Current regulatory focus on privacy misses the core issue of algorithmic harm. A more effective future approach is to establish a "right to algorithmic transparency," compelling companies like Amazon to publicly disclose how their recommendation and pricing algorithms operate.
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The need for explicit user transparency is most critical for nondeterministic systems like LLMs, where even creators don't always know why an output was generated. Unlike a simple rules engine with predictable outcomes, AI's "black box" nature requires giving users more context to build trust.
Instead of trying to anticipate every potential harm, AI regulation should mandate open, internationally consistent audit trails, similar to financial transaction logs. This shifts the focus from pre-approval to post-hoc accountability, allowing regulators and the public to address harms as they emerge.
Digital platforms can algorithmically change rules, prices, and recommendations on a per-user, per-session basis, a practice called "twiddling." This leverages surveillance data to maximize extraction, such as raising prices on payday or offering lower wages to workers with high credit card debt, which was previously too labor-intensive for businesses to implement.
The problem with social media isn't free speech itself, but algorithms that elevate misinformation for engagement. A targeted solution is to remove Section 230 liability protection *only* for content that platforms algorithmically boost, holding them accountable for their editorial choices without engaging in broad censorship.
In an era of opaque AI models, traditional contractual lock-ins are failing. The new retention moat is trust, which requires radical transparency about data sources, AI methodologies, and performance limitations. Customers will not pay long-term for "black box" risks they cannot understand or mitigate.
When addressing AI's 'black box' problem, lawmaker Alex Boris suggests regulators should bypass the philosophical debate over a model's 'intent.' The focus should be on its observable impact. By setting up tests in controlled environments—like telling an AI it will be shut down—you can discover and mitigate dangerous emergent behaviors before release.
As AI personalization grows, user consent will evolve beyond cookies. A key future control will be the "do not train" option, letting users opt out of their data being used to train AI models, presenting a new technical and ethical challenge for brands.
The real danger of algorithms isn't their ability to personalize offers based on taste. The harm occurs when they identify and exploit consumers' lack of information or cognitive biases, leading to manipulative sales of subpar products. This is a modern, scalable form of deception.
An FDA-style regulatory model would force AI companies to make a quantitative safety case for their models before deployment. This shifts the burden of proof from regulators to creators, creating powerful financial incentives for labs to invest heavily in safety research, much like pharmaceutical companies invest in clinical trials.
Technological advancement, particularly in AI, moves faster than legal and social frameworks can adapt. This creates 'lawless spaces,' akin to the Wild West, where powerful new capabilities exist without clear rules or recourse for those negatively affected. This leaves individuals vulnerable to algorithmic decisions about jobs, loans, and more.
