology that can falsely incriminate regular citizens. “You no longer need hours and hours of someone’s voice or image to create a deepfake,” Farid said. “We knew this was coming. It wasn’t a question of if—it was when. Now the technology is here.”
The situation also spotlights the asymmetry between the ease of creating fakes versus verifying them. “Detection is harder because it’s subtle; it’s complicated; the bar is always moving higher,” Farid explained. “I can count on one hand the number of labs in the world that can do this in a reliable way. That’s disconcerting.”
Proper analysis requires a multipronged approach – consulting multiple experts, learning about the recording’s origins, and looking for signs of splicing or manipulation in the audio spectrogram. But the stakes are high, Farid cautions, so publicly available tools aren’t yet reliable enough for deepfake detection.
As everyday citizens become more vulnerable to false accusations via deepfake, Farid argues that tech companies profiting from these AI services should face liability. “Deepfakes are not an unforeseen consequence of generative AI; this was clearly predictable,” he said. “But up until this point, many companies have just decided their profits were more important than preventing harm.”
The expert analysis of this apparent AI-generated audio deepfake of a school principal invites broader reflection on the social implications of technologies that allow falsifying records of speech. Far from a narrow technical matter of detection, this incident encapsulates a perfect storm gathering around the proliferation of generative AI.
On one front, it highlights the unchecked commercialization of these capabilities before adequate safeguards are in place. The astonishing ease of so-called voice cloning services permits casual abuse by tech-empowered provocateurs. And the principal is unlikely to be the last victim of weaponized fakery as citizens are stripped of basic expectations of evidentiary reliability in their own words.
Secondly, this case foreshadows the systemic erosion of truth in public discourse should counterfeit media overwhelm the capacity for verification. If even local controversies require days of expert authentication, the informational foundations of civil society risk collapse through a thousand cuts of doubt. The absence of accountability for those peddling generative models ultimately undermines trust in all media.
Finally, the episode suggests the need to evolve legal standards and corporate responsibility commensurate with technologies introducing radical uncertainties around truth itself. To stem complicity in a rising tide of falsification enabled by AI, providers should face liability when they prioritize profits over public welfare, flouting ethical red lines. The principal may be the first victim, but if left unchecked, society itself may not survive this undermining of reality’s role as final arbiter of facts. The future of communications truth rests on how seriously we take this early warning.