Algorithms are everywhere. Here's why you should care

Algorithms are everywhere. Here’s why you should care

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An algorithm is a set of rules or steps followed, often by a computer, to produce a result. And algorithms aren’t just on our phones: they’re used in all sorts of processes, online and offline, from upgrading your home to teaching your robot vacuum to avoid your dog’s feces. Over the years, they have increasingly been entrusted with life-changing decisions, such as helping decide who to arrest, who should be released from prison before a court date, and who is approved for a home loan.
In recent weeks, algorithms have come under renewed scrutiny, including how tech companies should change the way they use them. This stems both from concerns raised during hearings with Facebook whistleblower Frances Haugen and from bipartisan legislation introduced in the House (a complementary bill had already been reintroduced in the Senate). The legislation would require big tech companies to allow users access to a version of their platforms where what they see is not shaped by algorithms. These developments highlight a growing awareness of the central role that algorithms play in our society.

“At this point, they’re responsible for making decisions about just about every aspect of our lives,” said Chris Gilliard, visiting fellow at Harvard Kennedy School’s Shorenstein Center on Media, Politics and Public Policy.

Yet, how algorithms work and the conclusions they reach can be mysterious, especially as the use of artificial intelligence techniques makes them increasingly complex. Their results are not always understood or accurate – and the consequences can be disastrous. And the impact of possible new legislation aimed at limiting the influence of algorithms on our lives remains uncertain.

Algorithms, explained

In its most basic form, an algorithm is a series of instructions. As Sasha Luccioni, a researcher on the ethical AI team at AI model builder Hugging Face, pointed out, it can be hard-coded, with fixed instructions for a computer to follow, like putting a list names in alphabetical order. Simple algorithms have been used for decades for computerized decision making.

Today, algorithms are constantly facilitating otherwise complicated processes, whether we know it or not. When you ask a clothing website to filter pajamas to see the most popular or cheapest options, you’re essentially using an algorithm to say, “Hey Old Navy, follow the steps to show me the cheapest pajamas “.

All sorts of things can be algorithms, and they’re not just limited to computers: a recipe, for example, is a kind of algorithm, as is the weekday morning routine you drowsily go through before you leave the house.

“We use our own personal algorithms every day,” said Jevan Hutson, a privacy and data security attorney at Seattle-based Hintze Law, who has studied AI and surveillance.

But while we can question our own decisions, those made by machines have become increasingly enigmatic. This is due to the rise of a form of AI known as deep learning, which is modeled on how neurons work in the brain and rose to prominence about a decade ago.
How AI has dominated our lives over the past decade
A deep learning algorithm could task a computer to watch thousands of cat videos, for example, to learn how to identify what a cat looks like. (It was a big deal when Google figured out how to do it reliably in 2012.) The result of this process of binging on data and improving it over time would be, essentially, a computer-generated procedure for determining how the computer will identify if there is a cat in every new image it sees. This is often known as the model (although it is also sometimes called the algorithm itself).

These models can be incredibly complex. Facebook, Instagram and Twitter use them to help personalize user feeds based on each person’s interests and past activity. Models can also be based on mounds of data collected over many years that no human could possibly sort through. Zillow, for example, has been using its machine learning-assisted “Zestimate” trademark to estimate home values ​​since 2006, taking into account tax and real estate records, homeowner-submitted details such as bathroom additions bath and photos of a lodge.

The risks of relying on algorithms

As the case of Zillow shows, however, shifting decision-making to algorithmic systems can also go horribly wrong, and it’s not always clear why.

Zillow recently decided to shut down its home flipping business, Zillow Offers, showing how difficult it is to use AI to appraise real estate. In February, the company said its “Zestimate” would represent an initial cash offer from the company to purchase the property through its house flipping business; in November, the company wrote down $304 million of inventory, which it blamed on recently buying homes for more than it thought it could sell.

Elsewhere online, Meta, the company formerly known as Facebook, has come under scrutiny for tweaking its algorithms in ways that helped incite more negative content on the biggest social network. of the world.

Zillow's home buying debacle shows just how difficult it is to use AI to appraise real estate
Algorithms have also had life-changing consequences, especially in the hands of the police. We know, for example, that at least several black men have been wrongfully arrested for using facial recognition systems.

There is often little more than a basic explanation from tech companies of how their algorithmic systems work and what they are used for. Beyond that, technology and technology law experts told CNN Business that even those who build these systems don’t always know why they come to their conclusions — which is why they’re often called “black boxes.”

“Computer scientists, data scientists, at this current point, they look like wizards to a lot of people because we don’t understand what they’re doing,” Gilliard said. “And we think they always do, and that’s not always the case.”

Burst filter bubbles

The United States has no federal rules on how companies may or may not use algorithms in general, or those that leverage AI in particular. (Some states and cities have adopted their own rules, which tend to deal more generally with facial recognition software or biometrics.)

But Congress is currently considering legislation dubbed the Filter Bubble Transparency Act, which, if passed, would require major internet companies such as Google, Meta, TikTok and others to “give users the ability to engage with a platform without being manipulated by user-specific data-driven algorithms”.
The Netflix building on Sunset Boulevard is pictured on October 20, 2021 in Los Angeles.
In a recent CNN opinion piece, Republican Senator John Thune described the legislation he co-sponsored as “a bill that would essentially create a switch for secret big tech algorithms – artificial intelligence (AI) designed to shape and manipulate user experiences – and give consumers the choice to turn it on or off.”

Facebook, for example, already has it, although users are effectively discouraged from flipping the so-called switch permanently. A fairly well-hidden “Most Recent” button will show you posts in reverse chronological order, but your Facebook News Feed will revert to its original heavily moderated state once you leave the website or close the app. . Meta stopped offering such an option on Instagram, which it also owns, in 2016.

Hutson noted that while the Filter Bubble Transparency Act clearly focuses on big social platforms, it will inevitably affect others such as Spotify and Netflix that rely heavily on algorithm-based curation. If this passes, he said, it will “fundamentally change” the business model of companies that are built entirely around algorithmic curation – a feature he suspects many users value in certain contexts.

“This is going to impact organizations far beyond those in the spotlight,” he said.

Artificial intelligence experts say the need for more transparency is crucial for companies that make and use algorithms. Luccioni believes algorithmic transparency laws are needed before specific uses and applications of AI can be regulated.

“I see things changing, certainly, but there’s a really frustrating disconnect between what AI is capable of and what it’s legislated for,” Luccioni said.

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