Bias in Artificial Intelligence

Artificial intelligence is quickly taking over many functions of our day-to-day lives. It can already schedule appointments, look things up on the internet, and compose texts and emails for us. But what happens when AI takes over more serious parts of our lives (though, texting is pretty important)?

That’s the challenge researchers and developers are facing. They want to develop more robust AI solutions, but they are running into a serious roadblock – bias.

Our artificial intelligence solutions are currently being trained by humans using data. The trouble is that the data (and the humans sometimes) doesn’t give the AI an unbiased place to start its work. As a friend of mine says, “It’s garbage in, garbage out.”

Examples of Bias in Artificial Intelligence

The first example is COMPAS, and it’s been well-documented. COMPAS was a tool that would score criminal offenders on a scale between 1 and 10, determining whether the offender was likely to be re-arrested while awaiting trial. This is a common decision made during bond call in criminal cases. Judges have had to weigh factors to determine a criminal’s likelihood of reoffending for decades. COMPAS was designed to help them.

The trouble was that COMPAS gave higher risk scores to black offenders than white offenders. There is a wonderful breakdown of how the algorithm worked from MIT, here. It’s important to note that the program did not explicitly factor in race, but it had the effect of unfairly targeting blacks anyway. Without going into the math, the basic problem was that blacks were more likely to be arrested (due to current and previous racial discrimination), which meant that the program predicted a higher chance of re-arrest. The higher predicted chance of re-arrest translated into higher risk scores. In fact, the data (predictably) was wrong, meaning that it consistently led to a higher percentage of black offenders being held in jail unnecessarily.

For a program created to combat the prejudice that exists in our court system, it failed.

Our second example is the Allegheny Family Screening Tool, which was created to help humans determine whether a child should be removed from their family because of abusive circumstances. The designers knew that the data was biased from the start. The data was likely to show that children from black or biracial homes were more likely to need intervention from the state.

The engineers couldn’t get around the faulty data. Data is the primary way that we train artificial intelligence, and the developers could not bypass this necessary training or fudge the numbers. Because they didn’t feel like they could combat the bias in the numbers, they opted to educate those with the most influence over the numbers going forward, explaining the faulty data and implicit bias to the users of the system – mostly judges (article, here).

This is a good example of how bias in the data can be challenging to overcome.

My last example is from current facial recognition software. The top three facial recognition artificial intelligence from IBM, Microsoft, and Megvii (Chinese) can all correctly identify a person’s gender 99% of the time, if that person is white. If the person was a dark-skinned woman, the chance of correctly guessing that person’s gender is only 35% (article, here).

There is no doubt that facial recognition software has a long way to go. That’s why it is so disturbing to see it being used heavily by law enforcement. Perhaps we will also see its use in contact-tracing for COVID. I believe this technology is likely to start trampling on our privacy rights over the next few years.

Why does it matter?

Bias in artificial intelligence matters because the exact reason we want to use AI is to avoid biases that naturally exist in all humans. Computers represent the only true way to treat everyone fairly. We see how our courts, schools, and banks are biased on the basis of race and gender. AI could provide us with a way past these prejudices. Then as people who have been traditionally held down are lifted, we may see some of these implicit biases melt away.

But we cannot train an AI to avoid bias with biased data. That is the challenge for developers today.

Garbage in, garbage out.

Why Do People Invest in Unprofitable Businesses?

I have long been interested in why people invest in unprofitable companies. As a small business owner, profit is key (along with cash flow). Profit is how your company grows and how you feed your family. But for large companies like SpaceX, profit is not important. Growth is key, as we will see.

I chose to write about this not because it relates to the practice of law, but rather because I am fascinated by future technology. Most companies developing future tech fall into the category of profitless behemoths with seemingly limitless valuations.


I’ll start with SpaceX.

They recently grabbed attention by being the first privately held company to launch people into space. This is one of the reported 15 scheduled commercial launches this year. Each one earns SpaceX approximately $80 million (Forbes article), which means it should earn about $1.2 billion in revenue from launches in 2020. SpaceX is currently valued at $36 billion, roughly 30 times more than its revenue. That is a steep valuation, even for a tech company.

So how does it work? Where does the value come from?

Well, it comes from the concept of a moonshot (pun intended). Space travel is slated to be a $1 trillion industry by 2040 (per If SpaceX is a controlling player in the industry, it could be worth far more than $36 billion.

Most venture capitalists look to get their money out of a business within 7-10 years. This has to do with how funds work and what investors expect. The SpaceX gamble will take at least 20 years to play out, an uncommon investment. But SpaceX never claimed to be a common investment, even by VC standards. Venture capitalists are used to taking on risk, but a company like SpaceX is a huge gamble – a moonshot. If it pays off, the VC nets huge return on their investment, much more than they could hope to make on their normal (albeit, risky) portfolio.


We travel from sci-fi to fantasy – let’s talk about unicorns.

The term unicorn is used to represent a company that is a privately held startup worth over $1 billion. The term was coined by VC Aileen Lee in 2013. The idea was that these companies were so rare that the proper metaphor was a mythical creature (Wikipedia).

In 2019, 142 companies became newly minted unicorns (Crunchbase). Suddenly, they don’t seem so rare. However, VC investors bet on many, many more companies that become total flops. The idea is that they take losses on most of their investments and then make it all back (and then some) on the one unicorn company that returns 100x their investment.

There are a bunch of great examples of unicorns: Airbnb, Epic Games (makers of Fortnite), DoorDash, Udemy, Reddit, 23andMe, and Squarespace.

While most investments are either a bust or a unicorn (I’m sure there are some in the middle), a moonshot like SpaceX is something even bigger.

