The bad thing about bureaucracy is that it wants to regulate everything under the pretext that it is saving the society, it plays role of an autocratic rather than assistance. That being said, regulation is good only when their is a massive downside to something if things go wrong like Nuclear Power Plant or Police Force. We have to understand that tech itself has never been bad, its the intentions of its users which make it bad.
AI and algorithms are vastly misunderstood pieces. To define in simple terms, algorithms is nothing but steps to do something and to define AI (I use the term ML or Machine Learning because that’s what it is), it is made of a mathematical model which stores probability of having two things together. Most common use of this ML is to predict what’s next - book recommendation, post recommendation, advertisement etc. So, there is nothing inherently wrong with these models.
Since ML is just probability, that’s where that bias comes in. It is the bias. The change in probability happens whenever someone generate a positive or negative feedback loop for the prediction made by the model. For example, if Upvote/Like then show more of same things and if Downvote/Dislike/Report Abuse(often missing) show less of those. Basically, confirmation bias is a good strategy for the algorithm. Companies use them to grow and gain traction and users. Simple.
Probability is good predictive indicator only if the experiment is done lot of times. In the case of ML, it is Data that is fed to mathematical models to generate the probability network. In tech space, there is a saying “Garbage in, garbage out”. So if you put in meaningless stuff, models will still make something out of it. The same thing happens if there is very less data to train on. Now, this is where it gets interesting.
To create a good ML service, you need lots of data and that data cannot manually be cleaned by a company. Google created Captcha, in which it asks users to select boxes having traffic lights, zebra crossing etc. and used that data to train its self-driving cars. They got it done massive number of times from the users, that’s how much effort it requires.
Now, coming to the point of regulation by having a manual intervention. To break out the truth, there already is manual intervention. The problem is that it cannot be done at scale and hence, the regulation is destined to fail. The regulations are not on the companies on how they should do something, that’s for companies to decide. If regulations has to interject between the process of the companies, it will slow them down or cause disinterest. We usually call it “License Raj”.
How to regulate then? In case of self-driving cars, regulations are that “anything” on road has to follow traffic rules and it should not cause accidents. Very clear regulations and its applicable for all. Companies can choose their ways to follow this regulation. That is how it should be done.
IMO for FB, the regulations of media should apply as it allows news portals to exist on its platform and if it allows political parties to exist then it should also be under the scanner of Election Commission and so on and so forth. Why? These entities have regulations of what kind of activities they can do in physical space and those should be applicable in digital space as well. This doesn’t mean every regulation should happen as such because:
- The entities has far reaching consequences. For example, if a company on Twitter announces that their CEO is dumping shares in market without telling SEBI then SEBI can take action against them. There is a protocol for them to function. While for an individual there is none, almost everything will be under Freedom of Speech and that is how it should be.
- Internet is a subscriber media while TV and newspapers and broadcast media. Govt. banning adult websites is a bad idea because people visit them willingly but banning same content in newspaper or TV is a good idea because it is broadcasted. Perhaps, even newspaper can have some slack.
Adding few more things just for context but can be ignored. There is inherent bias in the data that is fed into these models.
- There is racial bias because most of the data generated is by the users who are privileged to have the internet.
- The face recognition fails because of lack of racial diversity in the organization.
- The self-driving cars still have very tiny possibility of not able to predict what’s next and can cause accident. Way lower than humans though.