The chair of the Federal Trade Commission, Lisa Khan, recently wrote an opinion piece in the New York Times about possible regulations on artificial intelligence the FTC would likely put in place soon.
Some of the biggest themes include how AI can be misused to create or maintain monopolies as well as exploit consumers.
Below, we will cover some of the biggest areas that the American government is most likely to target when it comes to taking on AI.
This may be a large reason for regulations on artificial intelligence and the biggest worry of the FTC, as AI can certainly be adept at helping dominant companies retain the throne by squashing smaller competition.
For example, industries could collusively utilize AI tools to predict the best prices for goods, resulting in inflated prices that directly impact consumers.
Ensuring fair competition is often seen as a way to help smaller businesses in this country have a better shot at big success, but one of the biggest motivations behind mitigating and preventing anticompetitive practices is that many of the negatives oftentimes fall on the consumer. One example of that is the aforementioned price prediction tools.
Soon enough, generative artificial intelligence will possess the capability to create video and audio of such sophistication that they can deceive even the most eagle-eyed individuals.
Imagine receiving a video of a loved one tied up in a dark basement, asking you to please pay a ransom of X amount of dollars—or else.
You will have to seriously consider whether this is real or not. If that loved one has photos and videos that are accessible online, or just on some digital advice, then someone who has access to such content can create a false ransom video or photo.
That is just one example of how AI could be employed by fraudsters, and the FTC will likely set regulations on artificial intelligence and the kinds of content that people can create.
Experts have long cited bias in AI systems as a problem primarily associated with the training phase. During this phase, the AI system was fed data sets that either underrepresented certain populations or intentionally provided negative impressions of them.
Another issue with AI and discrimination is AI’s struggle—or outright inability—to grasp context.
For instance, many discriminated groups in America have lower wages and lower overall financial net worth as a result of discrimination, but to an AI prediction platform that is simply reading the hard data, it can only notice the association between that group and the lower amount of money. It has no concept of discrimination, only hard data and the connections between them.
You can easily envision the issues that may arise from an AI platform designed to approve or disapprove home loans based on income and net worth.
As a result, developers need to program AI systems to adjust weights given to certain groups based on broader realities, such as the financial consequences of discrimination on affected groups.
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