Sure, you must wait 20-30 years for your investment to make a return, but that return could be staggering. Consider Softbank’s CEO, Masayoshi Son, and his $20 million bet on Alibaba Group in 1999, which turned into $60 billion at the time of Alibaba’s IPO in 2014. That’s 3,000x return in 15 years.

That’s the kind of returns investors are looking for.


But there is a darker side to these investments. Consider WeWork.

WeWork was a company that specialized in co-working spaces in major cities like New York and London. It portrayed itself as a tech startup but was more like a real estate holding company and landlord.

WeWork was founded in 2010 by Adam Neumann, Rebeckah Neumann, and Miguel McKelvey. They raised $14.2 billion from investors, and in just nine years were valued at $47 billion and poised for an IPO of epic proportions.

But then WeWork imploded over the course of a few months. As they prepared for their IPO, their finances came under heavy scrutiny. It turned out that they were burning $230 million monthly and there was no profit in sight. There were also some serious claims about Adam Neumann (great article on the topic, here), including claims that he took hundreds of millions of dollars out of the company. Really, the accusations are astounding. Neumann ended up receiving $1.7 billion for separating from the company in a shocking display of Wall Street shenanigans.

Now WeWork is valued at just $2.9 billion. A spectacular failure. The venture capitalists that invested billions of dollars in WeWork lost their entire investment. This was a unicorn gone wrong.

Conclusion: Why moonshots?

The simple answer is you can make a ridiculous amount of money. The investments in these companies make sense because there is an outrageous upside. Sure, it could be worth nothing, but it could be worth billions or trillions of dollars.

The other thing to understand is that most of these VC companies are invested in many companies, some are riskier than others, and that is how their business works. They take a lot of risk on each investment, but the hope is that one or two investments will return the fund and then some. Their investors understand this, and, frankly, there is a lot of money to be made.

Section 230 of the Communications Decency Act

Section 230 has come under considerable scrutiny over the last few weeks due to run-ins between Trump and Twitter. Last week the Justice Department proposed that Section 230 be scaled back.

This is all tied to Twitter flagging Trump’s tweets warning people of inciting language. Trump, obviously, was not happy about this and issued an executive order about it. That issue is currently being litigated, but conversations about Section 230 continue.

Before we discuss the ramifications of ‘scaling back’ Section 230, it probably benefits most of us to understand what Section 230 is and what it is designed to protect.

47 USC § 230

Section 230 was passed into law in 1996. The key portion of it reads:

No provider or user of an interactive computer service shall be treated as the publisher or speaker of any information provided by another information content provider. 47 USC § 230(c)(1).

It essentially means that websites are not liable for things posted on their sites. It acts as a complete shield against lawsuits based on libel or slander. You can still sue the person who posted the offensive post or tweet but not the service or platform.

Section 230 came about during the Dot-Com Bubble, which existed between 1995 and 2000 and saw the rise (and later fall) of many internet-based companies. It was designed to protect the small start-ups who could not afford to defend themselves in court.

Who does Section 230 protect?

Section 230 does exactly what it was meant to do. It protects internet start-ups from being sued into the ground because of things its users post online.

But it also protects billion-dollar businesses like Twitter and Facebook.

Theoretically, these huge social media companies are not liable for anything posted on their sites, which means that they don’t have to police their users. These companies choose to monitor their users, which can lead to taking down content or banning people, but they are not really required to do anything.

The argument is that Section 230 also protects our freedom of speech because without it these companies would need to police their content with a heavy hand, which would mean that some acceptable content will be lumped into the unacceptable content and be banned unnecessarily.

At least that’s the argument.

Every time Section 230 comes under scrutiny, the social media companies respond that if we remove their protections, they will have to ban everyone or, at least, blindly remove content that is completely acceptable. Then First Amendment activists jump up and down, claiming that by removing Section 230, we all lose valuable speech.

They’re right, of course, if the threat from the social media companies is real.

But what about Twitter flagging Trump’s tweets as inciting language? Isn’t that commentary on what someone is posting? Are they allowed to do that? To be honest, I am not an expert in First Amendment law, but it appears that they can under the current rules.

What happens if Section 230 goes away?

The truth is that no one is talking about getting rid of Section 230. Both parties have discussed how the section needs to be reworked now that it is 25 years old. There is no indication that the protections will be completely removed.

But what if they were? Would Twitter and Facebook do what they said they would do? They certainly could roll out some very heavy-handed policing tactics and start flagging all sorts of reasonable posts. But my guess is that they won’t.

These companies already pour through every post made. That’s the point of what they do. They collect data so that they can sell ads. None of this should be a surprise. Are we really suggesting that they have no way of going through every post and policing for offensive language (or whatever language will get them sued)?

Facebook is currently worth approximately $681.56 billion (per macrotrends). They can certainly spend the money to create a system to manage their responsibilities with or without Section 230. In fact, I have no doubt that they have already considered what happens if Section 230 is abandoned and have a back-up plan for the situation.


I don’t think we’ll see the destruction of Section 230. The section has made the US a haven for tech companies who wish to avoid this specific form of litigation. To torch it would be turning our backs on some of the biggest companies we have.

But I think it behooves us to consider that the companies we are protecting are worth billions of dollars and are the most capable of dealing with shifting rules related to policing their users.

I think threats from these social media giants about restricting free speech is just an effort to mobilize us to defend them, and I think those threats are toothless. These companies make money because we post on their sites. Restricting our posts will impact their bottom line. Even if that is their game plan, the next social media company to tackle the modified Section 230 by accurately policing their users will be the ones to take over the market. These companies are incentivized to make this system (with or without 230) work.